Beyond the Typical Day: Evaluating Multi-Day Travel Behaviour Through a Core–Satellite Survey Approach in the Greater Toronto and Hamilton Area

Beyond the Typical Day: Evaluating Multi-Day Travel Behaviour Through a Core–Satellite Survey Approach in the Greater Toronto and Hamilton Area | 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 Beyond the Typical Day: Evaluating Multi-Day Travel Behaviour Through a Core–Satellite Survey Approach in the Greater Toronto and Hamilton Area Melvyn Li, Sumaiya Afrose Suma, Felita Ong, Kaili Wang, Khandker Nurul Habib This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7273201/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This study evaluates the need for extended-duration travel surveys and builds the case for a core-satellite survey design in capturing multi-day travel behaviour. Leveraging the 2022 Transportation Tomorrow Survey (TTS) as the core, two satellite surveys were conducted in 2023 and 2025 in the Greater Toronto and Hamilton Area (GTHA), Canada, using GPS-based data collection methods. These satellite surveys targeted multi-day observations to assess how much additional behavioural insight can be gained beyond a typical one-day survey. This paper details the survey design and implementation of the 2025 cycle and compares travel patterns observed across the core and satellite datasets. To guide efficient multi-day survey design, Shannon’s entropy is used to quantify variability in individual travel behaviour across varying observation lengths and sample sizes. The findings reveal that while increasing both the number of observation days and the sample size enhances the informational value of the data, the marginal gains diminish beyond a certain point. Most of the day-to-day variability in travel is captured within the first five weekdays, suggesting that a five-day observation window and a sample size of at least 600 individuals can provide a sufficient balance between behavioural coverage and statistical robustness. These results support the use of targeted satellite surveys to complement large-scale, typical-day surveys in a cost-effective and methodologically sound manner. household travel survey GPS survey Smartphone survey Survey observation length Survey sample size Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1. Introduction & background A key area of ongoing advancement involves the development of travel demand forecasting models capable of capturing multi-day activity-travel patterns (Arentze & Timmermans, 2009 ; Kuhnimhof & Gringmuth, 2009 ; Auld & Mohammadian, 2012 ; Märki et al., 2014 ; Dianat et al., 2017 ; Pougala et al., 2023 ; Moeckel et al., 2024 ). These models account for interdependencies across consecutive days and address the limitations of single-day approaches in representing the cumulative effects of travel-related choices. Understanding the dynamics of travel behaviour requires high-quality data and robust models that capture the temporal complexity of individuals’ activity-travel decisions. A critical component supporting the development of such models is the multi-day travel diary, which provides sequential records of individuals’ trips and activities collected over a defined timeframe. Various methods exist for collecting multi-day diaries, with global positioning system (GPS) technology emerging as one of the most reliable approaches (Shen & Stopher, 2007). In recent years, the widespread adoption of GPS-enabled smartphones has significantly enhanced the feasibility of large-scale GPS data collection (Patterson & Fitzsimmons, 2016; Patterson et al., 2019). This technology enables the passive recording of travel diaries as respondents carry out their daily routines (Murakami & Wagner, 1999 ; Chen et al., 2010 ; Kelly et al., 2013 ; Shen & Stopher, 2014 ; Li et al., 2024 ; Li et al., 2025 ). Compared to traditional diary methods, GPS-based data offer more granular detail and can capture activity-travel patterns over extended periods (Allström et al., 2017 ; Molloy et al., 2022 ). Moreover, respondent burden is substantially reduced as trip attributes such as origin, destination, and timing are automatically logged rather than relying on manual recall (Itsubo & Hato, 2006 ; Murakami & Wagner, 1999 ). This study extends prior work by leveraging a multi-cycle GPS-based survey under the core-satellite paradigm (Goulias et al., 2013 ; Habib et al., 2018 ). Under this paradigm, large-scale household travel surveys serve as the core dataset, while satellite surveys are deployed for specialized purposes to enhance and complement the core. In this study, the 2022 Transportation Tomorrow Survey (TTS), a regional household travel survey conducted in the Greater Toronto and Hamilton Area (GTHA), sampled approximately 5% of the population and is the core dataset (Data Management Group, 2025). The first satellite cycle implemented in 2023 employed a hybrid approach. Respondents’ multi-day travel behaviour was captured using Google Location History. Then, they submitted their location history files via an online survey platform (Li et al., 2024 ). This hybrid approach successfully collected seven-day travel diaries for 956 individuals across the GTHA. The detailed survey design and results from the 2023 cycle are presented in Li et al. ( 2024 ). This study first reports on the survey design, implementation, and preliminary findings from the latest 2025 cycle. The 2025 cycle employs a dedicated smartphone-based travel survey application to collect multi-day travel data. Travel behaviours observed in the 2025 cycle are compared with those revealed in the 2022 core and the 2023 satellite cycle, enabling a comprehensive assessment across datasets. Unlike the first satellite cycle, which specifically targeted a seven-day observation window, the 2025 cycle allows respondents to participate for extended periods, thereby capturing multi-week activity-travel patterns. This extended observation provides a unique opportunity to address questions concerning the optimal length of data collection required to capture the dynamics of activity-travel behaviours adequately. Previous literature has debated the length of the observation period that can provide sufficient information on the dynamics of activity-travel behaviours. Hanson and Huff ( 1988 ) acknowledged that collecting data over multiple weeks would be necessary for a comprehensive observation of all travel patterns. However, multi-weeklong data collection was rare in practice due to its high operational cost. From the literature, only four surveys collected activity-travel behaviours for more than one week. The Cedar Rapids Travel Diary was compiled over 30 days in 1949 (Horton, 1968 ). The Uppsala survey was conducted over 35 days in 1971 (Huff & Hanson, 1986 ). The Mobidrive survey collected six-week travel diaries in Germany (Axhausen et al., 2002 ). Finally, the Thurgau survey also collected six-week travel diaries in Switzerland (Axhausen et al., 2006 ). Apart from the surveys above, most studies in the literature known to the authors collected multi-day dairies from two to seven days (Buliung et al., 2008 ). Using the Mobidrive survey, Schlich & Axhausen ( 2003 ) compared the distribution of the mean of the intrapersonal similarity indices and their standard deviations within one to six weeks. The similarity index measured the similarity between the number of trips made on different days. They found that the distribution of the mean intrapersonal similarity indices was similar for all time periods. Moreover, they found that the standard deviations of the mean intrapersonal similarity indices were similar after two weeks. Their results indicate that an observation period of one to two weeks was acceptable to capture day-to-day variability in travel behaviours. However, two weeks were preferred if conditions permitted. Schlich and Axhausen ( 2003 ) made a pioneering and significant contribution to the question of optimal observation length in travel behaviour studies. However, they also acknowledged certain limitations of their method. Firstly, the similarity index they used cannot account for all attributes of a trip, which means trips may be classified as identical even when they differ in important characteristics. More critically, their analysis compares days at an aggregate level rather than considering the sequence of activities. As a result, both order and the timing of activities did not influence the similarity measure. This study addresses the limitations identified above. It examines the uncertainty inherent in datasets with varying observation periods and sample sizes. The analysis focuses on key choice dimensions commonly explored in travel behaviour modelling, particularly in activity-based models. These dimensions include travel mode choice, activity purpose choice, location choice, and time allocation (Wang et al., 2024 ). To quantify the variability and predictability of individual travel behaviours, the study employs Shannon’s entropy from information theory (Shannon, 1948 ). With its solid theoretical foundation and widespread application across disciplines, Shannon entropy offers a rigorous and interpretable framework for measuring behavioural complexity and randomness in travel surveys. The paper is organized as follows. Section 2 presents the survey design and conduct. Section 3 presents descriptive analysis by comparing the 2025 cycle with the 2023 cycle and the core survey. Section 4 investigates the question of the desired survey length of a multi-day travel survey. Finally, Section 5 concludes the study by summarising key findings. 2. Survey Design This section presents the development of survey instruments used and the survey procedure & response behaviours in the 2025 cycle. 2.1 The smartphone application This study used a smartphone-based tool named TRAISI Move to collect a multi-day travel diary. The application was developed based on the open-source OpenPATH codes developed by the US National Renewable Energy Laboratory (NREL), which provides a robust platform for mobility tracking and survey data collection (Shankari et al., 2018 ). The OpenPATH family of travel survey tools have a proven record and have been used in many smartphone-based travel studies. Tabasi et al. ( 2024 ) used the OpenPATH code to develop an application called Fourstep to collect data in Sydney and Chicago. In a separate effort, Siripanich et al. ( 2024 ) leveraged a similar OpenPATH family smartphone application to examine the effectiveness of recruiting travel survey respondents via social media platforms. Their findings highlight social media recruitment as a cost-effective and efficient strategy for surveying the general population. The OpenPATH codes are free to be redistributed under the BSD 3-Clause license, which allows usage of the software on the condition that any redistributions of the code or compiled binaries must contain the original copyright notice. Additionally, it requires that derivatives of the software cannot be endorsed or promoted using the names of the original copyright holder or contributors (namely, the National Renewable Energy Laboratory). Therefore, the survey team named the application as TRAISI Move after the TRavel Activity Internet Survey Interface (TRAISI) web-based survey tools developed by the survey team (Li et al., 2024 ). TRAISI Move is available for both iOS and Android devices through the official Apple App Store and Google Play Store, respectively. Survey respondents can easily join the study by scanning a QR code or entering a special study code provided by the survey team. This allows respondents to easily log back into the study if they swap devices or have signed out for any reason. It prevents the need for any lengthy sign-up or account creation process. Figure 1 presents the main TRAISI Move launching screen with the QR code and manual code entry options. After installation and start tracking, the application can operate in the background, requiring minimal user interaction. TRAISI Move automatically identifies when a respondent begins a trip and records route traces. Trips are saved once the respondent has reached a destination, after which users can retrospectively annotate their trips by selecting travel modes and trip purposes. Figure 1 also contains a sample travel diary screen, showing trips that TRAISI Move has recorded for the respondent and user, labelled with travel mode and purpose for each trip. To accommodate varying respondent loads and ensure reliable data storage, TRAISI Move can utilize multiple backend servers. This distributes computational and storage demands more efficiently. The system is designed for scalability, allowing new servers to be deployed dynamically as needed. This study was deployed with a rolling server system. Each server was hosted on a cloud platform on local machines physically situated in Toronto. The specification of each server instance is eight vCPUS and 32GiB of memory. This configuration allows up to four hundred simultaneous users over around two weeks to utilize the application smoothly without interruption. The study was deployed on one instance initially. As more capacity was required, more server instances were deployed. In the end, three cases were required to complete the study. 