A Household Level Investigation into Personal Chauffeured Automobility

A Household Level Investigation into Personal Chauffeured Automobility | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Household Level Investigation into Personal Chauffeured Automobility Purnima Kumari Chowdhury, Ronita Bardhan, Tarak Nath Mazumder This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9002386/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Personal chauffeured automobility is a widespread yet under-researched practice, particularly in cities of the Global South. This study presents the first systematic investigation of personal chauffeured car use and the motivations that sustain it as an everyday mobility practice. Based on semi-structured, in-depth interviews with personal chauffeured car users, the study integrates qualitative thick description with quantitative analysis to explain the need to choose personal chauffeur-driven cars. In total, twenty-eight distinct reasons emerged from open ended responses. Subsequent cluster analysis of these reasons reveals four broad motivational themes shaping personal chauffeured automobility: (i) physical or functional constraints, (ii) utility maximization, (iii) habitual comfort-related motivations, and (iv) habitually assisted mobility practices. These motivations cut across diverse household types, indicating that structurally different households may share similar underlying motivation for relying on chauffeur-driven travel. The analysis reveals that personal chauffeured automobility is strongly shaped by socioeconomic characteristics, including the presence of elderly household members aged above fifty-five years, households with school-going children, very high annual incomes, salaried and self-employed occupations, multiple car ownership, long car ownership duration exceeding 20 years, ownership of high-value cars, and high work-related car trips. Household archetypes developed from these motivational clusters further reveal how life-stage challenges, mobility needs, lived circumstances, habitual practices, lifestyle preferences, and socially embedded norms—shaped by prior generational experiences and social circles—are intertwined with everyday mobility decisions. Urban Studies Personal chauffeur Mobility behaviour Cluster analysis Household archetypes Qualitative Global South Figures Figure 1 Figure 2 Figure 3 1. Introduction There is ample research in global south on mobility choice of using a car due to the influence of multiple socioeconomic characteristics (Guerra, 2015 ; Jiang et al., 2017 ; Ma et al., 2018 ; Meena et al., 2021 ; Prabnasak & Taylor, 2009 ; Raditya et al., 2025 ; Srinivasan et al., 2007 ; Verma, 2015 ; Wu et al., 2020 ; Wu & Tang, 2019 ); presence of other competetive modes (Dash et al., 2013 ; Vasudevan et al., 2021 ), built environment factors (Ashik et al., 2024 ; Feng, 2017 ; Yang et al., 2021 ); psychological factors (Prawira F. Belgiawan & Schmocker, 2016 ; Prawira Fajarindra Belgiawan et al., 2017 ; Kim et al., 2020 ; Martins et al., 2020 ; Meena et al., 2021 ; Moody & Zhao, 2019 ; Wu & Tang, 2019 ; Zhao & Zhao, 2020 ); attitudinal factors (Kim et al., 2020 ; Verma et al., 2016 ; Vos & Alemi, 2020; Zhou & Wang, 2019 ). Despite wide research on private car use, there is no study capturing the behaviour of two categories of car owners in the Global South: (a) self-drivers and (b) owners who rely on a personal chauffeur. None of the disaggregate choice models on car use and ownership has taken into consideration that the two car owners may or may not have different behaviour. 1.1 Research Gap and motivation of the study Personal chauffeured automobility refers to a mobility choice in which car-owning households employ a personal chauffeur to drive the car for household members. It is a very common mobility behaviour in the Global South. This mobility choice is largely absent in the Global North. In India, car owners across diverse income groups employ personal chauffeurs (PC) at monthly salaries ranging from INR 8,000 to INR 15,000 (87USD − 163USD) depending on daily trip frequency, vehicle kilometres travelled, and additional services provided to the household. Far from being a rigid arrangement, chauffeur use is often fluid—households may hire drivers temporarily or share them within familial or neighbourhood networks. In many cases, the decision to employ a chauffeur is not incidental but integrated into the initial vehicle purchase, with households factoring in the recurring cost as part of car ownership itself. Despite its visibility on the streets, this form of mobility remains statistically invisible. There is no systematic dataset capturing how many car-owning households rely on personal chauffeurs in India or across the Global South. Household travel surveys are scarce, and even where they exist, they overlook this practice. Administrative data—such as vehicle registration or insurance records—also fail to capture chauffeur employment. Parking systems do not distinguish between self-driven and chauffeur-driven cars, leaving the daily volume of such cars unrecorded. Nor do government systems track personal chauffeurs operating private cars, resulting in a lack of even basic information on their age, driving experience, or earnings. While many cab and taxi drivers are linked to unions or platform operators that maintain structured records, personal chauffeurs operate largely outside such systems. Their employment is typically informal, arranged through personal networks and referrals, making them effectively invisible in official data. Even in systems where such data could be captured—like digital traffic fines or toll payments—no information is recorded on whether a car is self-driven or chauffeur-driven. As a result, the scale and characteristics of household-level chauffeured car use remain largely undocumented in India. There is a clear need to study personal chauffeured automobility, as it remains an overlooked yet significant form of car use that limits our understanding of everyday mobility behaviour. Existing research and disaggregate choice models often assume car users as a homogeneous group, overlooking such distinct practices and thereby simplifying reality. Recognising this form of automobility is crucial to capture behavioural diversity more accurately. It also has important implications for emission studies, as different user groups generate varying levels of vehicle kilometres travelled. Without accounting for these differences, policy interventions risk being misdirected. Studying this phenomenon is therefore necessary to develop more realistic models and design targeted, evidence-based transport and environmental policies. This study is the first to systematically address this gap, drawing on semi-structured interviews with 126 users to uncover both the motivations behind chauffeured automobility and the profiles of those who adopt it. It is guided by the following research questions: Why do car owners choose to rely on a personal chauffeur to access their own vehicles? Which socioeconomic and household characteristics distinguish those who adopt chauffeured automobility? What behavioural patterns and lived circumstances characterize households that choose chauffeured automobility? This study combines qualitative depth with quantitative rigour to uncover why households rely on personal chauffeurs. Semi-structured interviews with car-owning households in Patna first captured a wide range of motivations through open-ended responses, alongside detailed socioeconomic data. Cluster analysis was then used to identify dominant motivation patterns and distinct household types separately. These clusters were further interpreted using over-representation analysis to highlight defining characteristics. Finally, both strands were integrated and enriched with qualitative insights to construct household archetypes, offering a clear, behaviourally grounded explanation of how lived realities shape the choice of chauffeured automobility. The study is structured as follows: Section 2 reviews the literature on how to approach studies where there are no prior work and the importance of developing archetypes in understanding underexplored behaviours. Section 3 presents the study area and data used in the study. Section 4 presents the analysis approach in the study. Section 5 presents the analysis and the results obtained. Finally, Section 6 offers discussion, conclusions, and lastly future scope of work is presented in Section 7. 2. Theoretical foundation 2.1 Pragmatic approach to producing in depth behavioural insights In the absence of both a strong theoretical foundation and prior empirical evidence on personal chauffeured automobility, —it is necessary to first undertake exploratory research capable of generating insights sufficient for hypothesis development. In such under-researched contexts, a pragmatic research design is essential to uncover patterns and build an initial evidence base (Evans et al., 2011 ). A pragmatic approach combines qualitative and quantitative methods in sequence, enabling a richer and more comprehensive understanding of complex, underexplored phenomena (Bryman, 2012c ; Creswell, 2013 ). This approach is referred to as mixed-method (Creswell & Clark, 2018 ). Despite its advantages, its application in transport-related studies is still scarce. Few examples are: ((Jason) Cao et al., 2019 ; Clark et al., 2016 ; Heinonen et al., 2021 ; Hopkins et al., 2021 ; Jiron & Carrasco, 2020 ; Tiznado-Aitken et al., 2020 ). A mixed-methods study in Greece illustrates this well: survey data (≈ 375 respondents per city) quantified mode-specific differences in travel satisfaction, while 14 qualitative interviews explained these patterns by revealing how public transport limitations and urban conditions shape lived travel experiences (Mouratidis et al., 2023 ). To fill the gaps in studies on behavioural drivers of car ownership, mixed-methods approaches have proven particularly effective in generating deeper insights. For instance, a study in Iceland used qualitative data to reveal psychosocial factors—such as social norms, status associated with cars, and stigma around public transport—embedded in Reykjavík’s urban context, while the logistic regression tested how factors like residential location, socio-demographics, attitudes, and preferences shape ownership decisions (Heinonen et al., 2021 ). Together, the methods revealed residential self-selection, a phenomenon difficult to isolate through quantitative analysis alone. Another study (König & Dreßler, 2021 ) demonstrates the strength of mixed-methods in capturing complex behavioural change. By combining 301 quantitative data with 15 qualitative insights, it was possible to track measurable shifts in mobility patterns in Northern Germany while also uncovering how individuals experienced and adapted to these changes during COVID-19. In another study, (Næss, 2020 ) demonstrates that incorporating qualitative insights helps avoid common quantitative pitfalls such as model misspecification by informing the selection and causal ordering of control variables. A study in Concepción, Chile shows how combining quantitative and qualitative data can uncover the complex, context-dependent nature of everyday mobility strategies (Jiron & Carrasco, 2020 ). By capturing both measurable patterns and the underlying social and spatial dynamics, this approach reveals how travel decisions are shaped by overlapping factors—offering a far more nuanced understanding than single-method studies. (Tiznado-Aitken et al., 2020 ) also adopted mixed-methods research in Santiago de Chile that demonstrates how integrating qualitative travel experiences with quantitative accessibility indicators reveals dimensions of public transport accessibility that are overlooked in studies with purely quantitative analyses. Previous study by (Clark et al., 2016 ) used a mixed-methods approach to examine car aspirations among adolescents in New Zealand, based on data from 184 households to provide a causal explanation of changes in household car ownership over time. In another case, where socio-cultural background and built environment factors were examined to explain automobility in households with older adults, both qualitative and quantitative data were used. It proved helpful in understanding how the social and cultural contexts impact the travel patterns of older Chinese adults (Feng, 2017 ). Overall, the literature shows that mixed-methods approaches are particularly powerful for studying everyday mobility strategies shaped by lived experiences and real-life circumstances. They allow researchers to move beyond what people do in travel behaviour to understand why they do it—capturing shifts in travel preferences driven by past experiences, and revealing how social norms, identity, and status influence mobility choices. As a result, mixed methods provide a richer, more grounded understanding of mobility behaviour—particularly in emerging research areas where purely model-driven studies may overlook important social and contextual realities. 2.2 Importance of creating archetypes in understanding underexplored behaviours While socioeconomic context remains a relevant factor, an overreliance on such variables may obscure deeper motivations, values, and lived experiences that shape everyday decision-making. As several studies have demonstrated, transport behaviours are intricately embedded in daily life activities, lifestyle preferences, and routine participation in social and economic spheres (Acker et al., 2014 ; Acker & Witlox, 2010; Kitamura, 2009 ; Pas & Koppelman, 1986 ; Van Acker, 2015 , 2017 ; Van Acker et al., 2016 ). Daily life experiences, therefore, provide a more powerful lens for understanding behaviour and habits, as they capture the routines, preferences, and choices enacted in lived contexts. Such an approach offers dynamic, grounded insights into how individuals interact with environments, revealing dimensions of mobility behaviour that remain invisible in static socioeconomic analyses. To better capture behavioural heterogeneity, archetypes have emerged as a valuable analytical approach in qualitative transport research. Archetypes facilitate the identification of recurring behavioural profiles that transcend socioeconomic categories, enabling systematic analysis of how and why specific mobility choices are made. Archetype classification supports the examination of culturally embedded practices by synthesising repeated behavioural patterns across heterogeneous user groups (J.Arnould & L.Price, 2000). Rooted in the concept of ideal type (Weber, 1904 ), archetypes enable researchers to interpret complex social phenomena through systematic comparison of cases or participants within qualitative datasets. This approach facilitates the identification of behavioural clusters, supporting deeper analysis of variation and comparability across household decision-making contexts. Archetype formation has also been shown to capture multiple layers of implicit and often unconscious behaviour at both individual and group levels (Sietz et al., 2019 ). Prior research has widely applied archetype-based approaches to capture behavioural heterogeneity across different domains. For instance (Ben & Steemers, 2018 ; Ortiz & Bluyssen, 2019 ) employed household archetypes to examine occupant comfort and adaptive behaviours in residential buildings in the Netherlands, while (Heinrich et al., 2022 ) developed seven behavioural archetypes to explain patterns of domestic energy use in the UK, demonstrating how lifestyle and value orientations shape energy-related choices. Beyond household behaviour, archetype analysis has been used to identify recurring business model configurations in the connected car sector (Sterk et al., 2024 ). In the transport planning domain, (Navarro-Ligero & Valenzuela-Montes, 2022 ) developed scenario-based archetypes to analyse the evolving planning logic of Granada’s Light Rail Transit (LRT) system. Drawing on survey data and planning documents, their study illustrates how archetypes can function as strategic analytical frameworks for interpreting institutional preferences and supporting integrated transport and urban development planning. Other research emphasises the value of everyday mobility narratives and anecdotal evidence in capturing the lived realities of travel behaviour that are often obscured by highly aggregated analytical approaches. By foregrounding users’ perspectives on travel mode choice, such methods provide rich behavioural insights that may be overlooked in expert-driven or purely quantitative assessments of sustainable mobility (von Schönfeld et al., 2020 ). A comparative study of thirty cities across Europe, Asia, Africa, and the Americas employed archetypes to classify transport solutions addressing common urban mobility challenges, thereby providing a structured framework for interpreting global transport systems (Thomson, 1977 ). Building on this foundation, (McIntosh et al., 2014 ) extended Thomson’s typology by classifying 26 additional global cities using the same five archetypes. Using household travel survey data from Minneapolis, (Charleux, 2017 ) developed mobility archetypes to uncover latent patterns in travel mode choice, trip purpose, and daily mobility rhythms that remained obscured in conventional aggregate analyses. Through the application of clustering techniques and systematic archetype characterization, the study demonstrated how archetype-based analysis can capture heterogeneity in lived travel experiences across demographic groups, offering a more human-centered interpretation of urban mobility. These contributions highlight the analytical potential of archetype formation for revealing behavioural diversity beyond standard quantitative representations. Thus, we can see that archetype-based analysis offers several advantages for studying underexplored mobility behaviours. First, it enables the systematic identification of behavioural heterogeneity that extends beyond conventional socioeconomic classifications, allowing researchers to examine how and why mobility choices are made across diverse household contexts. Second, by synthesising recurring thick descriptions or original narratives of the respondents about any behaviour, archetypes help reveal culturally embedded, habitual, and often implicit decision-making processes that are typically obscured in highly aggregated or purely quantitative analyses. 3. Data 3.1 Case Study area: Patna, India The study was conducted in the urban area of Patna, the capital of Bihar, which offers a compelling context for examining personal chauffeured car use. Over the past decade, the city has witnessed rapid growth in private motorisation, with car ownership rising from about 50 cars per 1,000 people in 2011 and projected to exceed 125 per 1,000 by 2025 (Ghate & Sundar, 2014 ).This growth has unfolded alongside an urban transport system that remains limited in scope and efficiency. With inadequate bus services and the absence of mass rapid transit options such as metro rail or BRT systems, households increasingly rely on private cars to meet their mobility needs (Tiwari & Sharma, 2016 ). This dependence is further intensified by structural constraints within the city. Road infrastructure covers only 5.9% of the urban land area, and most major corridors have right-of-way widths of less than 30 metres. Volume-to-capacity ratios on arterial roads frequently exceed one, reflecting persistent congestion and travel delays (CMP, 2009 ). Encroachments and widespread on-street parking further reduce the effective capacity of already constrained roads. In such a dense and infrastructure-limited urban environment, where viable alternatives to car use are scarce, everyday mobility decisions take on deeper social and practical significance. Patna therefore provides a particularly insightful setting to explore why households not only own cars but also continue to rely on personal chauffeurs. 3.2 Data collection approach To achieve the research questions (See section 1.1 ), the first author conducted in-depth semi-structured interviews (Bryman, 2012b ; Creswell, 2013 ; Creswell & Clark, 2018 ) with a purposively selected sample (Ahmad & Wilkins, 2025 ; Marshall, 1996 ) of car owners who are dependent on a personal chauffeur. The interview format typically comprised collection of socioeconomic data and open-ended questions designed to encourage interviewees to speak broadly about and around the need of a personal chauffeur, thereby allowing new ideas and insights to emerge that the researcher might not have otherwise anticipated. A pilot study involving fifty car users was conducted prior to the main interviews to gain preliminary insights into the motivations for employing a personal chauffeur (PC). Insights from the pilot study revealed a key practical challenge: it was difficult to identify in advance which households actually employed PC. Consequently, participant recruitment was primarily undertaken in public and semi-public urban spaces where car users are more readily accessible, such as commercial areas, parking facilities, and other activity hubs. Participants were approached in on-street and off-street parking areas, near commercial establishments, educational institutions, healthcare facilities, fuel stations, and, in some cases, adjacent residential areas. This strategy helped minimise location-specific bias and facilitated access to a diverse cross-section of PC car users. All participants were approached directly by the first author of this study, the purpose of the study was clearly explained, and informed consent was obtained prior to audio recording. Interviews were conducted in Hindi which typically continued around 40 minutes, with some extending up to one hour. The semi-structured interview consisted of two main sections. The first section collected quantitative data on the socioeconomic characteristics of PC car households. The second section comprised qualitative questions that explored the reasons prompting dependence on a PC, underlying motivations, and the perceived advantages of employing a driver. 3.2.1 Data collection: Quantitative data The semi-structured interview captures detailed household-level socioeconomic characteristics, including age distribution, household composition, the presence of children and elderly members, the number of earning members, occupational profiles, and income categories. In addition, vehicle-related information was collected on car ownership levels, vehicle typology, and the price range of vehicles that households are currently able to afford. The study also records driving license ownership among household members, distinguishing between active and inactive license holders. Finally, travel behaviour data document trip-making patterns across modes, identifying trips undertaken using personal cars as well as those specifically assisted by a personal chauffeur. Description of the data is presented in Table 2 . 