2.2 Survey procedure & response behaviours The TRAISI Move smartphone application described in the previous section was used to collect the second satellite cycle. The survey was conducted in the Spring of 2025, from March to June. The study area of the second satellite is the Greater Toronto and Hamilton Area (GTHA), Canada, which is the same as the 2023 cycle and is a sub-area of the 2022 TTS. Figure 2 shows the two-phase procedure implemented to collect the 2025 cycle. Random samples of GTHA residents who participated in the 2022 TTS (the core regional household travel survey) were invited to join the first phase. The invitation email was sent using a dedicated university-authorized email address to enhance credibility. The invitation clearly stated the purpose of the study, and at least one week of tracking time is required. Upon successful completion of the two-phase study, a C $ 20 gift card compensation would be providedresearch. In the first phase, socioeconomic information was collected through a web-based survey, where respondents also provided their consent to install the TRAISI Move application, enabling tracking on their phones. Respondents were asked to carry their smartphones throughout their daily activities during the data collection period. Respondents who had been tracked for at least a full week were subsequently invited to participate in a second-phase web survey, which collected additional information on their work-from-home (WFH) arrangements and e-shopping behaviours for the same observed full week period. This approach aligns with the Mobility-Activity-Expenditure-Diary (MAED) framework proposed by Aschauer et al. ( 2018 ), which advocates for expanding traditional travel diaries to include non-travel activities to present a more comprehensive picture of household behaviour. A similar approach was adopted in the 2023 satellite cycle that collected WFH schedules and detailed online shopping behaviours (e.g., item types, expenditure, delivery timing, etc.) alongside seven-day travel diaries. For the initial invitation, 32,067 invitation emails were sent. As a result, 1,172 individuals completed the registration survey in the first phase. The response rate for the first phase was 3.65% of the total initial invitation sent (American Association for Public Opinion Research, 2023 ). Among the 1,172 individuals who started tracking, 999 successfully generated travel diaries for at least two days. During the tracking period, respondents’ tracking status was actively monitored. A total of 318 status check emails were sent when the survey team identified that the tracking application on a respondent’s smartphone had stopped recording data. Of these, 154 respondents replied to the email and completed the one-week tracking requirement. In total, those 999 respondents generated 64,021 trips, and 48,900 trips were fully labelled with travel mode and purpose. Finally, out of the 999 respondents, 872 individuals achieved the requirement by accumulating at least one week of travel diary. All of them were invited to participate in the second phase survey. As a result, 861 respondents completed the second phase survey. Figure 3 illustrates the distribution of the number of days with travel data captured and subsequently labelled by respondents. While a substantial number of respondents had 10 to 20 days of captured data, the number of labelled days consistently falls below the number of captured days across the distribution. On average, 13.7 days were captured, and 10.7 days were fully labelled. This gap reflects the respondent burden associated with manual validation, especially over extended periods. Figure 4 shows the study area of the 2025 cycle and the spatial distribution of households in the 2025 cycle across the Greater Toronto and Hamilton Area (GTHA). The dataset captures a geographically diverse sample, with respondents spread across core urban centers such as Toronto, Mississauga, Hamilton, and extending into suburban areas. 3. Comparison of descriptive statistics between survey cycles This section compares the socioeconomic characteristics and revealed activity-travel behaviour of samples from the 2025 and 2023 satellite cycles with those from the 2022 Transportation Tomorrow Survey (TTS), which serves as the core and benchmark dataset. 3.1 Socioeconomic attributes Table 1 shows the distribution of socioeconomic variables in the 2025 cycle compared to both the 2023 cycle and the 2022 TTS. The 2022 TTS was weighted to match the 2021 Canadian Census, so the socioeconomic characteristics revealed from the TTS can be treated as the true population characteristics (Data Management Group, 2024 ). Overall, the 2025 cycle matches the general GTHA population well, although there are a few notable differences. The satellite samples have more males than the 2022 TTS. Males comprise 61.7% and 57.2% of the samples in 2025 and 2023 cycles, respectively. The gender distribution in TTS is more balanced. In terms of age, the 2025 and 2023 cycles over-represent middle-aged groups (age between 30 to 64). Additionally, the household income distribution in the 2025 cycle also shows some differences when compared to the 2023 cycle and 2022 TTS. The 2025 cycle has a higher percentage of households earning over $ 200,000 (27.99%) compared to the 2023 cycle (18.90%) and the 2022 TTS (13.18%). The employment status distribution in the 2025 cycle aligned closely with the 2023 cycle. However, the 2022 TTS had a higher proportion of unemployed individuals (47.05%) compared to both the 2025 (23.98%) and 2023 (20.90%) cycles. This aligns with the age distribution, where 2022 TTS captured more unemployed younger and older populations. In contrast, the 2025 and 2023 cycles had a higher representation of the working population. Three-quarters of the 2025 cycle workers reported that they could work remotely, which also aligns with the 2023 cycle distribution. Overall, the 2025 and 2023 samples are more affluent than the TTS benchmark, which may reflect sample selection biases (e.g., willingness to participate in follow-up study) or the effects of survey methodology preferences (e.g., desire to join in smartphone-based research). Table 1 Key sample socioeconomic attributes in the 2025, 2023 satellite, and the 2022 core dataset 2025 Cycle 2023 Cycle 2022 TTS Attributes Gender (%) Male 61.65 57.22 48.86 Female 38.35 42.78 51.14 Age (%) 20–29 7.26 12.78 17.69 30–39 22.39 29.98 18.42 40–49 24.62 22.54 17.18 50–64 26.41 23.94 26.39 65+ 19.32 10.76 20.33 Household Size (%) 1 21.33 19.88 25.25 2 38.99 38.16 29.20 3 15.36 16.58 17.32 4 16.38 17.28 17.27 4+ 7.94 8.09 10.97 Household Income (%) Below $ 39,999 5.72 6.19 11.42 $ 40,000- $ 79,999 11.35 17.48 18.57 $ 80,000- $ 124,999 22.87 26.17 20.95 $ 125,000- $ 199,999 27.47 27.17 18.13 $ 200,000 and above 27.99 18.88 13.18 Declined to answer/don’t know 4.61 4.10 17.74 Employment Type (%) Employed full-time (≥ 30 hours per week) 67.49 69.07 44.10 Employed part-time (< 30 hours per week) 8.53 10.01 8.85 Not employed 23.98 20.92 47.05 Workplace Arrangement (%) Usual place of work 22.42 27.60 Worked at home & hybrid 75.00 69.00 No fixed workplace address 2.58 3.40 Region (%) Toronto 54.80 59.91 38.19 Durham 6.35 6.21 9.67 York 17.92 8.93 16.21 Peel 10.89 11.98 19.78 Halton 6.69 6.10 8.31 Hamilton 3.34 6.86 7.83 3.2 Activity-travel behaviour 3.2.1 Average trip rates This section compares weekday trip rates across three datasets: the 2025 cycle, the 2023 cycle, and the 2022 TTS. Since the TTS only collected one-day travel diaries for weekdays, the weekdays in the satellite surveys are compared against the TTS. In addition, weekend trip rates are compared between the 2025 and 2023 cycles. Figure 5 illustrates that the 2025 cycle consistently reports higher trip rates than both the 2023 cycle and the 2022 TTS. The TTS yields the lowest average trip rate, with only 2.1 trips per day, reflecting measurement errors such as proxy and recall bias inherent in self-reported survey data (Li et al., 2025 ). In contrast, the satellite-based surveys capture substantially higher trip rates: the 2025 cycle reports an average of 3.8 trips on weekdays and 4.0 on weekend days, while the 2023 cycle reports 3.2 and 3.3 trips, respectively. The differences in reported trip rates between the 2025 and 2023 cycles can be attributed to the underlying data collection methods. The 2025 cycle employed a dedicated smartphone application specifically designed for continuous tracking, whereas the 2023 cycle relied on passive data from Google Location History (GLH). The dedicated smartphone application in 2025 was more effective in capturing short-distance movements, which are often overlooked in GLH-derived data (Cools et al., 2021 ). This is evident in the distribution of walking trip distances reported in the next section. The 2025 cycle reports significantly more walking trips under 500 meters. 3.2.2 Trip distances and durations Figure 6 presents a comparison of trip distance distributions across survey cycles. As previously discussed, the 2025 cycle captured a significantly higher number of short-distance walking trips under 500 meters compared to the 2023 cycle. This reflects the sensitivity of the dedicated smartphone application used in the 2025 cycle to detect fine-grained non-motorized movements. In contrast, the distance distributions for motorized modes of travel, including automobile and transit, as well as for cycling, appear broadly consistent across the surveys. This consistency suggests that while all instruments were effective in capturing medium- to long-distance trips, the 2025 cycle provided more comprehensive coverage of short-distance active travel. These findings highlight the importance of selecting appropriate survey instruments when aiming to capture the full spectrum of travel behaviour, especially for active modes. Figure 7 presents a comparison of trip duration distributions across survey cycles. The 2022 TTS is not included because it only collected the trip origin and destination without the trip level-of-service information. The results show that the trip duration distribution between 2025 and 2023 cycles is similar for automobile, cycling, and walking. The most significant difference is the higher share of longer-duration transit trips captured in the 2025 cycle. Specifically, nearly 40% of local transit trips in 2025 fall within the 30-minute to 1-hour range, compared to less than 20% in the 2023 cycle. 3.2.3 Trip start time Figure 8 depicts the share of trip start times throughout the day for weekdays across three datasets. All three datasets display the expected bimodal morning and afternoon commuting peaks. However, a key distinction emerges between the core and satellite surveys: both satellites exhibit a distinct midday peak around noon, which is largely absent from the TTS. This mid-day peak likely reflects non-commute activities that are typically underreported in self-reported TTS. The sharper and more compressed morning and afternoon peaks in the TTS indicate a bias toward capturing only work-related trips. In contrast, the passively tracked data from the 2023 and especially the 2025 cycles provide a more comprehensive temporal profile of daily travel behaviour. In addition, Fig. 8 also compares the distribution of weekend trip start times between the 2025 and 2023 survey cycles. Unlike weekday travel patterns, weekend trips show a delayed and more uniform distribution, with trips gradually increasing and peaking around noon and then tapering off into the evening. Both cycles exhibit nearly identical temporal profiles, suggesting consistent weekend travel behaviour across years and data collection methods. 3.2.4 Activity (trip) purpose Figure 9 presents the distribution of weekday activity purposes across the 2025 and 2023 satellite cycles and the 2022 TTS. Notable differences emerge across the datasets. The 2022 TTS places substantial emphasis on work and school-related trips, which together account for over 30% of reported weekday activities. In contrast, the 2025 and 2023 satellite cycles, both based on passively collected data, captured a broader range of discretionary and non-mandatory activities, such as recreational travel, social visits, and dining out. Notably, recreational activities account for nearly 20% of weekday trips in the 2025 cycle, compared to under 10% in the 2023 cycle. At the same time, the share of shopping trips declines from 2023 to 2025. A similar shift in activity purposes is also evident at weekends. This pattern may suggest a behavioural shift toward more diverse and socially oriented activity participation. 