3.2.2 Data collection: Open-ended questions Emphasis was given to directing the participants to share their lived experiences or cite daily life incidents or a major incident that is associated with the need to rely on a personal chauffeur. The interview included questions like the following: “What is the use of the car in the household?” and “What was the main reason for depending on a full-time chauffeur? Did any specific change in your life trigger the need of a driver?” These were instrumental in capturing if any life course event influenced the role of car and driver within the household. Moreover, inquiries about “mobility needs” and “travel routines” such as “which of the trips within the households are carried out by a personal car with a driver?” were also done. It offers a broad view of how the presence of a driver fits into their daily life mobility requirements. Intergenerational continuities were also probed with questions like: “Did your parents or previous generations employ drivers?”. 3.3 Determination of sample size in carrying out interviews Theoretical saturation is the yardstick for determining sample size in the case of interviews (Charmaz, 2006 ; Glaser & Strauss, 1967 ). Saturation refers to the point at which additional interviews no longer yield new information about the phenomenon under investigation and hence, carrying out more interviews with households needs to stop. Conducting interviews beyond this point risks unnecessarily expanding the dataset, thereby complicating theme or pattern identification (Bryant & Charmaz, 2007 ; Bryman, 2012a ; Morse, 2007 ). Determining when saturation has been achieved, remains challenging (Guest et al., 2006 ; Malterud et al., 2016 ). Several strategies have been proposed to assess saturation (Hennink & Kaiser, 2022 ). In this study, a code frequency count approach was adopted, indicating that the reasons for relying on a personal chauffeur in Indian households were sufficiently comprehensive and that no new reasons or motivation emerged from subsequent interviews. 4. Methodology 4.1 Analysis approach The study adopts a sequential mixed-methods approach to answer the central research question: why do car users need a personal chauffeur. In the first step, interview transcripts were transcribed daily in parallel with the field visits. Excel spreadsheet was maintained in which all reasons reported by each respondent were systematically listed by the first author. Respondents were not provided with a predefined list of reasons; instead, they articulated their motivations in their own language from their own lived realities. Emerging reasons were registered and reviewed daily at the end of the day. For instance, if a respondent stated that commuting becomes less stressful when travel is managed by a personal chauffeur (along with their broader lived experience explanation), this reason was recorded in the Excel sheet as R1 and coded as 1. If another respondent did not report this reason, it was coded as 0. This helped to determine whether any new reasons had emerged if yes then a new column would be added. In this way the entire spreadsheet was coded in binary based on which Table 1 was prepared. After the ninth interview, no new reasons were identified for a prolonged period; a new reason was recorded only during the seventy-third interview. Data collection was nonetheless continued for an additional 53 interviews to ensure robustness. As no further new reasons emerged during this phase, we stopped the interviews at 126 sample size. As reasons were coded line by line from the transcripts each day, recurrent patterns began to emerge (Bryant & Charmaz, 2007 ). To further explore these patterns, we conducted a cluster analysis (Wagschal, 2016 ) – an unsupervised machine learning technique for classification on the reasons mined from the open ended responses. Cluster analysis was performed in Python. The analysis was performed in Python and helped reveal underlying themes and relationships among them, allowing individual reasons to be grouped into broader motivational themes. The aim was to identify dominant thematic constellations that explain why car users perceive a need for a personal chauffeur and what discourages them from self-driving. Figure 1 shows the methodological framework. In the next step, we conducted a second cluster analysis at the household level. While the reasons were coded every day. All the interviewed households were given a nomenclature too. This analysis resulted in distinct household clusters that shared common motivational profiles for employing a personal chauffeur. In the subsequent step, we assessed the statistical robustness of the identified clusters. We conducted chi-square tests to examine whether significant differences existed between clusters across categorical variables, thereby confirming that the observed groupings were not due to random variation. We then applied hypergeometric p-value estimation to identify socioeconomic characteristics that were overrepresented within each cluster. The study uses a one-sided test in which a threshold of significance was determined at ( p ≤ 0.05) for both over-representation and under-representation. Finally, we developed household archetypes by integrating insights from the motivation (reason) clusters and household characteristics with thick qualitative descriptions. It was a triangulation process. We moved back and forth between the interview transcripts, the motivation (reason) clusters, and the prominent household characteristics to observe a coherent household profile. This profile helped explain why certain reason clusters appeared as they did, providing a grounded interpretation of why households choose to rely on a personal chauffeur. 5. Analysis and Results 5.1 Reasons for choosing personal chauffeured automobility A total of twenty-eight distinct reasons for employing a personal chauffeur emerged from the semi-structured interviews (Table 1 ). Overall, the distribution of responses indicates that employing a personal chauffeur is primarily driven by stress reduction, travel management, and household time coordination, while safety concerns, gendered mobility roles, and structural constraints play secondary but reinforcing roles in shaping this reliance. The high prevalence of reasons related to operational ease and professional driving suggests that households view the chauffeur as an extension of their mobility system rather than merely a driver. At the same time, mid- and lower-frequency reasons reveal how this practice is embedded within broader household dynamics, including division of responsibilities, activity patterns, and occasional situational needs. Together, these patterns highlight that reliance on a personal chauffeur is shaped by a combination of everyday mobility management and context-specific household circumstances. While several reasons may appear similar at first glance, they were expressed in different contextual settings and reflected distinct lived experiences and decision-making logics. Therefore, documenting the full range of articulated reasons was considered essential to preserve contextual nuance. Table 1 List of reasons behind the need for a personal chauffeur Code Reason % of Households R1 Commuting becomes less stressful when travel is managed by a personal chauffeur 88.10% R2 Vehicle maintenance and upkeep are better managed by professionally trained chauffeurs with superior technical knowledge 79.40% R3 Chauffeurs provide reliable, continuous assistance with household errands and light chores 69.80% R4 Chauffeurs possess professional driving expertise, including better knowledge of traffic rules, road hierarchies, and informal driving practices 65.90% R5 Chained trips are executed more efficiently when a chauffeur manages the vehicle between activities 60.30% R6 Household time constraints make delegating driving responsibilities to a chauffeur more practical 46.80% R7 Parking search and cruising in areas with limited parking supply are more effectively handled by chauffeurs 45.20% R8 Female household members lack driving skills, making reliance on a chauffeur more practical 42.10% R9 Driving after dark is perceived as uncomfortable and is therefore delegated to a personal chauffeur 42.10% R10 Frequent long-distance or inter-district travel is more manageable when driving is handled by a chauffeur 31.00% R11 Male household members show limited preference for driving and delegate this task to a chauffeur 30.20% R12 Lack of confidence in self-driving leads households to rely on chauffeurs 30.20% R13 Concerns about aggressive and rash driving behaviour are mitigated when a professional chauffeur is driving 30.20% R14 Male worker in the household has to access the chain of work locations 27.00% R15 Multiple simultaneous activities can be coordinated through chauffeur-managed vehicle use without owning multiple cars 23.80% R16 Waiting time outside activity locations is reduced when a chauffeur manages vehicle movements 23.80% R17 Escorting children to school and extracurricular activities is more dependable with a chauffeur 22.20% R18 Male household members lack driving skills, increasing dependence on chauffeurs 22.20% R19 Irregular work schedules and unexpected travel demands are more easily accommodated by a chauffeur 22.20% R20 Female household members show low preference for driving and delegate driving to a chauffeur 19.80% R21 Frequent phone calls, and texting basically need to stay connected online, making it difficult for members to focus on self-driving 19.00% R22 Chauffeurs provide practical support for learning driving skills and vehicle maintenance 15.90% R23 Medical-related trips are handled more smoothly when a chauffeur assists ill or elderly household members 13.50% R24 Driver was kept due to residential location and built environment factors that hindered easy car use 10.30% R25 Travel during odd hours or to less crowded locations feels safer when accompanied by a chauffeur 10.30% R26 Limited mobility among household members necessitates dependable driving assistance from a chauffeur 10.30% R27 Household members reside in geographically disadvantaged areas where there is a lack of suitable modes of travel 6.30% R28 Temporary inability to self-drive following an accident makes chauffeur assistance essential 2.40% 5.2 Cluster Analysis of Reasons for Choosing Personal Chauffeured Automobility To understand the broader motivations underlying personal chauffeured automobility, hierarchical clustering techniques were employed to move beyond analysing individual reasons in isolation and to reveal latent motivation. Each household representative articulated multiple reasons relevant to their decision, indicating that chauffeur reliance is shaped by a combination of factors rather than a single dominant motive. A preliminary review of the responses suggested that, despite variation at the individual level, there were clear thematic commonalities across households. The hierarchical clustering results reveal two broad motivational stems, which are further subdivided into distinct clusters labelled A, B, C, and D and differentiated by colour as shown in Fig. 2 . The reasons were grouped into mutually exclusive clusters, with each reason assigned to only one cluster to maintain thematic clarity. However, individual households often reported reasons spanning multiple clusters, indicating that multiple motivations can simultaneously shape the decision to rely on a personal chauffeur. The dendrograms display these groupings, with each cluster labelled to reflect its dominant underlying theme and overall motivational logic. Each cluster presents a distinct motivation, based on which they are given labels described below. a. Cluster 1 reasons: Physical or Functional Constraint-related reasons. This motivational category reflects situations in which reliance on a personal chauffeur emerges from constraints that limit the feasibility of self-driving. Such constraints may be physical, health-related, age-related, or situational in nature. In these contexts, chauffeur assistance enables households to sustain everyday mobility, maintain autonomy in daily activities, and navigate travel demands that would otherwise be difficult to manage independently. b. Cluster 2 reasons: Utility Maximization related reasons It reflects a motivation to streamline everyday travel by delegating driving tasks in order to save time, reduce cognitive and physical effort, and maintain flexibility in the face of complex and unpredictable daily schedules. Households in this category value the reliability and readiness offered by a personal chauffeur, which allows them to coordinate multiple activities, respond quickly to changing travel demands, and sustain high levels of productivity without being constrained by the demands of self-driving. c. Cluster 3 reasons: Habitually comfort-related reasons The combination of the reasons in Cluster C reflects the role of habitual comfort and emotional ease in motivating households to employ a driver. This cluster signifies how households prioritise a relaxed commuting experience while factoring in the influence of long-standing habits, cultural norms, and prior exposure to driver assistance. d. Cluster 4 reasons: Habitually Assisted related reasons The clustered reasons emphasise the role of deeply ingrained habits, practical reliance, and a seamless lifestyle enabled by personal chauffeur use. This cluster of reasons reveals how individual incidents or isolated conveniences do not solely drive the decision to employ a driver but are instead shaped by routine reliance on external assistance for transportation, managing logistical challenges. 5.3 Socioeconomic characteristics of the households that choose personal chauffeur automobility Table 2 Descriptive statistics of interviewed households and their characteristics 1 Household Characteristics Count (Total N = 126) Percent Household Size Less than three 26 20% Three-four 54 42% More than five 46 37% 2 Households with school-going children 64 52% 3 Male elderly above 55 years old 72 57% 4 Female elderly above 55 years old 47 37% 5 Households with the elderly above 55 years 80 64% 6 Households with economically active above 55 years 48 38% 7 Household With Immobile Members 48 40% 8 Household annual income 1 Medium 11 8% High 28 22% Very high 87 69% 9 Number of Earning Members Single 45 36% Dual 59 47% Multiple 14 11% Pensioner 8 6% 10 Female earners 43 34% 11 Occupation Type categories: Salaried 42 33% Self-employed 69 55% Salaried and self-employed both 6 5% Pensioner only 9 7% 12 Driving license male 115 92% 13 Driving license female 37 36% 14 Number of cars categories: One car 76 60% Two car 43 34% More than two cars 7 6% 15 Duration of car ownership: Less than 5 years 13 10% 6 to 10 years 33 26% 11 to 15 years 19 15% 16 to 20years 21 17% More than 20 years 40 32% 16 Price of car categories: Less than 5 lakhs 12 9% 5 to 10 lakhs 66 52% 10 to 15 lakhs 37 29% 15 to 25 lakhs 6 5% Above 25 lakhs 5 4% 17 Car variant categories: Hatchback only 57 45% Sedan only 4 3% Sports Utility Vehicle (SUV) only 10 8% Multi-utility vehicle (MUV) only 2 1% Luxury only (above 9 million) 1 1% Multiple variants (two SUV + SUV+ Luxury, SUV + MUV, SUV+ hatchback) 52 41% 18 Distance to school: Less than 5 km 17 13% 6 to 10 km 34 27% More than 10 km 15 12% Household with no school-going children 61 48% 19 Household with school trips by car 64 51% 20 Household with organized leisure activities by car 34 27% 21 Household with work trips by car 99 78% [1] Medium Income = INR 2,73,000 - 8,46,000; High Income= INR 8,46,000 - 15,00,000; Very High Income= INR 15,00,000 above. The classification is based on the world bank income group classification for low-income countries. 5.4 Cluster analysis of the households that choose personal chauffeur automobility Cluster analysis of households is presented Fig. 2 . The hierarchical dendrogram reveals two broad stems, labelled Cluster X and Cluster Y, based on the primary cut indicated by the dotted line. The dotted line is manually drawn to indicate the cut-off points in the dendrogram, highlighting the two main groups formed by the clustering. For analytical clarity, Cluster X is further subdivided into two sub-groups, labelled Clusters AB and C. The AB branch is then disaggregated into Cluster A, which is further refined into sub-clusters A1 and A2. At a deeper level of the hierarchy, Cluster A2 is additionally subdivided (A2′ and A2″), capturing finer distinctions in household profiles. Cluster Y is seen as a single cluster and labelled Cluster D. The findings indicate that reliance on personal chauffeured automobility spans a wide range of household types. Since we had given nomenclature to each sample when the interviews were done, we were able to capture what typologies of households are present in each cluster. Table 3 Description of household clusters that choose driver assistance No. Household Characteristics Cluster A Cluster B Cluster C Cluster D A1 A2′ A2″ (N = 19) (N = 21) (N = 20) (N = 21) (N = 32) (N = 13) 1 Household Size Less than three 47% 5% 5% 10% 34% 15% Three-four 21% 57% 50% 48% 28% 69% Five or more 32% 38% 45% 43% 38% 15% 2 Households with school-going children 42% 71% 50% 67% 38% 42% 3 Male elderly above 55 years old 74% 43% 70% 38% 21% 46% 4 Female elderly above 55 years old 63% 33% 40% 29% 41% 8% 5 Households with the elderly above 55 years 37% 52% 65% 52% 72% 42% 6 Households with economically active individuals above 55 years 42% 43% 40% 71% 34% 25% 7 Household With Immobile Members 63% 24% 35% 48% 44% 23% 8 Household annual income 2 Medium 11% 0% 5% 10% 9% 23% High 21% 5% 5% 24% 38% 38% Very high 68% 95% 90% 67% 53% 38% 9 Number of Earning Members Single 32% 48% 45% 14% 31% 8% Dual 53% 38% 40% 62% 50% 31% Multiple 0% 14% 15% 24% 6% 54% Pensioner 16% 0% 0% 0% 13% 8% 10 Female earners 47%% 10% 10% 47% 28% 15% 11 Occupation Type Salaried 37% 5% 25% 14% 20% 46% Self-employed 47% 95% 65% 76% 19% 38% Salaried and self-employed both 0% 0% 5% 10% 6% 8% Pensioner 16% 0% 5% 0% 13% 8% 12 Driving license male 89% 10% 95% 95% 53% 38% 13 Driving license female 42% 10 10% 52% 9% 23% 14 Number of cars One 53% 33% 65% 48% 84% 77% Two 42% 57% 35% 43% 16% 15% More than two 5% 10% 0% 14% 0% 8% 15 Duration of car ownership 0 to 5 years 0% 10% 5% 5% 22% 15% 6 to 10 years 16% 10% 10% 24% 44% 54% 11 to 15 years 21% 10% 25% 5% 16% 15% 16to 20years 21% 19% 20% 29% 9% 0% More than 20 years 42% 52% 40% 38% 9% 15% 16 Price of the car Less than five lakhs 5% 0% 0% 14% 16% 38% 5 to 10 lakhs 68% 33% 55% 29% 63% 62% 10 to 15 lakhs 26% 48% 30% 43% 19% 0% 15 to 25 lakhs 0% 10 0% 5% 3% 0% Above 25 lakhs 0% 0% 15% 10% 0% 0% 17 Car variant Hatchback only 53% 19% 35% 24% 66% 75% Sedan only 0% 0% 0% 10% 6% 0% Sports Utility Vehicle (SUV) only 0% 10% 15% 5% 13% 0% Multi-utility vehicle (MUV) only 5% 5% 0% 0% 0% 0% Luxury only (above 9 million) 0% 0% 5% 0% 0% 0% Multiple variants (Two hatchbacks, Hatchback + SUV, Two SUVs, SUV + MUV etc) 42% 67% 45% 62% 16% 23% 18 Distance to school Less than 5 km 26% 24% 5% 19% 0% 23% 6 to 10 km 11% 38% 40% 29% 25% 15% More than 10 km 5% 14% 5% 24% 13% 8% No school-going children 58% 29% 50% 29% 63% 58% 19 School trips by car 42% 71% 50% 71% 38% 46% 20 Organised leisure activity trips by car 0% 48% 25% 52% 19% 15% 21 Work trips by car 47% 86% 95% 90% 63% 85% 22 Presence of a two-wheeler 84% 71% 90% 90% 96% 92% [2] Medium Income = INR 2,73,000 - 8,46,000; High Income= INR 8,46,000 - 15,00,000 ; Very High Income= INR 15,00,000 above. The classification is based on the world bank income group classification for low income countries. Table 4 Description of different household typologies under each cluster Cluster Dominant Household Types Household Composition Breakdown (%) This is same nomenclature which is elaborated in third paragraph of section 4.1 Key Characterization Cluster A1 Predominantly elderly households • Older adults living alone without dependents: 47% • Multigenerational families: 38% • Older adults (late 50s) living with young adults: 16% Strongly age-driven household dominated by elderly residents Cluster A2′ Mid-life stage households • Parents (early/late 40s) with school-going children: 38% • Multigenerational families: 29% • Older adults (late 50s) with young adults: 19% • Late 40s households with school-going members: 14% Households managing overlapping caregiving and multiple mobility patterns Cluster A2″ Multigenerational households • Multigenerational families with school-going and non-school-going children: 44% • Elderly living with young adults: 33% • Parents (late 40s) with adolescents eligible for driving license: 11% • Parents (early 40s) with school-going children: 11% Well- off complex household structures with diverse and overlapping mobility needs Cluster B Mid-life and multigenerational households • Couples (late 40s) with adolescent children: 38% • Multigenerational families: 38% • Young couples without children: 10% Transitional households balancing adolescent and intergenerational mobility Cluster C Predominantly ageing households • Elderly living with young adults (20s): 31% • Elderly couples / widowed individuals living alone: 22% • Multigenerational families: 13% • Young couples without children: 9% Ageing households with varying degrees of intergenerational co-residence Cluster D Elderly–young adult co-residence • Older adults living with at least one young adult: 54% • Young couples with children under age 10 years: 31% • Elderly couples living alone: 15% Mixed-age households combining ageing and early family-stage needs The Table 4 suggests that households with very different demographic compositions are grouped within the same clusters, indicating that the need for a personal chauffeur is not driven by household type alone. Instead, households at different life stages—elderly living alone, multigenerational families, and mid-life households with children and others can share similar underlying motivations for relying on a personal chauffeur. This implies that comparable mobility constraints, supervised travel needs, safety and comfort needs, risk perceptions or their attachment to a familiar norm may generate convergent preferences for personal chauffeur-driven automobility across otherwise dissimilar household structures. This diversity suggests that the choice to employ a personal chauffeur is not confined to a specific age group or household typology. Instead, it varies across households with distinct compositions and life stages, each attaching different meanings and significance to chauffeur-driven automobility. These meanings are closely embedded in households lived realities and shaped by the social environments within which they are living. 