3.2.5 Work-from-home& e-shopping Both 2025 and 2023 cycles collected individuals' work-from-home (WFH) and e-shopping behaviours. Figure 10 presents the overall trends for both online behaviours. For WFH, individuals were asked in the survey if they worked from their homes on each day of the week. For each weekday, the share of workers in the samples who worked from home is almost identical between 2025 and 2023. The largest share of WFH happened on Monday and Friday, and more workers chose to work on-site during the middle of the week. For e-shopping, individuals were asked to report the details of their online purchases during the same week period. Figure 10 also presents the average frequency of three categories of e-shopping activities: cooked meal delivery (CMD), grocery delivery, and other retail e-shopping. The general e-shopping trend for all categories of goods remains similar between the 2025 and 2023 cycles. Across both cycles, retail purchases excluding meals and groceries online (named as “other e-shopping” in Fig. 10 ) are by far the most frequent, occurring more than once per respondent each week in both datasets. The 2025 cycle shows a slightly lower frequency of this category compared to the 2023 cycle. At the same time, CMD and grocery delivery frequencies remain relatively low and stable across both cycles. Figure 11 offers a more granular comparison of cooked meal delivery (CMD) patterns across the 2023 and 2025 cycles, focusing on both the day of the week and time of delivery. The 2025 cycle shows an increase in weekday CMD activity, particularly on Wednesdays. While the specific day on which respondents ordered meals varied between cycles, the temporal distribution of deliveries remained largely consistent between cycles. In both cycles, CMD activity exhibits a pronounced evening peak, with approximately 50% of deliveries occurring after 7:00 PM, indicating that most CMDs were ordered during dinner hours. These results underscore the strong alignment between meal delivery demand and conventional dining routines. Consequently, modelling efforts and policy interventions such as congestion pricing or delivery fleet optimization should account for this peaked evening demand. For each grocery delivery, the survey recorded the delivery day, time of arrival at the respondents’ homes, and the associated waiting time. Figure 12 compares grocery delivery patterns across the 2023 and 2025 survey cycles. It is important to note that these patterns reflect the interaction between consumer demand and the operational strategies of logistics providers. Unlike CMDs, which typically require fulfillment within a short time window (often within an hour), grocery deliveries allow for greater flexibility in scheduling. As a result, logistics providers have more control over delivery timing, enabling them to optimize route planning and resource allocation. The observed trends in delivery timing and waiting periods thus offer insights into the evolving dynamics between consumer expectations and supply-side logistics performance. The comparison across the two cycles highlights how delivery operations may have adapted or improved between 2023 and 2025. The results reveal modest but meaningful shifts in grocery delivery scheduling patterns over time, likely reflecting evolving consumer ordering behaviour and adaptations in logistics provider operations. In the 2023 cycle, deliveries were more concentrated at the beginning and end of the week, with pronounced peaks on Monday and Saturday. In contrast, the 2025 cycle exhibits a more balanced distribution of deliveries across weekdays, particularly from Tuesday to Friday, suggesting a smoother operational spread. In terms of delivery timing, the comparison highlights a stronger concentration of grocery deliveries during standard daytime hours in 2025 relative to 2023. This shift may indicate a growing preference among consumers for predictable daytime delivery windows. Concerning waiting times, both cycles show that the majority of grocery deliveries occurred within 24 to 48 hours of order placement, reinforcing a prevailing norm of next-day or second-day service. These patterns reflect a stabilized expectation from consumers and a logistical equilibrium between 2023 and 2025. 4. Effects of observation length on information quality Determining the appropriate observation length in multi-day travel surveys is a critical issue in guiding survey design and practice. In the transportation literature, only two major multi-week datasets have been rigorously examined on this topic: the Uppsala Household Travel Survey (Hanson & Huff, 1988 ) and the Mobidrive survey (Schlich & Axhausen, 2003 ). More recent studies by Jara-Díaz and Rosales-Salas ( 2014 ) and Li et al. ( 2018 ) have also explored this issue; however, their analyses were constrained by datasets limited to a maximum of seven days, preventing them from examining multi-week effects. In this study, a subset of respondents from the 2025 survey cycle was observed over multiple weeks, offering a valuable opportunity to empirically assess the impact of observation length on travel behaviour metrics. The following section presents an analysis aimed at identifying the appropriate duration for GPS-based travel surveys to capture the uncertainty in travel patterns adequately. 4.1 Entropy measurement The analysis uses Shannon's entropy, a concept derived from information theory, to quantify uncertainty (or termed as the variability in transport studies) in individuals’ travel behaviour across multiple days (Shannon, 1948 ). This measure captures predictability, or conversely, the diversity of travel choices over a given observation period. For a multinomially distributed travel behaviour choice \(\:K\) observed for a period of \(\:D\) days for a sample with \(\:N\) individuals, the average entropy ( \(\:{H}_{NDK}\) ) is calculated as follows: $$\:{H}_{NDK}=\:\frac{1}{N}\sum\:_{n=1}^{N}\frac{1}{D}\sum\:_{d=1}^{D}(-\sum\:_{k=1}^{K}{p}_{ndk}\text{l}\text{o}\text{g}({p}_{ndk}\left)\right)$$ 1 where \(\:{p}_{kdn}\) is the probability of choosing alternative \(\:k\) among the choice-set \(\:K\) in the observation length \(\:D\) for individual \(\:n\) . The entropy measurement in Eq. 1 allows for the simultaneous evaluation of trade-offs between two analytical objectives. Firstly, the entropy examines the stability and temporal dynamics of activity-travel patterns when comparing shorter to longer observation periods. Secondly, the entropy assesses the capacity of the sample size to capture uncertainty in travel behaviours in the survey. The analysis in this study estimates sample-enumerated average entropy in the 2025 survey cycle using a bootstrapping approach. Entropy is calculated for various travel behaviour choices across different observation lengths: one weekday, two weekdays, three weekdays, five weekdays (representing one full work week), and ten weekdays (representing two full work weeks). For each observation length, a wide range of sample sizes is examined, beginning with 20 respondents and incrementally increasing up to 800, which is close to the full size of the 2025 cycle. The only exception is for the ten-weekday observation length, where the sample size is constrained by the limited number of respondents with two full weeks of observation. Bootstrapping is then applied to assess the variability of entropy estimates. For each trial, a random sample of respondents is drawn without replacement from the entire 2025 cycle. This process is repeated 100 times for each combination of observation length and sample size. In each iteration, the sample-enumerated average entropy is computed. According to the central limit theorem, the bootstrapping procedure yields a normal distribution of the mean entropy values. From this distribution, the mean and standard deviation of the average entropy are derived for each combination of observation length and sample size for a targeted choice behaviour. This allows for assessing the variability and robustness of the entropy measure across different targeted travel behaviour choices. 4.2 Entropy calculation results The study evaluates three key choices to travel demand modelling: travel mode choice, activity purpose choice, and location choice. The results are presented below. 4.2.1 Travel mode choice The uncertainty in travel mode choice across different observation lengths and sample sizes is evaluated by categorizing each trip into one of the following modes: driving, auto passenger, ride-hailing and taxi, transit, walking, biking, or other. This classification is consistent with the mode choice definitions used in the operational activity-based models (ABMs) of the Greater Toronto and Hamilton Area (GTHA) (Wang et al., 2024 ). Figure 13 illustrates the relationship between the average entropy of mode choice and sample size across various observation lengths. As expected, entropy increases with longer observation periods, reflecting the ability to capture a more diverse range of mode choice behaviours over time. Single-day observations exhibit the lowest entropy values, indicating limited variability in recorded mode choices. A sharp increase in entropy is observed when the observation period extends beyond a single day, suggesting improved representation of variability in this target behaviour. However, beyond five weekdays, the marginal gain in entropy diminishes, with entropy levels for ten weekdays close to those observed for five weekdays. In terms of sample size, the results indicate that entropy stabilizes rapidly as the number of respondents increases. After approximately 400 individuals, the standard deviation of entropy converges and shows little variation with additional sample size. 4.2.2 Activity type choice Next, the individual’s activity type choice is examined. Targeting activity type choice will measure the variability of activity participation over multiple observation lengths. The activity type choice considers the following alternatives in the choice set: work & school, errands, retail shopping, eating out & picking up food, facilitating others, other discretionary activities, and home-based activities. This classification is also consistent with the activity type definitions used in the operational ABMs (Wang et al., 2024 ). The activity type choices for the first episode (the first activity of each day) and all subsequent episodes are distinguished in the entropy calculation. The market shares they are different due to the scheduling nature. Figure 14 compares the market share difference. Work & study activities, dominate the first episode and home-based activities dominate the subsequent episodes. Figure 15 presents the entropy results for the first activity episode of each day. As a baseline, it is important to note that the entropy for one weekday is effectively zero, since each respondent contributes only a single first-episode observation, leaving no room for measuring intra-individual variability. Like the findings for mode choice, the results indicate that longer observation periods consistently yield higher entropy values, reflecting greater variability in the types of activities individuals engage in at the start of their day. In terms of observation stability, the standard deviation of entropy declines sharply with increasing sample size for all observation lengths (excluding the ten-weekday sample, which is limited by a smaller respondent pool of around 200). After approximately 400 respondents, the standard deviation plateaus, indicating consistent entropy estimates and diminishing returns from further expanding the sample. For subsequent episodes, the results follow a similar pattern to those observed for the first episode. However, the pattern in standard deviation differs. While the general trend holds the same (i.e., standard deviation declines as sample size increases), the absolute values of the standard deviations vary across observation lengths. Specifically, for a given sample size, the standard deviation is consistently lower for longer observation periods (i.e., five and ten weekdays) compared to shorter ones. 4.2.3 Location choice The final targeted behaviour examined is the location choice. A discrete approach is applied to analyze this decision-making process. For each trip, respondents can select from eight possible location choices, which are classified into three main categories: home, work & school, and discretionary activities. For home, work & and school trips, destinations are assumed to be fixed, reflecting the routine nature of these activities. In contrast, discretionary activities are characterized by the distance between the destination and the respondents’ home. This results in six distinct location alternatives for discretionary activities: 0–2 kilometers from home (50th percentile), 2 (inclusive) – 6 kilometers from home (70th percentile), 6 (inclusive) – 10 kilometers from home (80th percentile), 10 (inclusive) – 20 kilometers from home (90th percentile), 20 (inclusive) – 40 kilometers from home (98th percentile), More than 40 km from home The distance thresholds for each discretionary location choice are determined based on the empirical distance distribution observed in the 2025 cycles and the corresponding percentile cutoffs. Locations falling beyond the 98th percentile are treated as a standalone category to ensure that the analysis remains sensitive to infrequent long-distance discretionary trips. Figure 16 shows the relationship between the average entropy of location choice and sample size. Consistent with the patterns observed for other behavioural choices, longer observation periods consistently yield higher entropy values. Entropy increases sharply when extending from single-day to multi-day observations, but the incremental gain beyond five weekdays is minimal, suggesting diminishing returns with extended survey duration. However, the standard deviation analysis highlights the trade-off between observation length and sample size. Approximately 600 respondents in a five-day survey are required to achieve a level of estimate stability comparable to that of 200 respondents in a ten-day study, demonstrating the benefits of longer observation periods. 4.2.4 Discussion The entropy analysis demonstrates that longer observation periods and larger sample sizes are always more effective in capturing variability across different dimensions of travel behaviour. Although extending the observation length and increasing the sample size generally improve information richness, the benefits are subject to diminishing returns. The analysis indicates that the incremental value of expanding the observation length from five to ten weekdays is relatively minor compared to the substantial improvement obtained when extending from a single weekday to five weekdays. In other words, most of the variability in travel behaviour is captured within the first week, and beyond that, the added effort to collect longer multi-week data yields progressively smaller additional insights. These findings are consistent with early multi-week survey studies (Schlich & Axhausen, 2003 ) and provide practical guidance for balancing observation length, sample size, and respondent burden in future GPS-based travel surveys. Based on the empirical results, a minimum observation length of five weekdays, combined with a sample of approximately 600 respondents, is required to capture sufficient behavioural variability for robust travel behaviour analysis. This threshold ensures that both core travel routines and occasional, discretionary trips are adequately represented. 5. Conclusion This study advances the application of the core–satellite paradigm in travel survey design by demonstrating how targeted, multi-day GPS-based satellite surveys can complement large-scale, single-day surveys to capture richer behavioural insights. Using the 2022 Transportation Tomorrow Survey (TTS) as the core dataset and two satellite survey cycles conducted in 2023 and 2025, we assessed how extended observation periods improve our understanding of day-to-day variability in travel behaviour. Through the application of Shannon’s entropy, the study quantifies the gains in behavioural information as a function of both observation length and sample size. The results reveal that while longer tracking windows and larger samples enhance data quality, the marginal benefits taper off beyond a certain point. Most day-to-day variability is captured within the first five weekdays, suggesting that weeklong data—particularly covering typical workweek patterns—is essential for accurately representing behavioural diversity, mode switching, and trip chaining that are not visible in single-day surveys. However, rather than extending the length of core surveys—an approach that risks increasing respondent burden, reducing compliance, and raising costs—we demonstrate the value of using efficiently designed satellite surveys to capture this extended information. By observing a smaller, targeted sample (e.g., 600 individuals) for five weekdays, transportation agencies can generate the behavioural depth needed to support advanced modelling, policy analysis, and service design, without compromising the scale and representativeness of the core survey. In summary, this study underscores the necessity of weeklong travel data for capturing intra-individual variability in travel patterns. It supports a practical, scalable approach: a core–satellite survey structure where lean, multi-day GPS-based satellite samples enhance large-scale, typical-day data. This framework ensures both statistical robustness and behavioural richness, empowering transportation planners with the insights needed for more responsive, equitable, and effective decision-making in complex urban regions. Declarations Author Contribution The authors confirm their contribution to the paper as follows: Study conception and design: K. Wang, K.M.N. Habib; Data collection: K. Wang, M. Li, S. Afrose Suma, F. Ong, K.M.N. Habib; Analysis and interpretation of results: K. Wang, M. Li, S. Afrose Suma, F. Ong, K.M.N. Habib; Draft manuscript preparation: K. Wang, M. Li, S. Afrose Suma, F. Ong, K.M.N. Habib; Overall project supervision: K. Wang, K.M.N. Habib. All authors reviewed the results and approved the final version of the manuscript. Acknowledgment The 2025 cycle was funded by the City of Toronto, York Region, and Toronto Transit Commission (TTC). The authors are responsible for all results, interpretations, and comments the paper reports. References Aschauer, F., Hössinger, R., Axhausen, K. W., Schmid, B., & Gerike, R. (2018). Implications of survey methods on travel and non-travel activities: A comparison of the Austrian national travel survey and an innovative mobility-activity-expenditure diary (MAED). European Journal of Transport and Infrastructure Research, 18(1). Axhausen, K. W., Löchl, M., Schlich, R., Buhl, T., & Widmer, P. (2006). Fatigue in long-duration travel diaries. Transportation, 34(2), 143–160. https://doi.org/10.1007/s11116-006-9106-4 Axhausen, K. W., Zimmermann, A., Schönfelder, S., Rindsfüser, G., & Haupt, T. (2002). Observing the rhythms of daily life: A six-week travel diary. Transportation, 29(2), 95–124. https://doi.org/10.1023/a:1014247822322 Arentze, T. T., & Timmermans, H. H. (2009). A need-based model of multi-day, multi-person activity generation. Transportation Research Part B: Methodological, 43(2), 251–265. https://doi.org/10.1016/j.trb.2008.05.007 Auld, J., & Mohammadian. (2012). Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model. Transportation Research Part a Policy and Practice, 46(8), 1386–1403. Allström, A., Kristoffersson, I., & Susilo, Y. O. (2017). Smartphone based travel diary collection: experiences from a field trial in Stockholm. Transportation Research Procedia, 26, 32–38. American Association for Public Opinion Research. (2023). Retrieved July 2, 2025, from Standards and definitions: 10th edition: https://aapor.org/wp-content/uploads/2023/05/Standards-Definitions-10th-edition.pdf Buliung, R., Roorda, M. J., & Remmel, T. K. (2008). Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS). Transportation, 35(6), 697–722. https://doi.org/10.1007/s11116-008-9178-4 Chen, C., Gong, H., Lawson, C. T., & Bialostozky, E. (2010). Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study. Transportation Research Part A-policy and Practice, 44(10), 830–840. Cools, D., McCallum, S. C., Rainham, D., Taylor, N., & Patterson, Z. (2021). Understanding Google Location History as a tool for travel diary data acquisition. Transportation Research Record Journal of the Transportation Research Board, 2675(5), 238–251. https://doi.org/10.1177/0361198120986169 Data Management Group. (2024). 2022 TTS: Data Guide (Issue November). https://dmg.utoronto.ca/wp-content/uploads/2024/12/2022TTS_Data_Guide-1.pdf Dianat, L., Habib, K.M.N., & Miller, E. J. (2017). Two-Level, Dynamic, Week-Long Work episode Scheduling model. Transportation Research Record. Goulias, K. G., Pendyala, R. M., & Bhat, C. R. (2013). Keynote — Total Design Data Needs for the New Generation Large-Scale Activity Microsimulation Models. Transport Survey Methods: Best Practice for Decision Making, 21-46. doi:10.1108/9781781902882-002 Hanson, S., & Huff, O. J. (1988). Systematic variability in repetitious travel. Transportation, 15(1–2). Habib, K.M.N., Miller, E.J., Srikukenthrian, S., Lee-Gosselin, M., Morency, C., Roorda, M., & Shalaby, A. (2018). TTS2.0: A research and development (R&D) project on passenger travel survey methods. Retrieved July 03, 2020, from Huff, J. O., & Hanson, S. (1986). Repetition and variability in urban travel. Geographical Analysis, 18(2), 97–114. https://doi.org/10.1111/j.1538-4632.1986.tb00085.x Hanson, S., & Huff, O. J. (1988). Systematic variability in repetitious travel. Transportation, 15(1–2). https://doi.org/10.1007/bf00167983 Horton, F. E. (1968). Geographic studies of urban transportation and network analysis. https://ci.nii.ac.jp/ncid/BA38734543 Itsubo, S., & Hato, E. (2006). Effectiveness of Household Travel Survey Using GPS-Equipped Cell Phones and Web Diary: Comparative Study with Paper-Based Travel Survey. Transportation Research Board 85th Annual MeetingTransportation Research Board. https://trid.trb.org/view/776475 Jara-Díaz, S., & Rosales-Salas, J. (2014). Understanding time use: Daily or weekly data? Transportation Research Part a Policy and Practice, 76, 38–57. https://doi.org/10.1016/j.tra.2014.07.009 Kuhnimhof, T., & Gringmuth, C. (2009). Multi-day multiagent model of travel behavior with activity scheduling. Transportation Research Record, 2134(1), 178–185. Li, M., Wang, K., Liu, Y., & Habib, K.M.N. (2024). Deriving weeklong activity-travel dairy from Google Location History: survey tool development and a field test in Toronto. Transportation. https://doi.org/10.1007/s11116-024-10523-3 Li, W., Houston, D., Boarnet, M. G., & Park, H. (2018). Intrapersonal day-to-day travel variability and duration of household travel surveys: Moving beyond the One-day convention. Journal of Transport and Land Use, 11(1). https://doi.org/10.5198/jtlu.2018.984 Li, M., Wang, K., Liu, Y., & Habib, K.M.N. (2024). Deriving weeklong activity-travel dairy from Google location history: Survey tool development and a field test in Toronto. Transportation, 1-22. Retrieved from https://doi.org/10.1007/s11116-024-10523-3 Li, M., Wang, K., & Habib, K.M.N. (2025). Investigating recall & proxy bias in household travel surveys under the core-satellite fusion paradigm. Transportation. https://doi.org/10.1007/s11116-025-10654-1 Kelly, P., Krenn, P., Titze, S., Stopher, P. R., & Foster, C. (2013). Quantifying the Difference between Self-Reported and Global Positioning Systems-Measured Journey Durations: A Systematic review. Transport Reviews, 33(4), 443–459. Märki, F., Charypar, D., & Axhausen, K. W. (2014). Agent-based model for continuous activity planning with an open planning horizon. Transportation, 41(4), 905–922. https://doi.org/10.1007/s11116-014-9512-y Murakami, E., & Wagner, D. P. (1999). Can using global positioning system (GPS) improve trip reporting? Transportation research part c: emerging technologies, 7(2-3), 149-165. Molloy, J., Castro, A., Götschi, T., Schoeman, B., Tchervenkov, C., Tomic, U., Hintermann, B., & Axhausen, K. W. (2022). The MOBIS dataset: a large GPS dataset of mobility behaviour in Switzerland. Transportation, 50(5), 1983–2007. Moeckel, R., Huang, W., Ji, J., Llorca, C., Moreno, A. T., Staves, C., Zhang, Q., & Erhardt, G. D. (2024). The Activity-based model ABIT: Modeling 24 hours, 7 days a week. Transportation Research Procedia, 78, 499–506. Pougala, J., Hillel, T., & Bierlaire, M. (2023). From one-day to multi-day activity scheduling: Extending the OASIS Framework. 23rd Swiss Transport Research Conference. https://discovery.ucl.ac.uk/id/eprint/10174111/1/Pougala_EtAl.pdf Shen, L., & Stopher, P. R. (2014). Review of GPS travel survey and GPS Data-Processing Methods. Transport Reviews, 34(3), 316–334. Schlich, R., & Axhausen, K. W. (2003). Habitual travel behaviour: Evidence from a six-week travel diary. Transportation, 30(1), 13–36. https://doi.org/10.1023/a:1021230507071 Shankari, K., Bouzaghrane, M. A., Maurer, S. M., Waddell, P., Culler, D. E., & Katz, R. H. (2018). E-mission: An open-source, smartphone platform for collecting human travel data. Transportation Research Record: Journal of the Transportation Research Board, 2672(42), 1–12. https://doi.org/10.1177/0361198118770167 Siripanich, A., Rashidi, T. H., Kalyanaraman, S., Waller, T. S., Saberi, M., Dixit, V., & Nair, D. (2024). Using facebook to recruit urban respondents for Smartphone-based travel surveys. Transportation Research Interdisciplinary Perspectives, 25, 101116. https://doi.org/10.1016/j.trip.2024.101116 Shannon, C. E. (1948). A mathematical theory of communication. In The Bell System Technical Journal (Vol. 27, pp. 379–423) [Journal-article]. Tabasi, M., Siripanich, A., Khan, N. A., Auld, J., & Rashidi, T. H. (2024). GPS-supported smartphone app-based integrated travel diary and time-use data collection: Challenges and lessons learned. Transportation. https://doi.org/10.1007/s11116-024-10546-w Wang, K., Mashrur, S.M., Habib, K.M.N. (2024). Developing CUSTOM Framework: Explore Telecommuting-Induced Activity-Travel Demands With Mode Choice. Transportmetrica A: Transport Science, 1–39. https://doi.org/10.1080/23249935.2024.2438307 Additional Declarations No competing interests reported. 