5.5 Overrepresentation analysis While Table 3 provides a summary of household characteristics distributed across each cluster. This gives a sense of different characteristics based on age group, income, number of earners, car ownership etc overlapping across clusters, making it difficult to identify clearly defining traits. To address this limitation and identify key differentiating socioeconomic characteristics across clusters, a hypergeometric p-value (Bardhan et al., 2015 ) was conducted. As a first step, chi-square tests were performed to examine whether household socioeconomic characteristics differed significantly across clusters. Only those characteristics that showed statistically significant differences in the chi-square analysis are reported in the paper as key socioeconomic differentiators. After this, hypergeometric p-values (Drǎghici et al., 2003 ) were calculated for these selected socioeconomic characteristics within each household cluster obtained from chi-square test. This helps us estimate statistically the probability of that socioeconomic characteristic occurring by chance in the cluster. In addition, under-representation p-values were also computed to show certain characteristics had a lower probability of occurring in a cluster than would be expected by chance. For clarity, only statistically significant over- or under-represented characteristics are presented, and non-significant results are left blank in the Table 6 . Table 5 Chi-square test results that indicate differences in the household clusters 1 Household Characteristics Chi-square X² df p-value Cramer’s V value Presence of households with school-going children 10.002 5 0.057 0.282 2 Households with female elderly above 55 years 11.34 5 0.045 0.3 3 Households with elderly above 55 years 12.706 5 0.026 0.318 4 Annual household income 24.898* 10 0.006 0.296 5 Household with self-employed 35.968 5 0.000 0.534 6 Households with Salaried employed 26.399 5 0.000 0.458 7 Households with more than 20 years of car ownership 15.084 5 0.01 0.346 8 Price of car owned by the household 47.31* 20 0.001 0.297 9 More than one car 18.613 5 0.002 0.384 10 Households with female driving license holders 16.961 5 0.005 0.367 11 Work trip 36.552 4 0.000 0.539 Table 6 Overrepresentation analysis for household clusters No. Household clusters. Household Characteristics A1 A2′ A2″ B C D 1 Households with school-going children 0.032 0.025 2 Elderly above 55 years old 0.007 0.012 3 Annual income: Medium Annual income: High 0.002 0.017 Annual income: Very High 0.002 0.020 0.003 4 Occupation type: Salaried 0.007 Occupation type: Self-employed 0.025 5 Driving license female 0.003 0.005 0.013 6 Two Car ownership 0.016 0.001 7 Number of car ownership: More than two 8 Ownership duration: More than 20 years 0.026 0.002 0.0001 0.040 9 Car price: Above 25 lakhs 0.028 10 Work trips by car 0.002 0.046 0.002 0.011 Red colour cells indicates over-represented p values. Blue colour indicates under-represented p values. Two p-values were calculated for each characteristic: one for over-representation and one for under-representation. If the p-value in either column was statistically significant, the characteristic was labelled accordingly. Only one of the two columns can show a significant p-value for a given characteristic. Thus, characteristics marked as over-represented correspond to cases where the over-representation p-value was significant. Empty cells indicate that the p-value was not significant in either column hence their level of over-under representation could not be determined. It happens because the samples were less in that cluster. When we cross-check the household composition within Cluster A1, it becomes evident why elderly household members above the age of 55 are significantly over-represented in this cluster (p = 0.007). Nearly 47% of households in Cluster A1 consist of elderly individuals living alone. This dominant household composition in the cluster A1 also explains why work-related trips are significantly under-represented (p = 0.002), indicating a low likelihood of such travel behaviour within this cluster. In Cluster A2′, households with school-going children are significantly over-represented ( p = 0.032 ). This aligns with the cluster’s demographic composition, with 38% comprising parents in their forties with school-age children and an additional 29% being multigenerational households also supporting school-going dependents. These households are also characterized by high annual incomes ( p = 0.002 ), ownership of multiple vehicles ( p = 0.016 ), and regular work-related travel (p = 0.046). The evidence suggests that households in Cluster A2 are affluent, family-oriented units that rely heavily on chauffeur-driven vehicles to coordinate complex daily schedules involving both school drop-offs and professional commitments. Cluster A2″ is distinguished by a significant concentration of high-end vehicle ownership, particularly luxury cars valued above INR 25 lakhs ( p = 0.028 ). Households in this cluster are predominantly multigenerational ( p = 0.002 ), with economically active members spanning multiple generations. These vehicles are frequently used for work-related travel ( p = 0.002 ), reflecting a household context in which affluence and intergenerational economic participation intersect. Together, these characteristics suggest a lifestyle configuration that supports sustained reliance on professional chauffeurs as part of everyday mobility practices. Cluster B includes a diverse mix of households with both salaried ( p = 0.007 ) and self-employed individuals (p = 0.025). This cluster is also marked by over-representation of work-related commutes ( p = 0.011 ) and a notable presence of female driving license holders ( p = 0.013 ). Despite the presence of licensed drivers, households here continue to opt for driver assistance, indicating the perceived value of convenience, time efficiency, or adherence to gendered driving norms. Cluster C is marked by a significant over-representation of two-car ownership ( p = 0.001 ) and a corresponding under-representation of long-term car ownership exceeding 20 years ( p = 0.0001 ). This indicates relatively recent and moderate levels of car accumulation within these households. Cluster C is largely characterised by older household structures: approximately 31% of households consist of elderly members living with young adults in their twenties, while 22% comprise elderly couples or widowed individuals living alone. Together, these characteristics suggest a cluster shaped by ageing household structures, stable salaried incomes, and comparatively recent engagement with private car ownership. Cluster D is demographically distinctive. It is characterized by the absence of elderly members above the age of 55, as indicated by under-representation. Instead, households in this cluster are more likely to include relatively younger elderly (under 55) or middle-aged adults. Additionally, long-term car ownership (over 20 years) is under-represented ( p = 0.04 ), suggesting these households are relatively new entrants into automobility. This profile reflects changing socioeconomic aspirations, potentially aligned with a generational shift in lifestyle expectations and mobility needs. 5.6 Household archetype Each household cluster displays a distinct socioeconomic profile that can be systematically linked to the motivation clusters derived from the stated reasons for employing a driver. By combining motivation clusters (based on interview-derived reasons) and connecting the household socioeconomic characteristics broadly understood through household clustering (AB, C, and D) and supplementing them with interview narratives (thick description), we develop household archetypes This process involves going back and forth with the interview transcripts and correlate with the reasons spoken by specific households and describing their real world setting so as to present a holistic understanding of “ why a car users chooses to rely on a personal chauffeur ”. This triangulation approach integrates quantitative and qualitative evidence, allowing a deeper understanding of how lived realities, effect of what we see around in family, friends and neighbours doing in a similar situation, and looking for solutions to a mobility challenge from their surrounding practical constraints interact to shape the decision to adopt personal chauffeured automobility. 5.6.1 Archetype A: Physical and Functional Constraint motivated Households Households in Clusters A1 and C predominantly exhibit constraint-driven reliance on driver assistance, consistent with the dynamics outlined in Reasons Cluster 1. (Table 3 and Table 4 ). These archetypes are primarily characterised by households with a high concentration of elderly members, including retirees living alone, elderly couples, and older adults in later life stages. Age-related physical and cognitive changes—such as reduced muscular strength, joint stiffness, declining visual acuity, and slower reaction times—progressively limit the ability of these individuals to self-drive safely and confidently. As a result, personal driving becomes increasingly constrained, leading to reduced transport autonomy and greater dependence on external support for everyday mobility. In this context, the transition from driver to passenger is not a voluntary preference but a response to declining driving capability and heightened safety risks. Empirical evidence of higher accident risk per unit of exposure among older drivers further reinforces these constraints. Households at different stages of their life course exhibit varying mobility needs that are often difficult to meet without assistance. In this context, the employment of a personal chauffeur emerges as a strategic and adaptive response. [I 13]: Driver of a 55+-year-old widow working as a school teacher living alone with her adolescent son and owner of a subcompact hatchback: Madam’s elder son, who lives in another city, purchased the car specifically for her. However, due to her knee problems, she is unable to drive at this age. If, for any reason, I am unable to come to work, she has no choice but to take leave from school and cancel any plans to go out. She relies on me entirely for all outdoor activities, including errands like shopping. Without my assistance, she is truly helpless. [I 6]: An elderly widower living alone and owner of a compact hatchback priced at 3000 USD describes: At 57, I bought an Alto and learned to drive. Although I used the car for my daily commute, I wasn’t very confident at first. There was an incident where I almost hit someone on the road, which shook me and made me fearful of driving alone. To help with this, I hired a driver who assisted me while I practised. However, as I grow older, I find it harder to drive confidently, so I prefer to rely on a driver for safety and convenience. Gendered differences in driving histories and mobility practices emerge as a significant factor shaping dependence on chauffeur-driven mobility within this archetype. Many women in this cohort never acquired driving skills due to prevailing gender norms and generational expectations that limited female participation in driving. As a result, dependence on external driving assistance becomes particularly pronounced in later life. In contrast, elderly men in this group often continue to self-drive into older age but gradually reduce or discontinue driving due to declining physical capacity, diminished confidence, or perceived safety risks. This transition is commonly marked by a shift toward trip-specific, part-time, or seasonal driver arrangements before moving to more consistent driver assistance. Notably, households with elderly female members were more likely to retain full-time drivers. [I 30]: A 30-year-old describes the role of a driver in his relative’s house who has a paralyzed individual in the leg and has pertinent walking issues : This is the greatest advantage of having a driver, especially for him. The driver not only drops him off at his office but also supports him while walking, helps him climb the stairs, and carries his belongings. Upon reaching his office cabin, the driver assists him in sitting down comfortably in his chair and stays nearby until he completes his work. Additionally, he helps him use his mobility aids whenever needed. Without a driver, his daily life would come to a standstill. Within this archetype, car ownership is commonly adopted as a convenience-oriented mobility strategy among elderly households, irrespective of gender or marital status, as it helps address both life-stage-specific and gendered mobility constraints. Importantly, the decision to purchase a car is closely coupled with reliance on driver assistance rather than independent driving. Acquiring driving skills later in life is perceived as difficult, and even among those who do learn, concerns related to accident risk and potential vehicle damage often discourage regular self-driving. 5.6.2 Archetype B: Utility Maximization motivated Households The theme of utility maximization corresponding to Reasons Cluster 2 (Fig. 2 ) is most strongly reflected in the socioeconomic profiles of households in Clusters A2′ and B (See Table 3 and Table 4 ). These households typically include school-going children and are often headed by a male primary earner engaged in self-employment or business activities. Consistent with these characteristics, most households in this group own two or more cars: one vehicle is commonly dedicated to routine household activities such as school drop-offs and organised leisure trips, while another is reserved for work-related trips only. The profile of the households in this group reflects occupational routines marked by frequent inter-city travel also apart from rigorous intra-city work travels, irregular working hours, which creates the need to respond quickly to shifting schedules. In such contexts, navigating congested city traffic through self-driving can impose substantial stress and time pressures. Within this everyday reality, relying on a personal chauffeur emerges as a practical mobility arrangement. The presence of a personal chauffeur enables the productive use of travel time for work-related tasks, communication, or recuperation, thereby enhancing overall time efficiency. For them, employing a personal chauffeur is not merely a matter of convenience but a rational response to complex mobility demands, allowing households to optimise daily routines often filled with uncertanity. [I 3]: A 33-year-old businessman with three school-going children hired a driver at the time of purchase due to erratic work hours; describes : “There is no fixed time when will I come and go … If a businessman would start driving in this traffic, he will go crazy. In the presence of a driver, I am always free and relaxed. …. Driving myself would not only reduce my efficiency but also impact the time I could spend with my family or attending to business matters. This flexibility is crucial for me because my schedule is unpredictable and whose work demands immediate attention.” [I 123]: A 40-year-old owner of a subcompact SUV dependent on a car for more than 20 years My business spans across a wide range of activities, and I’m often occupied with work, coordinating over the phone and dealing with unexpected events at work. With such a hectic schedule, it’s impossible for me to handle the chauffeuring duties of my children. [I 45]: Mother who had come to pick up her 14-year-old daughter accompanied by a chauffeur ….. We wanted to ensure their safety and avoid using school vans as the school is pretty far. Her father is employed in another district, and I am not very good at driving… Escalating safety issues outside schools, particularly due to the flocking of unruly individuals, prompted us to take this step. Another important detail about the profile of this archetype is that, male members are often the primary license holders, but demanding work schedules leave little room to manage family logistics, particularly those involving children. Interviews indicate that many parents enroll their children in schools located farther from home, where safety and supervision during travel become key concerns. Drivers often report back to parents, acting as a communication bridge that supports both security and peace of mind. As a result, employing a full-time driver emerges as a practical arrangement—not only to transport children but also to provide trusted oversight during trips. In this archetype, reliance on a personal chauffeur is therefore closely tied to the need to coordinate children’s mobility, ensure safety, and accommodate time-constrained household routines aligning with the household’s broader priorities and lifestyle needs. 5.6.3 Archetype C: Habitually comfort-seeking Households The habitually comfort-seeking households align closely with the profile of cluster D corresponding to the motivations most closely captured in Reasons Cluster 3. For many middle-aged and older household members—particularly those approaching their mid-fifties— the continued use of a personal chauffeur reflects a gradual shift away from self-driving rather than a sudden need. Subtle age-related discomforts, such as fatigue or reduced ease in night driving, combine with a declining inclination to drive, reinforcing reliance on chauffeur-driven travel. Notably, several female respondents in this group held valid driving licences and were economically active, yet continued to prefer being chauffeured. This preference appears rooted in long-standing household norms where driving was historically delegated to male members within the household and is now seamlessly transferred to a personal chauffeur. Overall, the mobility profile is shaped by habit and lifestyle continuity, where employing a personal chauffeur sustains comfort and convenience in everyday travel rather than responding to immediate mobility constraints. [I 33]: A female doctor and a mother of two teenage daughters who will with one elderly above 70 years : “My husband and I both come from a family where our parents kept drivers for their personal cars. Both our parents were salaried workers in stable jobs, and it was common for them to have drivers, especially since they worked in government positions. So, the habit of being driven is old…. At present, we have a driver to drive us to our clinics and hospital (workplace). It gives us a moment to rest when there is a driver. In fact, we’re even considering employing another driver because of the timing clashes between our schedules.….” Interviews revealed that the use of chauffeur-driven cars was common among elderly male respondents who previously held high-ranking government positions. In these cases, access to a driver was part of formal job entitlements, symbolising occupational status and authority. Over time, this institutional practice shaped personal mobility preferences, with many choosing to retain private drivers post-retirement, supported by household income. This pattern extended intergenerationally. Children raised in such households internalised the idea that self-driving was unnecessary even when licensed. For these households, employing a driver was not only a matter of convenience but a continuation of established norms associated with the prestige of a salaried profession. It underscores the role of occupational history and cultural norms in shaping driver dependency. For this archetype, employing a driver symbolized status, privilege, and professional success. 5.6.4 Archetype D: Habitually Assisted Households The habitually assisted archetype aligns closely with the profile of households in Cluster A2″, corresponding to the motivations most closest captured in Reasons Cluster 3 mostly characterized by multigenerational households. They typically include two or more economically active generations—often middle-aged siblings and their families—many of whom are self-employed or operate high-end businesses. With stable incomes and flexible work patterns, maintaining multiple vehicles and drivers becomes a practical way to manage the diverse and overlapping schedules of household members. Mobility demands in such households are inherently complex as different age groups access distinct destinations, such as schools, workplaces, and social or commercial venues, often requiring separate travel routes and timings. Drivers are employed not only to manage this logistical complexity but also to ensure that all household members enjoy flexibility, punctuality, and comfort. In these contexts, the driver functions as a mobility facilitator, enabling each member to maintain independence without the need for self-driving. The need for constant assistance is often a trait of elite households, passed down through generational habits. Observing family members using personal chauffeurs from a young age, household members tend to normalize and continue this practice as an extended form of household help. Since households in this archetype are typically multigenerational car users and personal chauffeur users, prolonged relationships with drivers allow them to engage in broader household tasks, integrating the chauffeur’s role into daily life. While this personalized assistance may seem minor, it reflects deeply ingrained habits shaped by parental practices and becomes a defining feature of their lifestyle. Chauffeurs insulate household members from the inconveniences and risks of direct car use, allowing travel to be limited to high-value activities such as professional commitments, curated leisure, or wellness routines. [Interview-68]: A 36-year-old male from a joint family of 15 members who has been using a car for more than 25 years and currently owns more than three luxurious cars. We are a joint family of 15 members, spanning three generations. We’ve been using driver assistance for more than 25 years now. It’s become a normal part of our lifestyle….. Honestly, no one really likes to drive…. It’s not that we can’t—most of us know how to drive, including women, but it’s just something we’ve gotten used to avoiding. The idea of self-driving feels unnecessary…. Also, we own different cars to meet everyone’s needs. These cars require careful driving because they’re high-performance vehicles, and improper handling can reduce their value…. Interview narratives repeatedly point to a shared theme within this archetype: employing a driver is not only a practical arrangement but also a subtle display of status. Households often described this practice as something long normalized within their close social and familial circles, where being constantly assisted becomes part of everyday life. Although not always expressed explicitly as a motivation, this lifelong exposure appears to normalize the practice and shape expectations around mobility. As a result, employing a driver is perceived as both a practical arrangement and a subtle marker of status. Over time, constant assistance becomes embedded in everyday life, reinforcing habits that reflect privilege, optimized time use, and continuity with long-standing social norms. 