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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-7273201","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524671436,"identity":"a6fdc78d-bdfa-47df-9259-df502f9128bf","order_by":0,"name":"Melvyn Li","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Melvyn","middleName":"","lastName":"Li","suffix":""},{"id":524671438,"identity":"fcaf6b8a-8a38-421f-ba83-1606a5f0626b","order_by":1,"name":"Sumaiya Afrose Suma","email":"","orcid":"","institution":"University of 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07:19:20","extension":"xml","order_by":73,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132653,"visible":true,"origin":"","legend":"","description":"","filename":"1c45f7b59799487aa20daea6070baecd1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/6b0541376935b7828d93bd1e.xml"},{"id":93013651,"identity":"80c31079-1b9a-4e89-9bbb-be632b78dd64","added_by":"auto","created_at":"2025-10-08 07:27:19","extension":"html","order_by":74,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":145595,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/a371c1994561f5b5c00581f3.html"},{"id":93014036,"identity":"2f3e0e70-5924-4dfd-b927-8bd8a28807ac","added_by":"auto","created_at":"2025-10-08 07:35:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":482439,"visible":true,"origin":"","legend":"\u003cp\u003eTRAISI Move main sign-up screen and sample trip records with user labelled travel mode and purpose\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/4f47500f300ba2628cc18da6.png"},{"id":93011474,"identity":"ca4d053c-bd0a-4a35-a437-a01d17df3d55","added_by":"auto","created_at":"2025-10-08 07:19:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188205,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic flow of the 2025 cycle survey process\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/8fcde96bd0b1a2b58cc036cd.png"},{"id":93011455,"identity":"3a8366ec-13b6-45d6-85b1-a31b24cc2569","added_by":"auto","created_at":"2025-10-08 07:19:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42214,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the number of days captured per respondent in the 2025 cycle\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/975d2962e88c3d1648f97414.png"},{"id":93011454,"identity":"6df99e5e-90cd-4c00-b307-0d2f049ea38d","added_by":"auto","created_at":"2025-10-08 07:19:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1002489,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the Greater Toronto and Hamilton Area (GTHA) and household location distribution for the 2025 cycle\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/9d81bc198b4112774447987b.png"},{"id":93011447,"identity":"4fb09866-394f-45de-b8da-a708a9b68ef2","added_by":"auto","created_at":"2025-10-08 07:19:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":121126,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of trip rates between 2025, 2023 cycles, and 2022 TTS\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/9aa6454c9b3232c940a13f5f.png"},{"id":93011523,"identity":"6522da26-619c-494d-9a44-76ecbe5beeee","added_by":"auto","created_at":"2025-10-08 07:19:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":115178,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of trip distance between 2025, 2023 cycles and 2022 TTS\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/3273b9bcc2f40dbe76ad616f.png"},{"id":93011449,"identity":"d9ff6361-f75d-4094-aaa4-3e045272dfed","added_by":"auto","created_at":"2025-10-08 07:19:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":94609,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of trip duration between 2025 and 2023 cycles\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/e775519665ba96ad73fc28ae.png"},{"id":93013648,"identity":"fff926f6-c259-43fe-8e66-a66543f99f89","added_by":"auto","created_at":"2025-10-08 07:27:18","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":80575,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of trip start time between 2025, 2023 cycles and 2022 TTS\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/d5b47ada1f145c6cf30fed53.png"},{"id":93011470,"identity":"f4fd7282-4e39-445b-9ea0-adb9f6045217","added_by":"auto","created_at":"2025-10-08 07:19:16","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":98960,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of activity distribution between 2025, 2023 cycles and 2022 TTS\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/1c8738dde2cc7292b4fb4a07.png"},{"id":93011479,"identity":"9f2271f0-ec25-4549-bd83-390101ba7dd1","added_by":"auto","created_at":"2025-10-08 07:19:17","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":59754,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-cycle comparison of WFH and e-shopping frequency\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/d84d55fa1604b5e0ef9d0f8d.png"},{"id":93011488,"identity":"c218d9b6-c765-4bce-bec6-0ce0d2c5b311","added_by":"auto","created_at":"2025-10-08 07:19:17","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":58235,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-cycle comparison of cooked meal delivery day and time\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/a61375a0747e487100126cd0.png"},{"id":93011461,"identity":"99829723-f012-4b86-a95f-4c5bfd14cfe2","added_by":"auto","created_at":"2025-10-08 07:19:16","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":74323,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-cycle comparison of grocery delivery day, time and waiting time\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/598ec1f46f2e5fd7da613746.png"},{"id":93011507,"identity":"9292285a-0d38-4747-b904-1e7005a399a3","added_by":"auto","created_at":"2025-10-08 07:19:18","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":200436,"visible":true,"origin":"","legend":"\u003cp\u003eAverage entropy for mode choice\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/fc6b67f520dc544f6536c82f.png"},{"id":93013619,"identity":"2f75df1f-d16d-494e-8776-dcc5fdf5abb2","added_by":"auto","created_at":"2025-10-08 07:27:16","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":156086,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of activity type choice for first and subsequent episodes for each day\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/ad7f326a44b542b1ff9e4a63.png"},{"id":93011481,"identity":"84b0504f-6811-4d4c-bd2e-e81c0323dba9","added_by":"auto","created_at":"2025-10-08 07:19:17","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":368978,"visible":true,"origin":"","legend":"\u003cp\u003eAverage entropy for activity type choice\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/c1ee851ba4c7c650c9e2d9e4.png"},{"id":93014041,"identity":"2f217b48-e9b6-457f-b4ef-05f9d69e647b","added_by":"auto","created_at":"2025-10-08 07:35:17","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":199058,"visible":true,"origin":"","legend":"\u003cp\u003eAverage entropy calculations for the dimension of location choice\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/104b71b4f5306cc9fa395cbe.png"},{"id":93014986,"identity":"0a85ccc6-f3bb-4313-bda0-7b88acc4dc3a","added_by":"auto","created_at":"2025-10-08 07:51:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3783576,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7273201/v1/dcac5be5-e754-4c34-9f76-76a96ad2e23c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond the Typical Day: Evaluating Multi-Day Travel Behaviour Through a Core–Satellite Survey Approach in the Greater Toronto and Hamilton Area","fulltext":[{"header":"1. Introduction \u0026 background","content":"\u003cp\u003eA key area of ongoing advancement involves the development of travel demand forecasting models capable of capturing multi-day activity-travel patterns (Arentze \u0026amp; Timmermans, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kuhnimhof \u0026amp; Gringmuth, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Auld \u0026amp; Mohammadian, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; M\u0026auml;rki et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dianat et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pougala et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Moeckel et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These models account for interdependencies across consecutive days and address the limitations of single-day approaches in representing the cumulative effects of travel-related choices. Understanding the dynamics of travel behaviour requires high-quality data and robust models that capture the temporal complexity of individuals\u0026rsquo; activity-travel decisions.\u003c/p\u003e\u003cp\u003eA critical component supporting the development of such models is the multi-day travel diary, which provides sequential records of individuals\u0026rsquo; trips and activities collected over a defined timeframe. Various methods exist for collecting multi-day diaries, with global positioning system (GPS) technology emerging as one of the most reliable approaches (Shen \u0026amp; Stopher, 2007). In recent years, the widespread adoption of GPS-enabled smartphones has significantly enhanced the feasibility of large-scale GPS data collection (Patterson \u0026amp; Fitzsimmons, 2016; Patterson et al., 2019). This technology enables the passive recording of travel diaries as respondents carry out their daily routines (Murakami \u0026amp; Wagner, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kelly et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Shen \u0026amp; Stopher, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Compared to traditional diary methods, GPS-based data offer more granular detail and can capture activity-travel patterns over extended periods (Allstr\u0026ouml;m et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Molloy et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, respondent burden is substantially reduced as trip attributes such as origin, destination, and timing are automatically logged rather than relying on manual recall (Itsubo \u0026amp; Hato, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Murakami \u0026amp; Wagner, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study extends prior work by leveraging a multi-cycle GPS-based survey under the core-satellite paradigm (Goulias et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Habib et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Under this paradigm, large-scale household travel surveys serve as the core dataset, while satellite surveys are deployed for specialized purposes to enhance and complement the core. In this study, the 2022 Transportation Tomorrow Survey (TTS), a regional household travel survey conducted in the Greater Toronto and Hamilton Area (GTHA), sampled approximately 5% of the population and is the core dataset (Data Management Group, 2025). The first satellite cycle implemented in 2023 employed a hybrid approach. Respondents\u0026rsquo; multi-day travel behaviour was captured using Google Location History. Then, they submitted their location history files via an online survey platform (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This hybrid approach successfully collected seven-day travel diaries for 956 individuals across the GTHA. The detailed survey design and results from the 2023 cycle are presented in Li et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study first reports on the survey design, implementation, and preliminary findings from the latest 2025 cycle. The 2025 cycle employs a dedicated smartphone-based travel survey application to collect multi-day travel data. Travel behaviours observed in the 2025 cycle are compared with those revealed in the 2022 core and the 2023 satellite cycle, enabling a comprehensive assessment across datasets. Unlike the first satellite cycle, which specifically targeted a seven-day observation window, the 2025 cycle allows respondents to participate for extended periods, thereby capturing multi-week activity-travel patterns. This extended observation provides a unique opportunity to address questions concerning the optimal length of data collection required to capture the dynamics of activity-travel behaviours adequately.