6. Discussion and Conclusion The present study examined for the first time why car users choose personal chauffeur automobility , using Patna, India, a case study. Personal chauffeur use is very common in the global south, including India and despite rising car ownership, there is not one single research on it. Our study is based on 126 semi-structured interviews compared to prior qualitative transport studies published in reputed journals that typically relied on interview sample sizes, ranging from 9 to 60 interviews ((Jason) Cao et al., 2019 ; Burnett & Lucas, 2010 ; Hoffmann et al., 2020 ; Kopnina, 2011 ; Levine, 2024 ; Li et al., 2023 ; Lin et al., 2024 ; Lucas et al., 2022 ; Merfeld et al., 2019 ; Mowri & Bailey, 2024 ; Næss, 2020 ; Paulson, 2018 ; Roy et al., 2024 ; Sherwin et al., 2014 ; Sigurdardottir et al., 2014 ; Tennant & Stilgoe, 2021 ). Our study adopts a mixed-methods approach, combining quantitative and qualitative data collection and analysis to gain deeper insights into personal chauffeured automobility. The research aimed to identify the underlying motivations for employing a personal chauffeur using open-ended responses from purposively selected car users who are users of a personal chauffeur. This study provides the first empirical evidence systematically documenting the range of reasons behind the need of personal chauffeurs, thereby addressing a largely implicit and understudied mobility practice in Global South. Firstly, our semi-structured interviews identified twenty-eight distinct reasons underlying dependence on personal chauffeurs. Based on the cluster analysis, four broad motivational themes underpin the choice of personal chauffeur-driven automobility: (i) physical or functional constraint–related reasons, (ii) utility maximization–related reasons, (iii) habitual comfort–related reasons, and (iv) habitual assistance–related reasons. Secondly, our findings show that the choice of personal chauffeured automobility is shaped by a combination of socioeconomic factors. The study examined household characteristics, including age distribution, presence of children and elderly members, number of economically active members—particularly those above 55 years—number of licensed drivers, occupation types, work- and school-related car trips, vehicle ownership, and types of cars owned and any others listed in Table 2 . Hypergeometric p-value analysis identifies characteristics that are statistically over-represented in clusters associated with chauffeur use. Households with school-going children and elderly members are more strongly linked to chauffeur use, reflecting needs for escorting and supervised travel due to safety and comfort concerns. These households prioritise reliability, flexibility, and reduced effort in daily mobility. Occupational status is also important, as salaried and self-employed households, reflecting stable incomes, enable the affordability of personal chauffeurs. Rather than indicating differences between occupational groups, the over-representation of both salaried and self-employed households suggests a shared preference for personal chauffeurs among economically secure groups. This convergence signals a normative mobility practice rather than occupation-specific behaviour. Here, employing a chauffeur is driven not only by affordability but also by social influence, where family, peers, and professional networks normalise chauffeur-driven travel as efficient and appropriate. Continued exposure to such environments reinforces this preference, turning it into a socially learned and validated choice. Over time, repeated reinforcement within close networks embeds chauffeur-driven automobility into everyday routines, making it less a deliberate decision and more an unquestioned mobility norm. In addition, long durations of car ownership (exceeding 20 years) and ownership of high-value cars (above ₹25 lakhs) indicate well-established car-oriented lifestyles. Prior research links expensive car ownership with car pride (Moody & Zhao, 2019 ; Zhao & Zhao, 2020 ). In the same vein, being driven in such vehicles can also be understood as part of pride-related experience. Multi-car households and frequent car-based work trips further indicates a strong dependency on private cars, where delegating driving responsibilities emerges as a functional need. Managing multiple vehicles entails ongoing demands—regular use, maintenance, timely repairs, and safeguarding high-value assets from unforeseen costs. In households with intensive work-related travel, these pressures intensify, making a personal chauffeur an effective mechanism for ensuring vehicle upkeep, reliability, and continuity of daily mobility. Thus, we observe that specific socioeconomic characteristics are systematically associated with the choice of personal chauffeured automobility. Thirdly, in this study, qualitative narratives from respondents were also used to contextualise the choice of personal chauffeured automobility. Beyond socioeconomic characteristics, the analysis reveals that situational and behavioural circumstances also play a critical role in shaping this mobility choice. These circumstances were categorized into four household archetypes: (A) Physical and Functional Constraint–led households, where health, age, or physical limitations necessitate assisted travel; (B) Utility Maximization–driven households, which adopt chauffeurs to optimize time use, reduce cognitive effort, and manage complex travel schedules; (C) Habitually Comfort-Seeking households, for whom convenience and travel comfort have become routine expectations; and (D) Habitually Assisted households, where long-standing trust on personal chauffeurs has normalized chauffeur-driven car use. Together, these archetypes demonstrate that personal chauffeured automobility is triggered not only by socioeconomic factors but also by life stage challenges, lived circumstances, habitual practices, lifestyle preferences and familiar norms as seen in their previous generation and social circle. Each household archetype has a unique way of living, shaped by their participation in daily activities, which clearly indicates why car users choose personal chauffeur automobility. 7. Benefit of the study and future scope of work Car ownership in India alone is expected to rise by an additional 262 million vehicles (Singh et al., 2020 ) amplifying existing challenges of congestion, and infrastructure (Verma, 2015 ). Alongside this surge, the prevalence of personal chauffeured automobility is also likely to grow. Capturing it is essential—not only to precisely explain how cars are used in India, but to anticipate how mobility behaviours may evolve in the future. Given the behavioural parallels between personal chauffeurs (PC) and personal autonomous vehicles (PAVs), analysing PC car use can significantly refine studies on implication of PAV especially preference towards PAV. (Wadud & Chintakayala, 2021 ) estimated preference towards PAV based on the value of convenience using a general population sample. More studies using PC car user’s value of travel time and experience of comfort can add to the findings. Previous studies on cross cultural difference in the acceptance of AV (Edelmann et al., 2021 ) can include the perception of personal chauffeur driven car users from India as current mobility practice is uncommon in global north. In order to bring improvement in those studies’ mobility choice of personal chauffeured car use needs more in-depth investigation. Studies estimating the AV travel demand such as (Dai et al., 2024 ) can refine the results by measuring the vehicle kilometre travelled of personal chauffeured car which closely reflects a delegated driving context similar to PAVs. Future work could examine the relationship between vehicle kilometre travelled and emission rates among personal chauffeur-driven users and self-driving car users, and further compare these patterns with those observed in developed countries. Such comparative analysis would help identify which user groups contribute most significantly to emissions and could support the design of evidence-based policies, including the categorical application of the polluter-pays principle. Studies which evaluate cost of PAV to understand the early adoption (Wadud, 2017 ) may consider adding the cost of keeping a personal chauffeur in the Indian context or other similar global south contexts to explain the transition better. (Wang et al., 2020 ) studied that attitude toward PAV and SAV. Similar analysis can compare personal chauffeured car users with ride-sharing users by linking their current motivations to future intentions. Such a comparison would offer deeper behavioural insights, helping anticipate adoption patterns and refine mobility models beyond simplified user classifications. Examining personal chauffeur–driven car use, and projecting its prevalence through scenario-based simulations, can reveal how equivalent PAV penetration levels—given their behavioural analogy—may impact road space and congestion in cities. This study also has strong implications for evidence-based policy. Interview findings indicate that users of personal chauffeur-driven cars often avoid paying parking charges, as chauffeurs typically wait in idle lanes or on nearby streets instead of using high-priced off-street or multi-level parking facilities. As parking fees are intended to increase the cost of car use and act as a deterrent, such practices effectively undermine the policy objective. Despite consuming higher vehicle kilometres travelled, personal chauffeur-driven car use frequently escapes these cost signals. Recognising and explicitly addressing chauffeur-driven use within parking and pricing policies could therefore help restore the effectiveness of parking charges as a demand-management tool. Note There is no one-to-one correspondence between Reasons Clusters (1–4) and household clusters (A1, A2′, A2″, B, C, D). Household mobility behaviour is inherently complex and shaped by overlapping motivations. Attempting rigid alignment risks oversimplifying this reality. Instead, the analysis identifies the most prominent and relatable motivations through the combined use of household archetypes and reason clusters. These represent the closest approximation of households’ lived circumstances, challenges, constraints, and aspirations, acknowledging that multiple influences coexist and vary in intensity across contexts. Household Clusters Most Associated Reasons Cluster Clusters A1 and C Reasons Cluster 1 Clusters A2′ and B Reasons Cluster 2 Cluster D Reasons Cluster 3 Cluster A2″ Reasons Cluster 4 The formula for calculating the over-represented p-value is given below: $$\left(overrepresented\right)pvalue=1-\sum_{i=0}^{x-1}\frac{\left(\begin{array}{c}M\\i\end{array}\right)\left(\begin{array}{c}N-M\\n-i\end{array}\right)}{\left(\begin{array}{c}N\\n\end{array}\right)}$$ $$\left(underrepresented\right)pvalue=\sum_{i=0}^{x}\frac{\left(\begin{array}{c}M\\i\end{array}\right)\left(\begin{array}{c}N-M\\n-i\end{array}\right)}{\left(\begin{array}{c}N\\n\end{array}\right)}$$ ‘ N’ is the total number of samples collected for the interview (N = 126). ‘M’ is the total number of successful samples found in different household characteristics in the total interviewed data (e.g. in a household with school-going children, M = 64, Refer Table 2 ). ‘ n’ is the total number of samples in one cluster (e.g., n cluster A2′ = 21, Refer Table 2 heading). ‘x’ is the number of successful sample of households with school-going children in the cluster of interest, i.e., Cluster A2′ (x = 15). 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Transp Res Part D: Transp Environ 72(May):261–278. https://doi.org/10.1016/j.trd.2019.05.008 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9002386","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598838391,"identity":"f4b0a18e-fc6e-4df2-8992-1bc06e7d4245","order_by":0,"name":"Purnima Kumari Chowdhury","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-4766-0834","institution":"Indian Institute of Technology Kharagpur","correspondingAuthor":true,"prefix":"","firstName":"Purnima","middleName":"Kumari","lastName":"Chowdhury","suffix":""},{"id":598838392,"identity":"ee6aa6ad-464a-45dc-bce4-c4474792bba3","order_by":1,"name":"Ronita Bardhan","email":"","orcid":"https://orcid.org/0000-0001-5336-4084","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Ronita","middleName":"","lastName":"Bardhan","suffix":""},{"id":598838393,"identity":"4d8d4a39-791f-4410-bee1-0bedb04d6f9d","order_by":2,"name":"Tarak Nath Mazumder","email":"","orcid":"","institution":"Indian Institute of Technology Kharagpur","correspondingAuthor":false,"prefix":"","firstName":"Tarak","middleName":"Nath","lastName":"Mazumder","suffix":""}],"badges":[],"createdAt":"2026-03-01 15:08:52","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9002386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9002386/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104105614,"identity":"cbe38a99-ded1-4271-aa93-3427934c7e72","added_by":"auto","created_at":"2026-03-06 21:43:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":146336,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological framework adopted for the study (A, B, C…marked are just for pictorial representation)\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9002386/v1/f2a0574ead885f374b8f4876.jpg"},{"id":104105612,"identity":"49d00eef-11e5-4fa1-b4c2-bd05a95af7e2","added_by":"auto","created_at":"2026-03-06 21:43:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":372174,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis of reasons for choosing Personal Chauffeured Automobility\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9002386/v1/718f90c5de6b44e278666407.jpg"},{"id":104105613,"identity":"82a1515f-5ac6-4dd7-88b6-747d8d6ca037","added_by":"auto","created_at":"2026-03-06 21:43:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102660,"visible":true,"origin":"","legend":"\u003cp\u003eCluster Analysis of Households that choose personal chauffeured automobility\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9002386/v1/d506dbb3c33532ffded57265.jpg"},{"id":104403433,"identity":"ed8c0852-d980-438a-8c3f-5bbc6eeff421","added_by":"auto","created_at":"2026-03-11 12:18:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2829111,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9002386/v1/bf97c809-db61-428f-bd24-cbf199522633.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA Household Level Investigation into Personal Chauffeured Automobility\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThere is ample research in global south on mobility choice of using a car due to the influence of multiple socioeconomic characteristics (Guerra, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Meena et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Prabnasak \u0026amp; Taylor, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Raditya et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Srinivasan et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Verma, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wu \u0026amp; Tang, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); presence of other competetive modes (Dash et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vasudevan et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), built environment factors (Ashik et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Feng, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); psychological factors (Prawira F. Belgiawan \u0026amp; Schmocker, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Prawira Fajarindra Belgiawan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Martins et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Meena et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Moody \u0026amp; Zhao, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wu \u0026amp; Tang, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao \u0026amp; Zhao, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); attitudinal factors (Kim et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Verma et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vos \u0026amp; Alemi, 2020; Zhou \u0026amp; Wang, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite wide research on private car use, there is no study capturing the behaviour of two categories of car owners in the Global South: (a) self-drivers and (b) owners who rely on a personal chauffeur. None of the disaggregate choice models on car use and ownership has taken into consideration that the two car owners may or may not have different behaviour.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Research Gap and motivation of the study\u003c/h2\u003e \u003cp\u003ePersonal chauffeured automobility refers to a mobility choice in which car-owning households employ a personal chauffeur to drive the car for household members. It is a very common mobility behaviour in the Global South. This mobility choice is largely absent in the Global North. In India, car owners across diverse income groups employ personal chauffeurs (PC) at monthly salaries ranging from INR 8,000 to INR 15,000 (87USD \u0026minus;\u0026thinsp;163USD) depending on daily trip frequency, vehicle kilometres travelled, and additional services provided to the household. Far from being a rigid arrangement, chauffeur use is often fluid\u0026mdash;households may hire drivers temporarily or share them within familial or neighbourhood networks. In many cases, the decision to employ a chauffeur is not incidental but integrated into the initial vehicle purchase, with households factoring in the recurring cost as part of car ownership itself.\u003c/p\u003e \u003cp\u003eDespite its visibility on the streets, this form of mobility remains statistically invisible. There is no systematic dataset capturing how many car-owning households rely on personal chauffeurs in India or across the Global South. Household travel surveys are scarce, and even where they exist, they overlook this practice. Administrative data\u0026mdash;such as vehicle registration or insurance records\u0026mdash;also fail to capture chauffeur employment. Parking systems do not distinguish between self-driven and chauffeur-driven cars, leaving the daily volume of such cars unrecorded. Nor do government systems track personal chauffeurs operating private cars, resulting in a lack of even basic information on their age, driving experience, or earnings. While many cab and taxi drivers are linked to unions or platform operators that maintain structured records, personal chauffeurs operate largely outside such systems. Their employment is typically informal, arranged through personal networks and referrals, making them effectively invisible in official data. Even in systems where such data could be captured\u0026mdash;like digital traffic fines or toll payments\u0026mdash;no information is recorded on whether a car is self-driven or chauffeur-driven. As a result, the scale and characteristics of household-level chauffeured car use remain largely undocumented in India.\u003c/p\u003e \u003cp\u003eThere is a clear need to study personal chauffeured automobility, as it remains an overlooked yet significant form of car use that limits our understanding of everyday mobility behaviour. Existing research and disaggregate choice models often assume car users as a homogeneous group, overlooking such distinct practices and thereby simplifying reality. Recognising this form of automobility is crucial to capture behavioural diversity more accurately. It also has important implications for emission studies, as different user groups generate varying levels of vehicle kilometres travelled. Without accounting for these differences, policy interventions risk being misdirected. Studying this phenomenon is therefore necessary to develop more realistic models and design targeted, evidence-based transport and environmental policies.\u003c/p\u003e \u003cp\u003eThis study is the first to systematically address this gap, drawing on semi-structured interviews with 126 users to uncover both the motivations behind chauffeured automobility and the profiles of those who adopt it. It is guided by the following research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhy do car owners choose to rely on a personal chauffeur to access their own vehicles?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhich socioeconomic and household characteristics distinguish those who adopt chauffeured automobility?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat behavioural patterns and lived circumstances characterize households that choose chauffeured automobility?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis study combines qualitative depth with quantitative rigour to uncover why households rely on personal chauffeurs. Semi-structured interviews with car-owning households in Patna first captured a wide range of motivations through open-ended responses, alongside detailed socioeconomic data. Cluster analysis was then used to identify dominant motivation patterns and distinct household types separately. These clusters were further interpreted using over-representation analysis to highlight defining characteristics. Finally, both strands were integrated and enriched with qualitative insights to construct household archetypes, offering a clear, behaviourally grounded explanation of how lived realities shape the choice of chauffeured automobility.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe study is structured as follows: Section 2 reviews the literature on how to approach studies where there are no prior work and the importance of developing archetypes in understanding underexplored behaviours. Section 3 presents the study area and data used in the study. Section 4 presents the analysis approach in the study. Section 5 presents the analysis and the results obtained. Finally, Section 6 offers discussion, conclusions, and lastly future scope of work is presented in Section 7.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Theoretical foundation","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Pragmatic approach to producing in depth behavioural insights\u003c/h2\u003e \u003cp\u003eIn the absence of both a strong theoretical foundation and prior empirical evidence on personal chauffeured automobility, \u0026mdash;it is necessary to first undertake exploratory research capable of generating insights sufficient for hypothesis development. In such under-researched contexts, a pragmatic research design is essential to uncover patterns and build an initial evidence base (Evans et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). A pragmatic approach combines qualitative and quantitative methods in sequence, enabling a richer and more comprehensive understanding of complex, underexplored phenomena (Bryman, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012c\u003c/span\u003e; Creswell, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This approach is referred to as mixed-method (Creswell \u0026amp; Clark, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite its advantages, its application in transport-related studies is still scarce. Few examples are: ((Jason) Cao et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Clark et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Heinonen et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hopkins et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jiron \u0026amp; Carrasco, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tiznado-Aitken et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA mixed-methods study in Greece illustrates this well: survey data (\u0026asymp;\u0026thinsp;375 respondents per city) quantified mode-specific differences in travel satisfaction, while 14 qualitative interviews explained these patterns by revealing how public transport limitations and urban conditions shape lived travel experiences (Mouratidis et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To fill the gaps in studies on behavioural drivers of car ownership, mixed-methods approaches have proven particularly effective in generating deeper insights. For instance, a study in Iceland used qualitative data to reveal psychosocial factors\u0026mdash;such as social norms, status associated with cars, and stigma around public transport\u0026mdash;embedded in Reykjav\u0026iacute;k\u0026rsquo;s urban context, while the logistic regression tested how factors like residential location, socio-demographics, attitudes, and preferences shape ownership decisions (Heinonen et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Together, the methods revealed residential self-selection, a phenomenon difficult to isolate through quantitative analysis alone. Another study (K\u0026ouml;nig \u0026amp; Dre\u0026szlig;ler, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrates the strength of mixed-methods in capturing complex behavioural change. By combining 301 quantitative data with 15 qualitative insights, it was possible to track measurable shifts in mobility patterns in Northern Germany while also uncovering how individuals experienced and adapted to these changes during COVID-19. In another study, (N\u0026aelig;ss, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrates that incorporating qualitative insights helps avoid common quantitative pitfalls such as model misspecification by informing the selection and causal ordering of control variables.\u003c/p\u003e \u003cp\u003eA study in Concepci\u0026oacute;n, Chile shows how combining quantitative and qualitative data can uncover the complex, context-dependent nature of everyday mobility strategies (Jiron \u0026amp; Carrasco, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By capturing both measurable patterns and the underlying social and spatial dynamics, this approach reveals how travel decisions are shaped by overlapping factors\u0026mdash;offering a far more nuanced understanding than single-method studies. (Tiznado-Aitken et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also adopted mixed-methods research in Santiago de Chile that demonstrates how integrating qualitative travel experiences with quantitative accessibility indicators reveals dimensions of public transport accessibility that are overlooked in studies with purely quantitative analyses. Previous study by (Clark et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) used a mixed-methods approach to examine car aspirations among adolescents in New Zealand, based on data from 184 households to provide a causal explanation of changes in household car ownership over time. In another case, where socio-cultural background and built environment factors were examined to explain automobility in households with older adults, both qualitative and quantitative data were used. It proved helpful in understanding how the social and cultural contexts impact the travel patterns of older Chinese adults (Feng, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the literature shows that mixed-methods approaches are particularly powerful for studying everyday mobility strategies shaped by lived experiences and real-life circumstances. They allow researchers to move beyond what people \u003cem\u003edo\u003c/em\u003e in travel behaviour to understand \u003cem\u003ewhy\u003c/em\u003e they do it\u0026mdash;capturing shifts in travel preferences driven by past experiences, and revealing how social norms, identity, and status influence mobility choices. As a result, mixed methods provide a richer, more grounded understanding of mobility behaviour\u0026mdash;particularly in emerging research areas where purely model-driven studies may overlook important social and contextual realities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Importance of creating archetypes in understanding underexplored behaviours\u003c/h2\u003e \u003cp\u003eWhile socioeconomic context remains a relevant factor, an overreliance on such variables may obscure deeper motivations, values, and lived experiences that shape everyday decision-making. As several studies have demonstrated, transport behaviours are intricately embedded in daily life activities, lifestyle preferences, and routine participation in social and economic spheres (Acker et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Acker \u0026amp; Witlox, 2010; Kitamura, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pas \u0026amp; Koppelman, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Van Acker, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Van Acker et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Daily life experiences, therefore, provide a more powerful lens for understanding behaviour and habits, as they capture the routines, preferences, and choices enacted in lived contexts. Such an approach offers dynamic, grounded insights into how individuals interact with environments, revealing dimensions of mobility behaviour that remain invisible in static socioeconomic analyses.\u003c/p\u003e \u003cp\u003eTo better capture behavioural heterogeneity, archetypes have emerged as a valuable analytical approach in qualitative transport research. Archetypes facilitate the identification of recurring behavioural profiles that transcend socioeconomic categories, enabling systematic analysis of how and why specific mobility choices are made. Archetype classification supports the examination of culturally embedded practices by synthesising repeated behavioural patterns across heterogeneous user groups (J.Arnould \u0026amp; L.Price, 2000). Rooted in the concept of \u003cem\u003eideal type\u003c/em\u003e (Weber, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1904\u003c/span\u003e), archetypes enable researchers to interpret complex social phenomena through systematic comparison of cases or participants within qualitative datasets. This approach facilitates the identification of behavioural clusters, supporting deeper analysis of variation and comparability across household decision-making contexts. Archetype formation has also been shown to capture multiple layers of implicit and often unconscious behaviour at both individual and group levels (Sietz et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrior research has widely applied archetype-based approaches to capture behavioural heterogeneity across different domains. For instance (Ben \u0026amp; Steemers, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ortiz \u0026amp; Bluyssen, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) employed household archetypes to examine occupant comfort and adaptive behaviours in residential buildings in the Netherlands, while (Heinrich et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) developed seven behavioural archetypes to explain patterns of domestic energy use in the UK, demonstrating how lifestyle and value orientations shape energy-related choices. Beyond household behaviour, archetype analysis has been used to identify recurring business model configurations in the connected car sector (Sterk et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the transport planning domain, (Navarro-Ligero \u0026amp; Valenzuela-Montes, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) developed scenario-based archetypes to analyse the evolving planning logic of Granada\u0026rsquo;s Light Rail Transit (LRT) system. Drawing on survey data and planning documents, their study illustrates how archetypes can function as strategic analytical frameworks for interpreting institutional preferences and supporting integrated transport and urban development planning. Other research emphasises the value of everyday mobility narratives and anecdotal evidence in capturing the lived realities of travel behaviour that are often obscured by highly aggregated analytical approaches. By foregrounding users\u0026rsquo; perspectives on travel mode choice, such methods provide rich behavioural insights that may be overlooked in expert-driven or purely quantitative assessments of sustainable mobility (von Sch\u0026ouml;nfeld et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA comparative study of thirty cities across Europe, Asia, Africa, and the Americas employed archetypes to classify transport solutions addressing common urban mobility challenges, thereby providing a structured framework for interpreting global transport systems (Thomson, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Building on this foundation, (McIntosh et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) extended Thomson\u0026rsquo;s typology by classifying 26 additional global cities using the same five archetypes. Using household travel survey data from Minneapolis, (Charleux, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) developed mobility archetypes to uncover latent patterns in travel mode choice, trip purpose, and daily mobility rhythms that remained obscured in conventional aggregate analyses. Through the application of clustering techniques and systematic archetype characterization, the study demonstrated how archetype-based analysis can capture heterogeneity in lived travel experiences across demographic groups, offering a more human-centered interpretation of urban mobility. These contributions highlight the analytical potential of archetype formation for revealing behavioural diversity beyond standard quantitative representations.\u003c/p\u003e \u003cp\u003eThus, we can see that archetype-based analysis offers several advantages for studying underexplored mobility behaviours. First, it enables the systematic identification of \u003cem\u003ebehavioural heterogeneity\u003c/em\u003e that extends beyond conventional socioeconomic classifications, allowing researchers to examine how and why mobility choices are made across diverse household contexts. Second, by synthesising recurring thick descriptions or original narratives of the respondents about any behaviour, archetypes help reveal culturally embedded, habitual, and often implicit decision-making processes that are typically obscured in highly aggregated or purely quantitative analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Case Study area: Patna, India\u003c/h2\u003e \u003cp\u003eThe study was conducted in the urban area of Patna, the capital of Bihar, which offers a compelling context for examining personal chauffeured car use. Over the past decade, the city has witnessed rapid growth in private motorisation, with car ownership rising from about 50 cars per 1,000 people in 2011 and projected to exceed 125 per 1,000 by 2025 (Ghate \u0026amp; Sundar, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).This growth has unfolded alongside an urban transport system that remains limited in scope and efficiency. With inadequate bus services and the absence of mass rapid transit options such as metro rail or BRT systems, households increasingly rely on private cars to meet their mobility needs (Tiwari \u0026amp; Sharma, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This dependence is further intensified by structural constraints within the city. Road infrastructure covers only 5.9% of the urban land area, and most major corridors have right-of-way widths of less than 30 metres. Volume-to-capacity ratios on arterial roads frequently exceed one, reflecting persistent congestion and travel delays (CMP, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Encroachments and widespread on-street parking further reduce the effective capacity of already constrained roads. In such a dense and infrastructure-limited urban environment, where viable alternatives to car use are scarce, everyday mobility decisions take on deeper social and practical significance. Patna therefore provides a particularly insightful setting to explore why households not only own cars but also continue to rely on personal chauffeurs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data collection approach\u003c/h2\u003e \u003cp\u003eTo achieve the research questions (See section 1.1 ), the first author conducted in-depth semi-structured interviews (Bryman, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e; Creswell, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Creswell \u0026amp; Clark, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) with a purposively selected sample (Ahmad \u0026amp; Wilkins, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Marshall, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) of car owners who are dependent on a personal chauffeur. The interview format typically comprised collection of socioeconomic data and open-ended questions designed to encourage interviewees to speak broadly about and around the need of a personal chauffeur, thereby allowing new ideas and insights to emerge that the researcher might not have otherwise anticipated.\u003c/p\u003e \u003cp\u003eA pilot study involving fifty car users was conducted prior to the main interviews to gain preliminary insights into the motivations for employing a personal chauffeur (PC). Insights from the pilot study revealed a key practical challenge: it was difficult to identify in advance which households actually employed PC. Consequently, participant recruitment was primarily undertaken in public and semi-public urban spaces where car users are more readily accessible, such as commercial areas, parking facilities, and other activity hubs. Participants were approached in on-street and off-street parking areas, near commercial establishments, educational institutions, healthcare facilities, fuel stations, and, in some cases, adjacent residential areas. This strategy helped minimise location-specific bias and facilitated access to a diverse cross-section of PC car users. All participants were approached directly by the first author of this study, the purpose of the study was clearly explained, and informed consent was obtained prior to audio recording. Interviews were conducted in Hindi which typically continued around 40 minutes, with some extending up to one hour.\u003c/p\u003e \u003cp\u003eThe semi-structured interview consisted of two main sections. The first section collected quantitative data on the socioeconomic characteristics of PC car households. The second section comprised qualitative questions that explored the reasons prompting dependence on a PC, underlying motivations, and the perceived advantages of employing a driver.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Data collection: Quantitative data\u003c/h2\u003e \u003cp\u003eThe semi-structured interview captures detailed household-level socioeconomic characteristics, including age distribution, household composition, the presence of children and elderly members, the number of earning members, occupational profiles, and income categories. In addition, vehicle-related information was collected on car ownership levels, vehicle typology, and the price range of vehicles that households are currently able to afford. The study also records driving license ownership among household members, distinguishing between active and inactive license holders. Finally, travel behaviour data document trip-making patterns across modes, identifying trips undertaken using personal cars as well as those specifically assisted by a personal chauffeur. Description of the data is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Data collection: Open-ended questions\u003c/h2\u003e \u003cp\u003eEmphasis was given to directing the participants to share their lived experiences or cite daily life incidents or a major incident that is associated with the need to rely on a personal chauffeur. The interview included questions like the following: \u0026ldquo;What is the use of the car in the household?\u0026rdquo; and \u0026ldquo;What was the main reason for depending on a full-time chauffeur? Did any specific change in your life trigger the need of a driver?\u0026rdquo; These were instrumental in capturing if any life course event influenced the role of car and driver within the household. Moreover, inquiries about \u0026ldquo;mobility needs\u0026rdquo; and \u0026ldquo;travel routines\u0026rdquo; such as \u0026ldquo;which of the trips within the households are carried out by a personal car with a driver?\u0026rdquo; were also done. It offers a broad view of how the presence of a driver fits into their daily life mobility requirements. Intergenerational continuities were also probed with questions like: \u0026ldquo;Did your parents or previous generations employ drivers?\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Determination of sample size in carrying out interviews\u003c/h2\u003e \u003cp\u003eTheoretical saturation is the yardstick for determining sample size in the case of interviews (Charmaz, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Glaser \u0026amp; Strauss, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1967\u003c/span\u003e). Saturation refers to the point at which additional interviews no longer yield new information about the phenomenon under investigation and hence, carrying out more interviews with households needs to stop. Conducting interviews beyond this point risks unnecessarily expanding the dataset, thereby complicating theme or pattern identification (Bryant \u0026amp; Charmaz, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Bryman, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e; Morse, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Determining when saturation has been achieved, remains challenging (Guest et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Malterud et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Several strategies have been proposed to assess saturation (Hennink \u0026amp; Kaiser, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, a code frequency count approach was adopted, indicating that the reasons for relying on a personal chauffeur in Indian households were sufficiently comprehensive and that no new reasons or motivation emerged from subsequent interviews.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Methodology","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Analysis approach\u003c/h2\u003e \u003cp\u003eThe study adopts a sequential mixed-methods approach to answer the central research question: \u003cem\u003ewhy do car users need a personal chauffeur.\u003c/em\u003e In the first step, interview transcripts were transcribed daily in parallel with the field visits. Excel spreadsheet was maintained in which all reasons reported by each respondent were systematically listed by the first author. Respondents were not provided with a predefined list of reasons; instead, they articulated their motivations in their own language from their own lived realities. Emerging reasons were registered and reviewed daily at the end of the day. For instance, if a respondent stated that commuting becomes less stressful when travel is managed by a personal chauffeur (along with their broader lived experience explanation), this reason was recorded in the Excel sheet as R1 and coded as 1. If another respondent did not report this reason, it was coded as 0. This helped to determine whether any new reasons had emerged if yes then a new column would be added. In this way the entire spreadsheet was coded in binary based on which Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e was prepared. After the ninth interview, no new reasons were identified for a prolonged period; a new reason was recorded only during the seventy-third interview. Data collection was nonetheless continued for an additional 53 interviews to ensure robustness. As no further new reasons emerged during this phase, we stopped the interviews at 126 sample size.\u003c/p\u003e \u003cp\u003eAs reasons were coded line by line from the transcripts each day, recurrent patterns began to emerge (Bryant \u0026amp; Charmaz, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). To further explore these patterns, we conducted a cluster analysis (Wagschal, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) \u0026ndash; an unsupervised machine learning technique for classification on the reasons mined from the open ended responses. Cluster analysis was performed in Python. The analysis was performed in Python and helped reveal underlying themes and relationships among them, allowing individual reasons to be grouped into broader motivational themes. The aim was to identify dominant thematic constellations that explain why car users perceive a need for a personal chauffeur and what discourages them from self-driving. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the methodological framework.\u003c/p\u003e \u003cp\u003eIn the next step, we conducted a second cluster analysis at the household level. While the reasons were coded every day. All the interviewed households were given a nomenclature too. This analysis resulted in distinct household clusters that shared common motivational profiles for employing a personal chauffeur. In the subsequent step, we assessed the statistical robustness of the identified clusters. We conducted chi-square tests to examine whether significant differences existed between clusters across categorical variables, thereby confirming that the observed groupings were not due to random variation. We then applied hypergeometric p-value estimation to identify socioeconomic characteristics that were overrepresented within each cluster. The study uses a one-sided test in which a threshold of significance was determined at (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) for both over-representation and under-representation. Finally, we developed household archetypes by integrating insights from the motivation (reason) clusters and household characteristics with thick qualitative descriptions. It was a triangulation process. We moved back and forth between the interview transcripts, the motivation (reason) clusters, and the prominent household characteristics to observe a coherent household profile. This profile helped explain why certain reason clusters appeared as they did, providing a grounded interpretation of why households choose to rely on a personal chauffeur.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Analysis and Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1 Reasons for choosing personal chauffeured automobility\u003c/h2\u003e\n \u003cp\u003eA total of twenty-eight distinct reasons for employing a personal chauffeur emerged from the semi-structured interviews (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, the distribution of responses indicates that employing a personal chauffeur is primarily driven by stress reduction, travel management, and household time coordination, while safety concerns, gendered mobility roles, and structural constraints play secondary but reinforcing roles in shaping this reliance. The high prevalence of reasons related to operational ease and professional driving suggests that households view the chauffeur as an extension of their mobility system rather than merely a driver. At the same time, mid- and lower-frequency reasons reveal how this practice is embedded within broader household dynamics, including division of responsibilities, activity patterns, and occasional situational needs. Together, these patterns highlight that reliance on a personal chauffeur is shaped by a combination of everyday mobility management and context-specific household circumstances. While several reasons may appear similar at first glance, they were expressed in different contextual settings and reflected distinct lived experiences and decision-making logics. Therefore, documenting the full range of articulated reasons was considered essential to preserve contextual nuance.