\u003c/p\u003e\u003cp\u003ePrevious literature has debated the length of the observation period that can provide sufficient information on the dynamics of activity-travel behaviours. Hanson and Huff (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) acknowledged that collecting data over multiple weeks would be necessary for a comprehensive observation of all travel patterns. However, multi-weeklong data collection was rare in practice due to its high operational cost. From the literature, only four surveys collected activity-travel behaviours for more than one week. The Cedar Rapids Travel Diary was compiled over 30 days in 1949 (Horton, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1968\u003c/span\u003e). The Uppsala survey was conducted over 35 days in 1971 (Huff \u0026amp; Hanson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). The Mobidrive survey collected six-week travel diaries in Germany (Axhausen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Finally, the Thurgau survey also collected six-week travel diaries in Switzerland (Axhausen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Apart from the surveys above, most studies in the literature known to the authors collected multi-day dairies from two to seven days (Buliung et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUsing the Mobidrive survey, Schlich \u0026amp; Axhausen (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) compared the distribution of the mean of the intrapersonal similarity indices and their standard deviations within one to six weeks. The similarity index measured the similarity between the number of trips made on different days. They found that the distribution of the mean intrapersonal similarity indices was similar for all time periods. Moreover, they found that the standard deviations of the mean intrapersonal similarity indices were similar after two weeks. Their results indicate that an observation period of one to two weeks was acceptable to capture day-to-day variability in travel behaviours. However, two weeks were preferred if conditions permitted. Schlich and Axhausen (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) made a pioneering and significant contribution to the question of optimal observation length in travel behaviour studies. However, they also acknowledged certain limitations of their method. Firstly, the similarity index they used cannot account for all attributes of a trip, which means trips may be classified as identical even when they differ in important characteristics. More critically, their analysis compares days at an aggregate level rather than considering the sequence of activities. As a result, both order and the timing of activities did not influence the similarity measure.\u003c/p\u003e\u003cp\u003eThis study addresses the limitations identified above. It examines the uncertainty inherent in datasets with varying observation periods and sample sizes. The analysis focuses on key choice dimensions commonly explored in travel behaviour modelling, particularly in activity-based models. These dimensions include travel mode choice, activity purpose choice, location choice, and time allocation (Wang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To quantify the variability and predictability of individual travel behaviours, the study employs Shannon\u0026rsquo;s entropy from information theory (Shannon, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1948\u003c/span\u003e). With its solid theoretical foundation and widespread application across disciplines, Shannon entropy offers a rigorous and interpretable framework for measuring behavioural complexity and randomness in travel surveys.\u003c/p\u003e\u003cp\u003eThe paper is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the survey design and conduct. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents descriptive analysis by comparing the 2025 cycle with the 2023 cycle and the core survey. Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e4\u003c/span\u003e investigates the question of the desired survey length of a multi-day travel survey. Finally, Section \u003cspan refid=\"Sec20\" class=\"InternalRef\"\u003e5\u003c/span\u003e concludes the study by summarising key findings.\u003c/p\u003e"},{"header":"2. Survey Design","content":"\u003cp\u003eThis section presents the development of survey instruments used and the survey procedure \u0026amp; response behaviours in the 2025 cycle.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 The smartphone application\u003c/h2\u003e\n \u003cp\u003eThis study used a smartphone-based tool named TRAISI Move to collect a multi-day travel diary. The application was developed based on the open-source OpenPATH codes developed by the US National Renewable Energy Laboratory (NREL), which provides a robust platform for mobility tracking and survey data collection (Shankari et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The OpenPATH family of travel survey tools have a proven record and have been used in many smartphone-based travel studies. Tabasi et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) used the OpenPATH code to develop an application called Fourstep to collect data in Sydney and Chicago. In a separate effort, Siripanich et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) leveraged a similar OpenPATH family smartphone application to examine the effectiveness of recruiting travel survey respondents via social media platforms. Their findings highlight social media recruitment as a cost-effective and efficient strategy for surveying the general population.\u003c/p\u003e\n \u003cp\u003eThe OpenPATH codes are free to be redistributed under the BSD 3-Clause license, which allows usage of the software on the condition that any redistributions of the code or compiled binaries must contain the original copyright notice. Additionally, it requires that derivatives of the software cannot be endorsed or promoted using the names of the original copyright holder or contributors (namely, the National Renewable Energy Laboratory). Therefore, the survey team named the application as TRAISI Move after the TRavel Activity Internet Survey Interface (TRAISI) web-based survey tools developed by the survey team (Li et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTRAISI Move is available for both iOS and Android devices through the official Apple App Store and Google Play Store, respectively. Survey respondents can easily join the study by scanning a QR code or entering a special study code provided by the survey team. This allows respondents to easily log back into the study if they swap devices or have signed out for any reason. It prevents the need for any lengthy sign-up or account creation process. Figure\u0026nbsp;1 presents the main TRAISI Move launching screen with the QR code and manual code entry options.\u003c/p\u003e\n \u003cp\u003eAfter installation and start tracking, the application can operate in the background, requiring minimal user interaction. TRAISI Move automatically identifies when a respondent begins a trip and records route traces. Trips are saved once the respondent has reached a destination, after which users can retrospectively annotate their trips by selecting travel modes and trip purposes. Figure 1 also contains a sample travel diary screen, showing trips that TRAISI Move has recorded for the respondent and user, labelled with travel mode and purpose for each trip.\u003c/p\u003e\n \u003cp\u003eTo accommodate varying respondent loads and ensure reliable data storage, TRAISI Move can utilize multiple backend servers. This distributes computational and storage demands more efficiently. The system is designed for scalability, allowing new servers to be deployed dynamically as needed. This study was deployed with a rolling server system. Each server was hosted on a cloud platform on local machines physically situated in Toronto. The specification of each server instance is eight vCPUS and 32GiB of memory. This configuration allows up to four hundred simultaneous users over around two weeks to utilize the application smoothly without interruption. The study was deployed on one instance initially. As more capacity was required, more server instances were deployed. In the end, three cases were required to complete the study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Survey procedure \u0026amp; response behaviours\u003c/h2\u003e\n \u003cp\u003eThe TRAISI Move smartphone application described in the previous section was used to collect the second satellite cycle. The survey was conducted in the Spring of 2025, from March to June. The study area of the second satellite is the Greater Toronto and Hamilton Area (GTHA), Canada, which is the same as the 2023 cycle and is a sub-area of the 2022 TTS.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the two-phase procedure implemented to collect the 2025 cycle. Random samples of GTHA residents who participated in the 2022 TTS (the core regional household travel survey) were invited to join the first phase. The invitation email was sent using a dedicated university-authorized email address to enhance credibility. The invitation clearly stated the purpose of the study, and at least one week of tracking time is required. Upon successful completion of the two-phase study, a C\u003cspan\u003e$\u003c/span\u003e20 gift card compensation would be providedresearch. In the first phase, socioeconomic information was collected through a web-based survey, where respondents also provided their consent to install the TRAISI Move application, enabling tracking on their phones. Respondents were asked to carry their smartphones throughout their daily activities during the data collection period.\u003c/p\u003e\n \u003cp\u003eRespondents who had been tracked for at least a full week were subsequently invited to participate in a second-phase web survey, which collected additional information on their work-from-home (WFH) arrangements and e-shopping behaviours for the same observed full week period. This approach aligns with the Mobility-Activity-Expenditure-Diary (MAED) framework proposed by Aschauer et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), which advocates for expanding traditional travel diaries to include non-travel activities to present a more comprehensive picture of household behaviour. A similar approach was adopted in the 2023 satellite cycle that collected WFH schedules and detailed online shopping behaviours (e.g., item types, expenditure, delivery timing, etc.) alongside seven-day travel diaries.\u003c/p\u003e\n \u003cp\u003eFor the initial invitation, 32,067 invitation emails were sent. As a result, 1,172 individuals completed the registration survey in the first phase. The response rate for the first phase was 3.65% of the total initial invitation sent (American Association for Public Opinion Research, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Among the 1,172 individuals who started tracking, 999 successfully generated travel diaries for at least two days. During the tracking period, respondents\u0026rsquo; tracking status was actively monitored. A total of 318 status check emails were sent when the survey team identified that the tracking application on a respondent\u0026rsquo;s smartphone had stopped recording data. Of these, 154 respondents replied to the email and completed the one-week tracking requirement.\u003c/p\u003e\n \u003cp\u003eIn total, those 999 respondents generated 64,021 trips, and 48,900 trips were fully labelled with travel mode and purpose. Finally, out of the 999 respondents, 872 individuals achieved the requirement by accumulating at least one week of travel diary. All of them were invited to participate in the second phase survey. As a result, 861 respondents completed the second phase survey.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the distribution of the number of days with travel data captured and subsequently labelled by respondents. While a substantial number of respondents had 10 to 20 days of captured data, the number of labelled days consistently falls below the number of captured days across the distribution. On average, 13.7 days were captured, and 10.7 days were fully labelled. This gap reflects the respondent burden associated with manual validation, especially over extended periods. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the study area of the 2025 cycle and the spatial distribution of households in the 2025 cycle across the Greater Toronto and Hamilton Area (GTHA). The dataset captures a geographically diverse sample, with respondents spread across core urban centers such as Toronto, Mississauga, Hamilton, and extending into suburban areas.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Comparison of descriptive statistics between survey cycles","content":"\u003cp\u003eThis section compares the socioeconomic characteristics and revealed activity-travel behaviour of samples from the 2025 and 2023 satellite cycles with those from the 2022 Transportation Tomorrow Survey (TTS), which serves as the core and benchmark dataset.\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Socioeconomic attributes\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution of socioeconomic variables in the 2025 cycle compared to both the 2023 cycle and the 2022 TTS. The 2022 TTS was weighted to match the 2021 Canadian Census, so the socioeconomic characteristics revealed from the TTS can be treated as the true population characteristics (Data Management Group, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Overall, the 2025 cycle matches the general GTHA population well, although there are a few notable differences. The satellite samples have more males than the 2022 TTS. Males comprise 61.7% and 57.2% of the samples in 2025 and 2023 cycles, respectively. The gender distribution in TTS is more balanced. In terms of age, the 2025 and 2023 cycles over-represent middle-aged groups (age between 30 to 64). Additionally, the household income distribution in the 2025 cycle also shows some differences when compared to the 2023 cycle and 2022 TTS. The 2025 cycle has a higher percentage of households earning over \u003cspan\u003e$\u003c/span\u003e200,000 (27.99%) compared to the 2023 cycle (18.90%) and the 2022 TTS (13.18%).