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eList of reasons behind the need for a personal chauffeur\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReason\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% of Households\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommuting becomes less stressful when travel is managed by a personal chauffeur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVehicle maintenance and upkeep are better managed by professionally trained chauffeurs with superior technical knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChauffeurs provide reliable, continuous assistance with household errands and light chores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChauffeurs possess professional driving expertise, including better knowledge of traffic rules, road hierarchies, and informal driving practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChained trips are executed more efficiently when a chauffeur manages the vehicle between activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold time constraints make delegating driving responsibilities to a chauffeur more practical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParking search and cruising in areas with limited parking supply are more effectively handled by chauffeurs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale household members lack driving skills, making reliance on a chauffeur more practical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDriving after dark is perceived as uncomfortable and is therefore delegated to a personal chauffeur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequent long-distance or inter-district travel is more manageable when driving is handled by a chauffeur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale household members show limited preference for driving and delegate this task to a chauffeur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLack of confidence in self-driving leads households to rely on chauffeurs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConcerns about aggressive and rash driving behaviour are mitigated when a professional chauffeur is driving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale worker in the household has to access the chain of work locations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple simultaneous activities can be coordinated through chauffeur-managed vehicle use without owning multiple cars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaiting time outside activity locations is reduced when a chauffeur manages vehicle movements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEscorting children to school and extracurricular activities is more dependable with a chauffeur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale household members lack driving skills, increasing dependence on chauffeurs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrregular work schedules and unexpected travel demands are more easily accommodated by a chauffeur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale household members show low preference for driving and delegate driving to a chauffeur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequent phone calls, and texting basically need to stay connected online, making it difficult for members to focus on self-driving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChauffeurs provide practical support for learning driving skills and vehicle maintenance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical-related trips are handled more smoothly when a chauffeur assists ill or elderly household members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDriver was kept due to residential location and built environment factors that hindered easy car use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTravel during odd hours or to less crowded locations feels safer when accompanied by a chauffeur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimited mobility among household members necessitates dependable driving assistance from a chauffeur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold members reside in geographically disadvantaged areas where there is a lack of suitable modes of travel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemporary inability to self-drive following an accident makes chauffeur assistance essential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2 Cluster Analysis of Reasons for Choosing Personal Chauffeured Automobility\u003c/h2\u003e\n \u003cp\u003eTo understand the broader motivations underlying personal chauffeured automobility, hierarchical clustering techniques were employed to move beyond analysing individual reasons in isolation and to reveal latent motivation. Each household representative articulated multiple reasons relevant to their decision, indicating that chauffeur reliance is shaped by a combination of factors rather than a single dominant motive. A preliminary review of the responses suggested that, despite variation at the individual level, there were clear thematic commonalities across households. The hierarchical clustering results reveal two broad motivational stems, which are further subdivided into distinct clusters labelled A, B, C, and D and differentiated by colour as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe reasons were grouped into mutually exclusive clusters, with each reason assigned to only one cluster to maintain thematic clarity. However, individual households often reported reasons spanning multiple clusters, indicating that multiple motivations can simultaneously shape the decision to rely on a personal chauffeur. The dendrograms display these groupings, with each cluster labelled to reflect its dominant underlying theme and overall motivational logic. Each cluster presents a distinct motivation, based on which they are given labels described below.\u003c/p\u003e\n \u003cp\u003ea. Cluster 1 reasons: Physical or Functional Constraint-related reasons.\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThis motivational category reflects situations in which reliance on a personal chauffeur emerges from constraints that limit the feasibility of self-driving. Such constraints may be physical, health-related, age-related, or situational in nature. In these contexts, chauffeur assistance enables households to sustain everyday mobility, maintain autonomy in daily activities, and navigate travel demands that would otherwise be difficult to manage independently.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eb. Cluster 2 reasons: Utility Maximization related reasons\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIt reflects a motivation to streamline everyday travel by delegating driving tasks in order to save time, reduce cognitive and physical effort, and maintain flexibility in the face of complex and unpredictable daily schedules. Households in this category value the reliability and readiness offered by a personal chauffeur, which allows them to coordinate multiple activities, respond quickly to changing travel demands, and sustain high levels of productivity without being constrained by the demands of self-driving.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003ec. Cluster 3 reasons: Habitually comfort-related reasons\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe combination of the reasons in Cluster C reflects the role of habitual comfort and emotional ease in motivating households to employ a driver. This cluster signifies how households prioritise a relaxed commuting experience while factoring in the influence of long-standing habits, cultural norms, and prior exposure to driver assistance.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003ed. Cluster 4 reasons: Habitually Assisted related reasons\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe clustered reasons emphasise the role of deeply ingrained habits, practical reliance, and a seamless lifestyle enabled by personal chauffeur use. This cluster of reasons reveals how individual incidents or isolated conveniences do not solely drive the decision to employ a driver but are instead shaped by routine reliance on external assistance for transportation, managing logistical challenges.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3 Socioeconomic characteristics of the households that choose personal chauffeur automobility\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of interviewed households and their characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHousehold Characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCount (Total N\u0026thinsp;=\u0026thinsp;126)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHousehold Size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than three\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThree-four\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than five\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with school-going children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale elderly above 55 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale elderly above 55 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with the elderly above 55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with economically active above 55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold With Immobile Members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold annual income\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Earning Members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePensioner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale earners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOccupation Type categories:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSalaried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSalaried and self-employed both\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePensioner only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDriving license male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDriving license female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of cars categories:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than two cars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration of car ownership:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 to 10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 to 15 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 to 20years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than 20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrice of car categories:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 5 lakhs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 to 10 lakhs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 to 15 lakhs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 to 25 lakhs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove 25 lakhs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCar variant categories:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHatchback only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSedan only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Utility Vehicle (SUV) only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMulti-utility vehicle (MUV) only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuxury only (above 9\u0026nbsp;million)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple variants (two SUV\u0026thinsp;+\u0026thinsp;SUV+ Luxury, SUV\u0026thinsp;+\u0026thinsp;MUV, SUV+ hatchback)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to school:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 5 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 to 10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than 10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold with no school-going children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold with school trips by car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold with organized leisure activities by car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold with work trips by car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e[1] Medium Income = INR 2,73,000 - 8,46,000; High Income= INR 8,46,000 - 15,00,000; Very High Income= INR 15,00,000 above. The classification is based on the world bank income group classification for low-income countries.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e5.4 Cluster analysis of the households that choose personal chauffeur automobility\u003c/h2\u003e\n \u003cp\u003eCluster analysis of households is presented Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The hierarchical dendrogram reveals two broad stems, labelled Cluster X and Cluster Y, based on the primary cut indicated by the dotted line. The dotted line is manually drawn to indicate the cut-off points in the dendrogram, highlighting the two main groups formed by the clustering. For analytical clarity, Cluster X is further subdivided into two sub-groups, labelled Clusters AB and C. The AB branch is then disaggregated into Cluster A, which is further refined into sub-clusters A1 and A2. At a deeper level of the hierarchy, Cluster A2 is additionally subdivided (A2\u0026prime; and A2\u0026Prime;), capturing finer distinctions in household profiles. Cluster Y is seen as a single cluster and labelled Cluster D. The findings indicate that reliance on personal chauffeured automobility spans a wide range of household types. Since we had given nomenclature to each sample when the interviews were done, we were able to capture what typologies of households are present in each cluster.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of household clusters that choose driver assistance\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHousehold Characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCluster A\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster B\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster C\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster D\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA2\u0026prime;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA2\u0026Prime;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than three\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThree-four\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFive or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with school-going children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale elderly above 55 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale elderly above 55 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with the elderly above 55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with economically active individuals above 55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold With Immobile\u003c/p\u003e\n \u003cp\u003eMembers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold annual income\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Earning Members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePensioner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale earners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOccupation Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSalaried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSalaried and self-employed both\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePensioner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDriving license male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDriving license female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of cars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than two\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration of car ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 to 5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 to 10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 to 15 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16to 20years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than 20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrice of the car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than five lakhs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 to 10 lakhs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 to 15 lakhs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 to 25 lakhs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove 25 lakhs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCar variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHatchback only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSedan only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Utility Vehicle (SUV) only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMulti-utility vehicle (MUV) only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuxury only (above 9\u0026nbsp;million)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple variants (Two hatchbacks, Hatchback\u0026thinsp;+\u0026thinsp;SUV, Two SUVs, SUV\u0026thinsp;+\u0026thinsp;MUV etc)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 5 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 to 10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than 10 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo school-going children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSchool trips by car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrganised leisure activity trips by car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWork trips by car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresence of a two-wheeler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e[2] Medium Income = INR 2,73,000 - 8,46,000; High Income= INR 8,46,000 - 15,00,000 ; Very High Income= INR 15,00,000 above. The classification is based on the world bank income group classification for low income countries.\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of different household typologies under each cluster\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDominant Household Types\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHousehold Composition Breakdown (%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eThis is same nomenclature which is elaborated in third paragraph of section 4.1\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKey Characterization\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredominantly elderly households\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Older adults living alone without dependents: \u003cstrong\u003e47%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Multigenerational families: \u003cstrong\u003e38%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Older adults (late 50s) living with young adults: \u003cstrong\u003e16%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly age-driven household dominated by elderly residents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster A2\u0026prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMid-life stage households\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Parents (early/late 40s) with school-going children: \u003cstrong\u003e38%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Multigenerational families: \u003cstrong\u003e29%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Older adults (late 50s) with young adults: \u003cstrong\u003e19%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Late 40s households with school-going members: \u003cstrong\u003e14%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds managing overlapping caregiving and multiple mobility patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster A2\u0026Prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultigenerational households\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Multigenerational families with school-going and non-school-going children: \u003cstrong\u003e44%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Elderly living with young adults: \u003cstrong\u003e33%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Parents (late 40s) with adolescents eligible for driving license: \u003cstrong\u003e11%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Parents (early 40s) with school-going children: \u003cstrong\u003e11%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWell- off complex household structures with diverse and overlapping mobility needs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMid-life and multigenerational households\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Couples (late 40s) with adolescent children: \u003cstrong\u003e38%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Multigenerational families: \u003cstrong\u003e38%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Young couples without children: \u003cstrong\u003e10%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransitional households balancing adolescent and intergenerational mobility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredominantly ageing households\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Elderly living with young adults (20s): \u003cstrong\u003e31%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Elderly couples / widowed individuals living alone: \u003cstrong\u003e22%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Multigenerational families: \u003cstrong\u003e13%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Young couples without children: \u003cstrong\u003e9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgeing households with varying degrees of intergenerational co-residence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElderly\u0026ndash;young adult co-residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Older adults living with at least one young adult: \u003cstrong\u003e54%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Young couples with children under age 10 years: \u003cstrong\u003e31%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026bull; Elderly couples living alone: \u003cstrong\u003e15%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed-age households combining ageing and early family-stage needs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e suggests that households with very different demographic compositions are grouped within the same clusters, indicating that the need for a personal chauffeur is not driven by household type alone. Instead, households at different life stages\u0026mdash;elderly living alone, multigenerational families, and mid-life households with children and others can share similar underlying motivations for relying on a personal chauffeur. This implies that comparable mobility constraints, supervised travel needs, safety and comfort needs, risk perceptions or their attachment to a familiar norm may generate convergent preferences for personal chauffeur-driven automobility across otherwise dissimilar household structures. This diversity suggests that the choice to employ a personal chauffeur is not confined to a specific age group or household typology. Instead, it varies across households with distinct compositions and life stages, each attaching different \u003cem\u003emeanings\u003c/em\u003e and significance to chauffeur-driven automobility. These meanings are closely embedded in households lived realities and shaped by the social environments within which they are living.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e5.5 Overrepresentation analysis\u003c/h2\u003e\n \u003cp\u003eWhile Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e provides a summary of household characteristics distributed across each cluster. This gives a sense of different characteristics based on age group, income, number of earners, car ownership etc overlapping across clusters, making it difficult to identify clearly defining traits. To address this limitation and identify key differentiating socioeconomic characteristics across clusters, a hypergeometric p-value (Bardhan et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) was conducted. As a first step, chi-square tests were performed to examine whether household socioeconomic characteristics differed significantly across clusters. Only those characteristics that showed statistically significant differences in the chi-square analysis are reported in the paper as key socioeconomic differentiators. After this, hypergeometric p-values (Drǎghici et al., \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e) were calculated for these selected socioeconomic characteristics within each household cluster obtained from chi-square test. This helps us estimate statistically the probability of that socioeconomic characteristic occurring by chance in the cluster. In addition, under-representation p-values were also computed to show certain characteristics had a lower probability of occurring in a cluster than would be expected by chance. For clarity, only statistically significant over- or under-represented characteristics are presented, and non-significant results are left blank in the\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e \u003cbr\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eChi-square test results that indicate differences in the household clusters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHousehold Characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChi-square X\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCramer\u0026rsquo;s V value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePresence of households with school-going children\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10.002\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with female elderly above 55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with elderly above 55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual household income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.898*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold with self-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with Salaried employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with more than 20 years of car ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrice of car owned by the household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.31*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than one car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with female driving license holders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWork trip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOverrepresentation analysis for household clusters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eHousehold clusters.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHousehold Characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA2\u0026prime;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA2\u0026Prime;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with school-going children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElderly above 55 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual income: Medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual income: High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual income: Very High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOccupation type: Salaried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOccupation type: Self-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDriving license female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo Car ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of car ownership: More than two\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOwnership duration: More than 20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCar price: Above 25 lakhs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWork trips by car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eRed colour cells indicates over-represented p values.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eBlue colour indicates under-represented p values.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTwo p-values were calculated for each characteristic: one for over-representation and one for under-representation. If the p-value in either column was statistically significant, the characteristic was labelled accordingly. Only one of the two columns can show a significant p-value for a given characteristic. Thus, characteristics marked as over-represented correspond to cases where the over-representation p-value was significant. Empty cells indicate that the p-value was not significant in either column hence their level of over-under representation could not be determined. It happens because the samples were less in that cluster.\u003c/p\u003e\n \u003cp\u003eWhen we cross-check the household composition within Cluster A1, it becomes evident why elderly household members above the age of 55 are significantly over-represented in this cluster (p\u0026thinsp;=\u0026thinsp;0.007). Nearly 47% of households in Cluster A1 consist of elderly individuals living alone. This dominant household composition in the cluster A1 also explains why work-related trips are significantly under-represented (p\u0026thinsp;=\u0026thinsp;0.002), indicating a low likelihood of such travel behaviour within this cluster.\u003c/p\u003e\n \u003cp\u003eIn Cluster A2\u0026prime;, households with school-going children are significantly over-represented (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.032\u003c/em\u003e). This aligns with the cluster\u0026rsquo;s demographic composition, with 38% comprising parents in their forties with school-age children and an additional 29% being multigenerational households also supporting school-going dependents. These households are also characterized by high annual incomes (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/em\u003e), ownership of multiple vehicles (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.016\u003c/em\u003e), and regular work-related travel (p\u0026thinsp;=\u0026thinsp;0.046). The evidence suggests that households in Cluster A2 are affluent, family-oriented units that rely heavily on chauffeur-driven vehicles to coordinate complex daily schedules involving both school drop-offs and professional commitments.\u003c/p\u003e\n \u003cp\u003eCluster A2\u0026Prime; is distinguished by a significant concentration of high-end vehicle ownership, particularly luxury cars valued above INR 25 lakhs (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.028\u003c/em\u003e). Households in this cluster are predominantly multigenerational (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/em\u003e), with economically active members spanning multiple generations. These vehicles are frequently used for work-related travel (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/em\u003e), reflecting a household context in which affluence and intergenerational economic participation intersect. Together, these characteristics suggest a lifestyle configuration that supports sustained reliance on professional chauffeurs as part of everyday mobility practices.\u003c/p\u003e\n \u003cp\u003eCluster B includes a diverse mix of households with both salaried (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.007\u003c/em\u003e) and self-employed individuals (p\u0026thinsp;=\u0026thinsp;0.025). This cluster is also marked by over-representation of work-related commutes (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.011\u003c/em\u003e) and a notable presence of female driving license holders (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.013\u003c/em\u003e). Despite the presence of licensed drivers, households here continue to opt for driver assistance, indicating the perceived value of convenience, time efficiency, or adherence to gendered driving norms.\u003c/p\u003e\n \u003cp\u003eCluster C is marked by a significant over-representation of two-car ownership (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/em\u003e) and a corresponding under-representation of long-term car ownership exceeding 20 years (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.0001\u003c/em\u003e). This indicates relatively recent and moderate levels of car accumulation within these households. Cluster C is largely characterised by older household structures: approximately 31% of households consist of elderly members living with young adults in their twenties, while 22% comprise elderly couples or widowed individuals living alone. Together, these characteristics suggest a cluster shaped by ageing household structures, stable salaried incomes, and comparatively recent engagement with private car ownership.\u003c/p\u003e\n \u003cp\u003eCluster D is demographically distinctive. It is characterized by the absence of elderly members above the age of 55, as indicated by under-representation. Instead, households in this cluster are more likely to include relatively younger elderly (under 55) or middle-aged adults. Additionally, long-term car ownership (over 20 years) is under-represented (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.04\u003c/em\u003e), suggesting these households are relatively new entrants into automobility. This profile reflects changing socioeconomic aspirations, potentially aligned with a generational shift in lifestyle expectations and mobility needs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e5.6 Household archetype\u003c/h2\u003e\n \u003cp\u003eEach household cluster displays a distinct socioeconomic profile that can be systematically linked to the motivation clusters derived from the stated reasons for employing a driver. By combining motivation clusters (based on interview-derived reasons) and connecting the household socioeconomic characteristics broadly understood through household clustering (AB, C, and D) and supplementing them with interview narratives (thick description), we develop household archetypes This process involves going back and forth with the interview transcripts and correlate with the reasons spoken by specific households and describing their real world setting so as to present a holistic understanding of \u0026ldquo;\u003cem\u003ewhy a car users chooses to rely on a personal chauffeur\u003c/em\u003e\u0026rdquo;. This triangulation approach integrates quantitative and qualitative evidence, allowing a deeper understanding of how lived realities, effect of what we see around in family, friends and neighbours doing in a similar situation, and looking for solutions to a mobility challenge from their surrounding practical constraints interact to shape the decision to adopt personal chauffeured automobility.\u003c/p\u003e\n \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n \u003ch2\u003e5.6.1 Archetype A: Physical and Functional Constraint motivated Households\u003c/h2\u003e\n \u003cp\u003eHouseholds in Clusters A1 and C predominantly exhibit constraint-driven reliance on driver assistance, consistent with the dynamics outlined in Reasons Cluster 1. (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). These archetypes are primarily characterised by households with a high concentration of elderly members, including retirees living alone, elderly couples, and older adults in later life stages. Age-related physical and cognitive changes\u0026mdash;such as reduced muscular strength, joint stiffness, declining visual acuity, and slower reaction times\u0026mdash;progressively limit the ability of these individuals to self-drive safely and confidently. As a result, personal driving becomes increasingly constrained, leading to reduced transport autonomy and greater dependence on external support for everyday mobility. In this context, the transition from driver to passenger is not a voluntary preference but a response to declining driving capability and heightened safety risks. Empirical evidence of higher accident risk per unit of exposure among older drivers further reinforces these constraints. Households at different stages of their life course exhibit varying mobility needs that are often difficult to meet without assistance. In this context, the employment of a personal chauffeur emerges as a strategic and adaptive response.\u003c/p\u003e\n \u003cp\u003e[I 13]: Driver of a 55+-year-old widow working as a school teacher living alone with her adolescent son and owner of a subcompact hatchback:\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eMadam\u0026rsquo;s elder son, who lives in another city, purchased the car specifically for her. However, due to her knee problems, she is unable to drive at this age. If, for any reason, I am unable to come to work, she has no choice but to take leave from school and cancel any plans to go out. She relies on me entirely for all outdoor activities, including errands like shopping. Without my assistance, she is truly helpless.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e[I 6]: An elderly widower living alone and owner of a compact hatchback priced at 3000 USD describes:\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAt 57, I bought an Alto and learned to drive. Although I used the car for my daily commute, I wasn\u0026rsquo;t very confident at first. There was an incident where I almost hit someone on the road, which shook me and made me fearful of driving alone. To help with this, I hired a driver who assisted me while I practised. However, as I grow older, I find it harder to drive confidently, so I prefer to rely on a driver for safety and convenience.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eGendered differences in driving histories and mobility practices emerge as a significant factor shaping dependence on chauffeur-driven mobility within this archetype. Many women in this cohort never acquired driving skills due to prevailing gender norms and generational expectations that limited female participation in driving. As a result, dependence on external driving assistance becomes particularly pronounced in later life. In contrast, elderly men in this group often continue to self-drive into older age but gradually reduce or discontinue driving due to declining physical capacity, diminished confidence, or perceived safety risks. This transition is commonly marked by a shift toward trip-specific, part-time, or seasonal driver arrangements before moving to more consistent driver assistance. Notably, households with elderly female members were more likely to retain full-time drivers.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e[I 30]: A 30-year-old describes the role of a driver in his relative\u0026rsquo;s house who has a paralyzed individual in the leg and has pertinent walking issues\u003c/em\u003e:\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThis is the greatest advantage of having a driver, especially for him. The driver not only drops him off at his office but also supports him while walking, helps him climb the stairs, and carries his belongings. Upon reaching his office cabin, the driver assists him in sitting down comfortably in his chair and stays nearby until he completes his work. Additionally, he helps him use his mobility aids whenever needed. Without a driver, his daily life would come to a standstill.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eWithin this archetype, car ownership is commonly adopted as a convenience-oriented mobility strategy among elderly households, irrespective of gender or marital status, as it helps address both life-stage-specific and gendered mobility constraints. Importantly, the decision to purchase a car is closely coupled with reliance on driver assistance rather than independent driving. Acquiring driving skills later in life is perceived as difficult, and even among those who do learn, concerns related to accident risk and potential vehicle damage often discourage regular self-driving.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e5.6.2 Archetype B: Utility Maximization motivated Households\u003c/h2\u003e\n \u003cp\u003eThe theme of utility maximization corresponding to Reasons Cluster 2 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) is most strongly reflected in the socioeconomic profiles of households in Clusters A2\u0026prime; and B (See Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). These households typically include school-going children and are often headed by a male primary earner engaged in self-employment or business activities. Consistent with these characteristics, most households in this group own two or more cars: one vehicle is commonly dedicated to routine household activities such as school drop-offs and organised leisure trips, while another is reserved for work-related trips only. The profile of the households in this group reflects occupational routines marked by frequent inter-city travel also apart from rigorous intra-city work travels, irregular working hours, which creates the need to respond quickly to shifting schedules. In such contexts, navigating congested city traffic through self-driving can impose substantial stress and time pressures. Within this everyday reality, relying on a personal chauffeur emerges as a practical mobility arrangement. The presence of a personal chauffeur enables the productive use of travel time for work-related tasks, communication, or recuperation, thereby enhancing overall time efficiency. For them, employing a personal chauffeur is not merely a matter of convenience but a rational response to complex mobility demands, allowing households to optimise daily routines often filled with uncertanity.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e[I 3]: A 33-year-old businessman with three school-going children hired a driver at the time of purchase due to erratic work hours; describes\u003c/em\u003e:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;There is no fixed time when will I come and go \u0026hellip; If a businessman would start driving in this traffic, he will go crazy. In the presence of a driver, I am always free and relaxed.\u003c/em\u003e\u0026hellip;. \u003cem\u003eDriving myself would not only reduce my efficiency but also impact the time I could spend with my family or attending to business matters. This flexibility is crucial for me because my schedule is unpredictable and whose work demands immediate attention.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e[I 123]: A 40-year-old owner of a subcompact SUV dependent on a car for more than 20 years\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eMy business spans across a wide range of activities, and I\u0026rsquo;m often occupied with work, coordinating over the phone and dealing with unexpected events at work. With such a hectic schedule, it\u0026rsquo;s impossible for me to handle the chauffeuring duties of my children.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003e[I 45]: Mother who had come to pick up her 14-year-old daughter accompanied by a chauffeur\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u0026hellip;.. We wanted to ensure their safety and avoid using school vans as the school is pretty far. Her father is employed in another district, and I am not very good at driving\u0026hellip; Escalating safety issues outside schools, particularly due to the flocking of unruly individuals, prompted us to take this step.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eAnother important detail about the profile of this archetype is that, male members are often the primary license holders, but demanding work schedules leave little room to manage family logistics, particularly those involving children. Interviews indicate that many parents enroll their children in schools located farther from home, where safety and supervision during travel become key concerns. Drivers often report back to parents, acting as a communication bridge that supports both security and peace of mind. As a result, employing a full-time driver emerges as a practical arrangement\u0026mdash;not only to transport children but also to provide trusted oversight during trips. In this archetype, reliance on a personal chauffeur is therefore closely tied to the need to coordinate children\u0026rsquo;s mobility, ensure safety, and accommodate time-constrained household routines aligning with the household\u0026rsquo;s broader priorities and lifestyle needs.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003e5.6.3 Archetype C: Habitually comfort-seeking Households\u003c/h2\u003e\n \u003cp\u003eThe habitually comfort-seeking households align closely with the profile of cluster D corresponding to the motivations most closely captured in Reasons Cluster 3. For many middle-aged and older household members\u0026mdash;particularly those approaching their mid-fifties\u0026mdash; the continued use of a personal chauffeur reflects a gradual shift away from self-driving rather than a sudden need. Subtle age-related discomforts, such as fatigue or reduced ease in night driving, combine with a declining inclination to drive, reinforcing reliance on chauffeur-driven travel. Notably, several female respondents in this group held valid driving licences and were economically active, yet continued to prefer being chauffeured. This preference appears rooted in long-standing household norms where driving was historically delegated to male members within the household and is now seamlessly transferred to a personal chauffeur. Overall, the mobility profile is shaped by habit and lifestyle continuity, where employing a personal chauffeur sustains comfort and convenience in everyday travel rather than responding to immediate mobility constraints.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e[I 33]: A female doctor and a mother of two teenage daughters who will with one elderly above 70 years\u003c/em\u003e:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;My husband and I both come from a family where our parents kept drivers for their personal cars. Both our parents were salaried workers in stable jobs, and it was common for them to have drivers, especially since they worked in government positions. So, the habit of being driven is old\u0026hellip;. At present, we have a driver to drive us to our clinics and hospital (workplace). It gives us a moment to rest when there is a driver. In fact, we\u0026rsquo;re even considering employing another driver because of the timing clashes between our schedules.\u0026hellip;.