\u003c/p\u003e\n \u003cp\u003eThe employment status distribution in the 2025 cycle aligned closely with the 2023 cycle. However, the 2022 TTS had a higher proportion of unemployed individuals (47.05%) compared to both the 2025 (23.98%) and 2023 (20.90%) cycles. This aligns with the age distribution, where 2022 TTS captured more unemployed younger and older populations. In contrast, the 2025 and 2023 cycles had a higher representation of the working population. Three-quarters of the 2025 cycle workers reported that they could work remotely, which also aligns with the 2023 cycle distribution. Overall, the 2025 and 2023 samples are more affluent than the TTS benchmark, which may reflect sample selection biases (e.g., willingness to participate in follow-up study) or the effects of survey methodology preferences (e.g., desire to join in smartphone-based research).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \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\u003eKey sample socioeconomic attributes in the 2025, 2023 satellite, and the 2022 core dataset\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2025 Cycle\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2023 Cycle\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022 TTS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAttributes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eGender (%)\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\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Size (%)\u003c/strong\u003e\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\u003e21.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.25\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\u003e38.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.20\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\u003e15.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.32\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\u003e16.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.27\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\u003e7.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Income (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow \u003cspan\u003e$\u003c/span\u003e39,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e40,000-\u003cspan\u003e$\u003c/span\u003e79,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e80,000-\u003cspan\u003e$\u003c/span\u003e124,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e125,000-\u003cspan\u003e$\u003c/span\u003e199,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e200,000 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeclined to answer/don\u0026rsquo;t know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment Type (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed full-time (\u0026ge;\u0026thinsp;30 hours per week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed part-time (\u0026lt;\u0026thinsp;30 hours per week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorkplace Arrangement (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsual place of work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.60\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\u003eWorked at home \u0026amp; hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.00\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\u003eNo fixed workplace address\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.40\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\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eToronto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDurham\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHalton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHamilton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Activity-travel behaviour\u003c/h2\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Average trip rates\u003c/h2\u003e\n \u003cp\u003eThis section compares weekday trip rates across three datasets: the 2025 cycle, the 2023 cycle, and the 2022 TTS. Since the TTS only collected one-day travel diaries for weekdays, the weekdays in the satellite surveys are compared against the TTS. In addition, weekend trip rates are compared between the 2025 and 2023 cycles. Figure 5 illustrates that the 2025 cycle consistently reports higher trip rates than both the 2023 cycle and the 2022 TTS. The TTS yields the lowest average trip rate, with only 2.1 trips per day, reflecting measurement errors such as proxy and recall bias inherent in self-reported survey data (Li et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). In contrast, the satellite-based surveys capture substantially higher trip rates: the 2025 cycle reports an average of 3.8 trips on weekdays and 4.0 on weekend days, while the 2023 cycle reports 3.2 and 3.3 trips, respectively.\u003c/p\u003e\n \u003cp\u003eThe differences in reported trip rates between the 2025 and 2023 cycles can be attributed to the underlying data collection methods. The 2025 cycle employed a dedicated smartphone application specifically designed for continuous tracking, whereas the 2023 cycle relied on passive data from Google Location History (GLH). The dedicated smartphone application in 2025 was more effective in capturing short-distance movements, which are often overlooked in GLH-derived data (Cools et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This is evident in the distribution of walking trip distances reported in the next section. The 2025 cycle reports significantly more walking trips under 500 meters.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Trip distances and durations\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 6\u003c/strong\u003e presents a comparison of trip distance distributions across survey cycles. As previously discussed, the 2025 cycle captured a significantly higher number of short-distance walking trips under 500 meters compared to the 2023 cycle. This reflects the sensitivity of the dedicated smartphone application used in the 2025 cycle to detect fine-grained non-motorized movements. In contrast, the distance distributions for motorized modes of travel, including automobile and transit, as well as for cycling, appear broadly consistent across the surveys. This consistency suggests that while all instruments were effective in capturing medium- to long-distance trips, the 2025 cycle provided more comprehensive coverage of short-distance active travel. These findings highlight the importance of selecting appropriate survey instruments when aiming to capture the full spectrum of travel behaviour, especially for active modes.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e presents a comparison of trip duration distributions across survey cycles. The 2022 TTS is not included because it only collected the trip origin and destination without the trip level-of-service information. The results show that the trip duration distribution between 2025 and 2023 cycles is similar for automobile, cycling, and walking. The most significant difference is the higher share of longer-duration transit trips captured in the 2025 cycle. Specifically, nearly 40% of local transit trips in 2025 fall within the 30-minute to 1-hour range, compared to less than 20% in the 2023 cycle.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.3 Trip start time\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 8\u003c/strong\u003e depicts the share of trip start times throughout the day for weekdays across three datasets. All three datasets display the expected bimodal morning and afternoon commuting peaks. However, a key distinction emerges between the core and satellite surveys: both satellites exhibit a distinct midday peak around noon, which is largely absent from the TTS. This mid-day peak likely reflects non-commute activities that are typically underreported in self-reported TTS. The sharper and more compressed morning and afternoon peaks in the TTS indicate a bias toward capturing only work-related trips. In contrast, the passively tracked data from the 2023 and especially the 2025 cycles provide a more comprehensive temporal profile of daily travel behaviour.\u003c/p\u003e\n \u003cp\u003eIn addition, \u003cstrong\u003eFig.\u0026nbsp;8\u003c/strong\u003e also compares the distribution of weekend trip start times between the 2025 and 2023 survey cycles. Unlike weekday travel patterns, weekend trips show a delayed and more uniform distribution, with trips gradually increasing and peaking around noon and then tapering off into the evening. Both cycles exhibit nearly identical temporal profiles, suggesting consistent weekend travel behaviour across years and data collection methods.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.4 Activity (trip) purpose\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e presents the distribution of weekday activity purposes across the 2025 and 2023 satellite cycles and the 2022 TTS. Notable differences emerge across the datasets. The 2022 TTS places substantial emphasis on work and school-related trips, which together account for over 30% of reported weekday activities. In contrast, the 2025 and 2023 satellite cycles, both based on passively collected data, captured a broader range of discretionary and non-mandatory activities, such as recreational travel, social visits, and dining out. Notably, recreational activities account for nearly 20% of weekday trips in the 2025 cycle, compared to under 10% in the 2023 cycle. At the same time, the share of shopping trips declines from 2023 to 2025. A similar shift in activity purposes is also evident at weekends. This pattern may suggest a behavioural shift toward more diverse and socially oriented activity participation.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.5 Work-from-home\u0026amp; e-shopping\u003c/h2\u003e\n \u003cp\u003eBoth 2025 and 2023 cycles collected individuals\u0026apos; work-from-home (WFH) and e-shopping behaviours. Figure\u0026nbsp;10 presents the overall trends for both online behaviours. For WFH, individuals were asked in the survey if they worked from their homes on each day of the week. For each weekday, the share of workers in the samples who worked from home is almost identical between 2025 and 2023. The largest share of WFH happened on Monday and Friday, and more workers chose to work on-site during the middle of the week.\u003c/p\u003e\n \u003cp\u003eFor e-shopping, individuals were asked to report the details of their online purchases during the same week period. Figure 10 also presents the average frequency of three categories of e-shopping activities: cooked meal delivery (CMD), grocery delivery, and other retail e-shopping. The general e-shopping trend for all categories of goods remains similar between the 2025 and 2023 cycles. Across both cycles, retail purchases excluding meals and groceries online (named as \u0026ldquo;other e-shopping\u0026rdquo; in \u003cstrong\u003eFig.\u0026nbsp;10\u003c/strong\u003e) are by far the most frequent, occurring more than once per respondent each week in both datasets. The 2025 cycle shows a slightly lower frequency of this category compared to the 2023 cycle. At the same time, CMD and grocery delivery frequencies remain relatively low and stable across both cycles.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 11\u003c/strong\u003e offers a more granular comparison of cooked meal delivery (CMD) patterns across the 2023 and 2025 cycles, focusing on both the day of the week and time of delivery. The 2025 cycle shows an increase in weekday CMD activity, particularly on Wednesdays. While the specific day on which respondents ordered meals varied between cycles, the temporal distribution of deliveries remained largely consistent between cycles. In both cycles, CMD activity exhibits a pronounced evening peak, with approximately 50% of deliveries occurring after 7:00 PM, indicating that most CMDs were ordered during dinner hours. These results underscore the strong alignment between meal delivery demand and conventional dining routines. Consequently, modelling efforts and policy interventions such as congestion pricing or delivery fleet optimization should account for this peaked evening demand.