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eInterviews revealed that the use of chauffeur-driven cars was common among elderly male respondents who previously held high-ranking government positions. In these cases, access to a driver was part of formal job entitlements, symbolising occupational status and authority. Over time, this institutional practice shaped personal mobility preferences, with many choosing to retain private drivers post-retirement, supported by household income. This pattern extended intergenerationally. Children raised in such households internalised the idea that self-driving was unnecessary even when licensed. For these households, employing a driver was not only a matter of convenience but a continuation of established norms associated with the prestige of a salaried profession. It underscores the role of occupational history and cultural norms in shaping driver dependency. For this archetype, employing a driver symbolized status, privilege, and professional success.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\n \u003ch2\u003e5.6.4 Archetype D: Habitually Assisted Households\u003c/h2\u003e\n \u003cp\u003eThe habitually assisted archetype aligns closely with the profile of households in Cluster A2\u0026Prime;, corresponding to the motivations most closest captured in Reasons Cluster 3 mostly characterized by multigenerational households. They typically include two or more economically active generations\u0026mdash;often middle-aged siblings and their families\u0026mdash;many of whom are self-employed or operate high-end businesses. With stable incomes and flexible work patterns, maintaining multiple vehicles and drivers becomes a practical way to manage the diverse and overlapping schedules of household members. Mobility demands in such households are inherently complex as different age groups access distinct destinations, such as schools, workplaces, and social or commercial venues, often requiring separate travel routes and timings. Drivers are employed not only to manage this logistical complexity but also to ensure that all household members enjoy flexibility, punctuality, and comfort. In these contexts, the driver functions as a mobility facilitator, enabling each member to maintain independence without the need for self-driving. The need for constant assistance is often a trait of elite households, passed down through generational habits. Observing family members using personal chauffeurs from a young age, household members tend to normalize and continue this practice as an extended form of household help. Since households in this archetype are typically multigenerational car users and personal chauffeur users, prolonged relationships with drivers allow them to engage in broader household tasks, integrating the chauffeur\u0026rsquo;s role into daily life. While this personalized assistance may seem minor, it reflects deeply ingrained habits shaped by parental practices and becomes a defining feature of their lifestyle. Chauffeurs insulate household members from the inconveniences and risks of direct car use, allowing travel to be limited to high-value activities such as professional commitments, curated leisure, or wellness routines.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e[Interview-68]: A 36-year-old male from a joint family of 15 members who has been using a car for more than 25 years and currently owns more than three luxurious cars.\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eWe are a joint family of 15 members, spanning three generations. We\u0026rsquo;ve been using driver assistance for more than 25 years now. It\u0026rsquo;s become a normal part of our lifestyle\u0026hellip;.. Honestly, no one really likes to drive\u0026hellip;. It\u0026rsquo;s not that we can\u0026rsquo;t\u0026mdash;most of us know how to drive, including women, but it\u0026rsquo;s just something we\u0026rsquo;ve gotten used to avoiding. The idea of self-driving feels unnecessary\u0026hellip;. Also, we own different cars to meet everyone\u0026rsquo;s needs. These cars require careful driving because they\u0026rsquo;re high-performance vehicles, and improper handling can reduce their value\u0026hellip;.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eInterview narratives repeatedly point to a shared theme within this archetype: employing a driver is not only a practical arrangement but also a subtle display of status. Households often described this practice as something long normalized within their close social and familial circles, where being constantly assisted becomes part of everyday life. Although not always expressed explicitly as a motivation, this lifelong exposure appears to normalize the practice and shape expectations around mobility. As a result, employing a driver is perceived as both a practical arrangement and a subtle marker of status. Over time, constant assistance becomes embedded in everyday life, reinforcing habits that reflect privilege, optimized time use, and continuity with long-standing social norms.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"6. Discussion and Conclusion","content":"\u003cp\u003eThe present study examined for the first time \u003cem\u003ewhy car users choose personal chauffeur automobility\u003c/em\u003e, using Patna, India, a case study. Personal chauffeur use is very common in the global south, including India and despite rising car ownership, there is not one single research on it. Our study is based on 126 semi-structured interviews compared to prior qualitative transport studies published in reputed journals that typically relied on interview sample sizes, ranging from 9 to 60 interviews ((Jason) Cao et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Burnett \u0026amp; Lucas, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hoffmann et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kopnina, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Levine, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lucas et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Merfeld et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mowri \u0026amp; Bailey, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; N\u0026aelig;ss, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Paulson, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Roy et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sherwin et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sigurdardottir et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tennant \u0026amp; Stilgoe, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our study adopts a mixed-methods approach, combining quantitative and qualitative data collection and analysis to gain deeper insights into personal chauffeured automobility. The research aimed to identify the underlying motivations for employing a personal chauffeur using open-ended responses from purposively selected car users who are users of a personal chauffeur. This study provides the first empirical evidence systematically documenting the range of reasons behind the need of personal chauffeurs, thereby addressing a largely implicit and understudied mobility practice in Global South.\u003c/p\u003e \u003cp\u003eFirstly, our semi-structured interviews identified twenty-eight distinct reasons underlying dependence on personal chauffeurs. Based on the cluster analysis, four broad motivational themes underpin the choice of personal chauffeur-driven automobility: (i) physical or functional constraint\u0026ndash;related reasons, (ii) utility maximization\u0026ndash;related reasons, (iii) habitual comfort\u0026ndash;related reasons, and (iv) habitual assistance\u0026ndash;related reasons.\u003c/p\u003e \u003cp\u003eSecondly, our findings show that the choice of personal chauffeured automobility is shaped by a combination of socioeconomic factors. The study examined household characteristics, including age distribution, presence of children and elderly members, number of economically active members\u0026mdash;particularly those above 55 years\u0026mdash;number of licensed drivers, occupation types, work- and school-related car trips, vehicle ownership, and types of cars owned and any others listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Hypergeometric p-value analysis identifies characteristics that are statistically over-represented in clusters associated with chauffeur use. Households with school-going children and elderly members are more strongly linked to chauffeur use, reflecting needs for escorting and supervised travel due to safety and comfort concerns. These households prioritise reliability, flexibility, and reduced effort in daily mobility. Occupational status is also important, as salaried and self-employed households, reflecting stable incomes, enable the affordability of personal chauffeurs.\u003c/p\u003e \u003cp\u003eRather than indicating differences between occupational groups, the over-representation of both salaried and self-employed households suggests a shared preference for personal chauffeurs among economically secure groups. This convergence signals a normative mobility practice rather than occupation-specific behaviour. Here, employing a chauffeur is driven not only by affordability but also by social influence, where family, peers, and professional networks normalise chauffeur-driven travel as efficient and appropriate. Continued exposure to such environments reinforces this preference, turning it into a socially learned and validated choice. Over time, repeated reinforcement within close networks embeds chauffeur-driven automobility into everyday routines, making it less a deliberate decision and more an unquestioned mobility norm.\u003c/p\u003e \u003cp\u003eIn addition, long durations of car ownership (exceeding 20 years) and ownership of high-value cars (above ₹25 lakhs) indicate well-established car-oriented lifestyles. Prior research links expensive car ownership with car pride (Moody \u0026amp; Zhao, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao \u0026amp; Zhao, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the same vein, being driven in such vehicles can also be understood as part of pride-related experience. Multi-car households and frequent car-based work trips further indicates a strong dependency on private cars, where delegating driving responsibilities emerges as a functional need. Managing multiple vehicles entails ongoing demands\u0026mdash;regular use, maintenance, timely repairs, and safeguarding high-value assets from unforeseen costs. In households with intensive work-related travel, these pressures intensify, making a personal chauffeur an effective mechanism for ensuring vehicle upkeep, reliability, and continuity of daily mobility.\u003c/p\u003e \u003cp\u003eThus, we observe that specific socioeconomic characteristics are systematically associated with the choice of personal chauffeured automobility. Thirdly, in this study, qualitative narratives from respondents were also used to contextualise the choice of personal chauffeured automobility. Beyond socioeconomic characteristics, the analysis reveals that situational and behavioural circumstances also play a critical role in shaping this mobility choice. These circumstances were categorized into four household archetypes: (A) Physical and Functional Constraint\u0026ndash;led households, where health, age, or physical limitations necessitate assisted travel; (B) Utility Maximization\u0026ndash;driven households, which adopt chauffeurs to optimize time use, reduce cognitive effort, and manage complex travel schedules; (C) Habitually Comfort-Seeking households, for whom convenience and travel comfort have become routine expectations; and (D) Habitually Assisted households, where long-standing trust on personal chauffeurs has normalized chauffeur-driven car use.\u003c/p\u003e \u003cp\u003eTogether, these archetypes demonstrate that personal chauffeured automobility is triggered not only by socioeconomic factors but also by life stage challenges, lived circumstances, habitual practices, lifestyle preferences and familiar norms as seen in their previous generation and social circle. Each household archetype has a unique way of living, shaped by their participation in daily activities, which clearly indicates \u003cem\u003ewhy car users choose personal chauffeur automobility.\u003c/em\u003e\u003c/p\u003e"},{"header":"7. Benefit of the study and future scope of work","content":"\u003cp\u003eCar ownership in India alone is expected to rise by an additional 262\u0026nbsp;million vehicles (Singh et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) amplifying existing challenges of congestion, and infrastructure (Verma, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Alongside this surge, the prevalence of personal chauffeured automobility is also likely to grow. Capturing it is essential\u0026mdash;not only to precisely explain how cars are used in India, but to anticipate how mobility behaviours may evolve in the future. Given the behavioural parallels between personal chauffeurs (PC) and personal autonomous vehicles (PAVs), analysing PC car use can significantly refine studies on implication of PAV especially preference towards PAV. (Wadud \u0026amp; Chintakayala, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) estimated preference towards PAV based on the \u003cem\u003evalue\u003c/em\u003e of convenience using a general population sample. More studies using PC car user\u0026rsquo;s \u003cem\u003evalue\u003c/em\u003e of travel time and experience of comfort can add to the findings. Previous studies on cross cultural difference in the acceptance of AV (Edelmann et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) can include the perception of personal chauffeur driven car users from India as current mobility practice is uncommon in global north. In order to bring improvement in those studies\u0026rsquo; mobility choice of personal chauffeured car use needs more in-depth investigation. Studies estimating the AV travel demand such as (Dai et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) can refine the results by measuring the vehicle kilometre travelled of personal chauffeured car which closely reflects a delegated driving context similar to PAVs. Future work could examine the relationship between vehicle kilometre travelled and emission rates among personal chauffeur-driven users and self-driving car users, and further compare these patterns with those observed in developed countries. Such comparative analysis would help identify which user groups contribute most significantly to emissions and could support the design of evidence-based policies, including the categorical application of the polluter-pays principle. Studies which evaluate cost of PAV to understand the early adoption (Wadud, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) may consider adding the cost of keeping a personal chauffeur in the Indian context or other similar global south contexts to explain the transition better. (Wang et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) studied that attitude toward PAV and SAV. Similar analysis can compare personal chauffeured car users with ride-sharing users by linking their current motivations to future intentions. Such a comparison would offer deeper behavioural insights, helping anticipate adoption patterns and refine mobility models beyond simplified user classifications. Examining personal chauffeur\u0026ndash;driven car use, and projecting its prevalence through scenario-based simulations, can reveal how equivalent PAV penetration levels\u0026mdash;given their behavioural analogy\u0026mdash;may impact road space and congestion in cities. This study also has strong implications for evidence-based policy. Interview findings indicate that users of personal chauffeur-driven cars often avoid paying parking charges, as chauffeurs typically wait in idle lanes or on nearby streets instead of using high-priced off-street or multi-level parking facilities. As parking fees are intended to increase the cost of car use and act as a deterrent, such practices effectively undermine the policy objective. Despite consuming higher vehicle kilometres travelled, personal chauffeur-driven car use frequently escapes these cost signals. Recognising and explicitly addressing chauffeur-driven use within parking and pricing policies could therefore help restore the effectiveness of parking charges as a demand-management tool.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eThere is no one-to-one correspondence between Reasons Clusters (1\u0026ndash;4) and household clusters (A1, A2\u0026prime;, A2\u0026Prime;, B, C, D). Household mobility behaviour is inherently complex and shaped by overlapping motivations. Attempting rigid alignment risks oversimplifying this reality. Instead, the analysis identifies the most prominent and relatable motivations through the combined use of household archetypes and reason clusters. These represent the closest approximation of households\u0026rsquo; lived circumstances, challenges, constraints, and aspirations, acknowledging that multiple influences coexist and vary in intensity across contexts.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Clusters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMost Associated Reasons Cluster\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClusters A1 and C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReasons Cluster 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClusters A2\u0026prime; and B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReasons Cluster 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReasons Cluster 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster A2\u0026Prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReasons Cluster 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe formula for calculating the over-represented p-value is given below:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\left(overrepresented\\right)pvalue=1-\\sum_{i=0}^{x-1}\\frac{\\left(\\begin{array}{c}M\\\\i\\end{array}\\right)\\left(\\begin{array}{c}N-M\\\\n-i\\end{array}\\right)}{\\left(\\begin{array}{c}N\\\\n\\end{array}\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\left(underrepresented\\right)pvalue=\\sum_{i=0}^{x}\\frac{\\left(\\begin{array}{c}M\\\\i\\end{array}\\right)\\left(\\begin{array}{c}N-M\\\\n-i\\end{array}\\right)}{\\left(\\begin{array}{c}N\\\\n\\end{array}\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e\u0026lsquo;\u003cem\u003eN\u0026rsquo;\u003c/em\u003e is the total number of samples collected for the interview (N\u0026thinsp;=\u0026thinsp;126). \u003cem\u003e\u0026lsquo;M\u0026rsquo;\u003c/em\u003e is the total number of successful samples found in different household characteristics in the total interviewed data (e.g. in a household with school-going children, M\u0026thinsp;=\u0026thinsp;64, Refer Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u0026lsquo;\u003cem\u003en\u0026rsquo;\u003c/em\u003e is the total number of samples in one cluster (e.g., n \u003csub\u003e\u003cem\u003ecluster A2\u0026prime;\u003c/em\u003e\u003c/sub\u003e = 21, Refer Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e heading). \u003cem\u003e\u0026lsquo;x\u0026rsquo;\u003c/em\u003e is the number of successful sample of households with school-going children in the cluster of interest, i.e., Cluster A2\u0026prime; (x\u0026thinsp;=\u0026thinsp;15).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePersonal chauffeurs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAutonomous vehicles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrivate autonomous vehicles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis study involved interviews with car owners. Informed consent was obtained from all individual participants prior to their participation. The study was reviewed and approved by my Doctoral Scrutiny Committee (DSC) at the Department of Architecture and Regional Planning, Indian Institute of Technology Kharagpur, India.\u003c/p\u003e\u003cp\u003eThe authors declare that there are no conflicts of interest. This research received no external funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJason) Cao X, N\u0026aelig;ss P, Wolday F (2019) Examining the effects of the built environment on auto ownership in two Norwegian urban regions. 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Transp Res Part D: Transp Environ 72(May):261\u0026ndash;278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.trd.2019.05.008\u003c/span\u003e\u003cspan address=\"10.1016/j.trd.2019.05.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Indian Institute of Technology Kharagpur","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Personal chauffeur, Mobility behaviour, Cluster analysis, Household archetypes, Qualitative, Global South","lastPublishedDoi":"10.21203/rs.3.rs-9002386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9002386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePersonal chauffeured automobility is a widespread yet under-researched practice, particularly in cities of the Global South. This study presents the first systematic investigation of personal chauffeured car use and the motivations that sustain it as an everyday mobility practice. Based on semi-structured, in-depth interviews with personal chauffeured car users, the study integrates qualitative thick description with quantitative analysis to explain the need to choose personal chauffeur-driven cars. In total, twenty-eight distinct reasons emerged from open ended responses. Subsequent cluster analysis of these reasons reveals four broad motivational themes shaping personal chauffeured automobility: (i) physical or functional constraints, (ii) utility maximization, (iii) habitual comfort-related motivations, and (iv) habitually assisted mobility practices. These motivations cut across diverse household types, indicating that structurally different households may share similar underlying motivation for relying on chauffeur-driven travel. The analysis reveals that personal chauffeured automobility is strongly shaped by socioeconomic characteristics, including the presence of elderly household members aged above fifty-five years, households with school-going children, very high annual incomes, salaried and self-employed occupations, multiple car ownership, long car ownership duration exceeding 20 years, ownership of high-value cars, and high work-related car trips. Household archetypes developed from these motivational clusters further reveal how life-stage challenges, mobility needs, lived circumstances, habitual practices, lifestyle preferences, and socially embedded norms\u0026mdash;shaped by prior generational experiences and social circles\u0026mdash;are intertwined with everyday mobility decisions.\u003c/p\u003e","manuscriptTitle":"A Household Level Investigation into Personal Chauffeured Automobility","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 21:42:56","doi":"10.21203/rs.3.rs-9002386/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"540b94fb-bcd9-4790-a5c3-c74f590b2b44","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63723734,"name":"Urban Studies"}],"tags":[],"updatedAt":"2026-03-06T21:42:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-06 21:42:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9002386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9002386","identity":"rs-9002386","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}