\u003c/p\u003e\n \u003cp\u003eFor each grocery delivery, the survey recorded the delivery day, time of arrival at the respondents\u0026rsquo; homes, and the associated waiting time. Figure\u0026nbsp;12 compares grocery delivery patterns across the 2023 and 2025 survey cycles. It is important to note that these patterns reflect the interaction between consumer demand and the operational strategies of logistics providers. Unlike CMDs, which typically require fulfillment within a short time window (often within an hour), grocery deliveries allow for greater flexibility in scheduling. As a result, logistics providers have more control over delivery timing, enabling them to optimize route planning and resource allocation. The observed trends in delivery timing and waiting periods thus offer insights into the evolving dynamics between consumer expectations and supply-side logistics performance.\u003c/p\u003e\n \u003cp\u003eThe comparison across the two cycles highlights how delivery operations may have adapted or improved between 2023 and 2025. The results reveal modest but meaningful shifts in grocery delivery scheduling patterns over time, likely reflecting evolving consumer ordering behaviour and adaptations in logistics provider operations. In the 2023 cycle, deliveries were more concentrated at the beginning and end of the week, with pronounced peaks on Monday and Saturday. In contrast, the 2025 cycle exhibits a more balanced distribution of deliveries across weekdays, particularly from Tuesday to Friday, suggesting a smoother operational spread. In terms of delivery timing, the comparison highlights a stronger concentration of grocery deliveries during standard daytime hours in 2025 relative to 2023. This shift may indicate a growing preference among consumers for predictable daytime delivery windows. Concerning waiting times, both cycles show that the majority of grocery deliveries occurred within 24 to 48 hours of order placement, reinforcing a prevailing norm of next-day or second-day service. These patterns reflect a stabilized expectation from consumers and a logistical equilibrium between 2023 and 2025.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Effects of observation length on information quality","content":"\u003cp\u003eDetermining the appropriate observation length in multi-day travel surveys is a critical issue in guiding survey design and practice. In the transportation literature, only two major multi-week datasets have been rigorously examined on this topic: the Uppsala Household Travel Survey (Hanson \u0026amp; Huff, \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e) and the Mobidrive survey (Schlich \u0026amp; Axhausen, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). More recent studies by Jara-D\u0026iacute;az and Rosales-Salas (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Li et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) have also explored this issue; however, their analyses were constrained by datasets limited to a maximum of seven days, preventing them from examining multi-week effects.\u003c/p\u003e\n\u003cp\u003eIn this study, a subset of respondents from the 2025 survey cycle was observed over multiple weeks, offering a valuable opportunity to empirically assess the impact of observation length on travel behaviour metrics. The following section presents an analysis aimed at identifying the appropriate duration for GPS-based travel surveys to capture the uncertainty in travel patterns adequately.\u003c/p\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Entropy measurement\u003c/h2\u003e\n \u003cp\u003eThe analysis uses Shannon\u0026apos;s entropy, a concept derived from information theory, to quantify uncertainty (or termed as the variability in transport studies) in individuals\u0026rsquo; travel behaviour across multiple days (Shannon, \u003cspan class=\"CitationRef\"\u003e1948\u003c/span\u003e). This measure captures predictability, or conversely, the diversity of travel choices over a given observation period. For a multinomially distributed travel behaviour choice \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:K\\)\u003c/span\u003e\u003c/span\u003e observed for a period of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:D\\)\u003c/span\u003e\u003c/span\u003e days for a sample with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N\\)\u003c/span\u003e\u003c/span\u003e individuals, the average entropy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{NDK}\\)\u003c/span\u003e\u003c/span\u003e) is calculated as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{H}_{NDK}=\\:\\frac{1}{N}\\sum\\:_{n=1}^{N}\\frac{1}{D}\\sum\\:_{d=1}^{D}(-\\sum\\:_{k=1}^{K}{p}_{ndk}\\text{l}\\text{o}\\text{g}({p}_{ndk}\\left)\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{kdn}\\)\u003c/span\u003e\u003c/span\u003e is the probability of choosing alternative \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e among the choice-set \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:K\\)\u003c/span\u003e\u003c/span\u003e in the observation length \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:D\\)\u003c/span\u003e\u003c/span\u003e for individual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe entropy measurement in Eq. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e allows for the simultaneous evaluation of trade-offs between two analytical objectives. Firstly, the entropy examines the stability and temporal dynamics of activity-travel patterns when comparing shorter to longer observation periods. Secondly, the entropy assesses the capacity of the sample size to capture uncertainty in travel behaviours in the survey.\u003c/p\u003e\n \u003cp\u003eThe analysis in this study estimates sample-enumerated average entropy in the 2025 survey cycle using a bootstrapping approach. Entropy is calculated for various travel behaviour choices across different observation lengths: one weekday, two weekdays, three weekdays, five weekdays (representing one full work week), and ten weekdays (representing two full work weeks). For each observation length, a wide range of sample sizes is examined, beginning with 20 respondents and incrementally increasing up to 800, which is close to the full size of the 2025 cycle. The only exception is for the ten-weekday observation length, where the sample size is constrained by the limited number of respondents with two full weeks of observation.\u003c/p\u003e\n \u003cp\u003eBootstrapping is then applied to assess the variability of entropy estimates. For each trial, a random sample of respondents is drawn without replacement from the entire 2025 cycle. This process is repeated 100 times for each combination of observation length and sample size. In each iteration, the sample-enumerated average entropy is computed. According to the central limit theorem, the bootstrapping procedure yields a normal distribution of the mean entropy values. From this distribution, the mean and standard deviation of the average entropy are derived for each combination of observation length and sample size for a targeted choice behaviour. This allows for assessing the variability and robustness of the entropy measure across different targeted travel behaviour choices.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Entropy calculation results\u003c/h2\u003e\n \u003cp\u003eThe study evaluates three key choices to travel demand modelling: travel mode choice, activity purpose choice, and location choice. The results are presented below.\u003c/p\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.1 Travel mode choice\u003c/h2\u003e\n \u003cp\u003eThe uncertainty in travel mode choice across different observation lengths and sample sizes is evaluated by categorizing each trip into one of the following modes: driving, auto passenger, ride-hailing and taxi, transit, walking, biking, or other. This classification is consistent with the mode choice definitions used in the operational activity-based models (ABMs) of the Greater Toronto and Hamilton Area (GTHA) (Wang et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 13\u003c/strong\u003e illustrates the relationship between the average entropy of mode choice and sample size across various observation lengths. As expected, entropy increases with longer observation periods, reflecting the ability to capture a more diverse range of mode choice behaviours over time. Single-day observations exhibit the lowest entropy values, indicating limited variability in recorded mode choices. A sharp increase in entropy is observed when the observation period extends beyond a single day, suggesting improved representation of variability in this target behaviour. However, beyond five weekdays, the marginal gain in entropy diminishes, with entropy levels for ten weekdays close to those observed for five weekdays.\u003c/p\u003e\n \u003cp\u003eIn terms of sample size, the results indicate that entropy stabilizes rapidly as the number of respondents increases. After approximately 400 individuals, the standard deviation of entropy converges and shows little variation with additional sample size.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.2 Activity type choice\u003c/h2\u003e\n \u003cp\u003eNext, the individual\u0026rsquo;s activity type choice is examined. Targeting activity type choice will measure the variability of activity participation over multiple observation lengths. The activity type choice considers the following alternatives in the choice set: work \u0026amp; school, errands, retail shopping, eating out \u0026amp; picking up food, facilitating others, other discretionary activities, and home-based activities. This classification is also consistent with the activity type definitions used in the operational ABMs (Wang et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe activity type choices for the first episode (the first activity of each day) and all subsequent episodes are distinguished in the entropy calculation. The market shares they are different due to the scheduling nature. Figure \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e compares the market share difference. Work \u0026amp; study activities, dominate the first episode and home-based activities dominate the subsequent episodes.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 15\u003c/strong\u003e presents the entropy results for the first activity episode of each day. As a baseline, it is important to note that the entropy for one weekday is effectively zero, since each respondent contributes only a single first-episode observation, leaving no room for measuring intra-individual variability. Like the findings for mode choice, the results indicate that longer observation periods consistently yield higher entropy values, reflecting greater variability in the types of activities individuals engage in at the start of their day.\u003c/p\u003e\n \u003cp\u003eIn terms of observation stability, the standard deviation of entropy declines sharply with increasing sample size for all observation lengths (excluding the ten-weekday sample, which is limited by a smaller respondent pool of around 200). After approximately 400 respondents, the standard deviation plateaus, indicating consistent entropy estimates and diminishing returns from further expanding the sample.\u003c/p\u003e\n \u003cp\u003eFor subsequent episodes, the results follow a similar pattern to those observed for the first episode. However, the pattern in standard deviation differs. While the general trend holds the same (i.e., standard deviation declines as sample size increases), the absolute values of the standard deviations vary across observation lengths. Specifically, for a given sample size, the standard deviation is consistently lower for longer observation periods (i.e., five and ten weekdays) compared to shorter ones.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.3 Location choice\u003c/h2\u003e\n \u003cp\u003eThe final targeted behaviour examined is the location choice. A discrete approach is applied to analyze this decision-making process. For each trip, respondents can select from eight possible location choices, which are classified into three main categories: home, work \u0026amp; school, and discretionary activities. For home, work \u0026amp; and school trips, destinations are assumed to be fixed, reflecting the routine nature of these activities.\u003c/p\u003e\n \u003cp\u003eIn contrast, discretionary activities are characterized by the distance between the destination and the respondents\u0026rsquo; home. This results in six distinct location alternatives for discretionary activities:\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e0\u0026ndash;2 kilometers from home (50th percentile),\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e2 (inclusive) \u0026ndash; 6 kilometers from home (70th percentile),\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e6 (inclusive) \u0026ndash; 10 kilometers from home (80th percentile),\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e10 (inclusive) \u0026ndash; 20 kilometers from home (90th percentile),\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e20 (inclusive) \u0026ndash; 40 kilometers from home (98th percentile),\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMore than 40 km from home\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe distance thresholds for each discretionary location choice are determined based on the empirical distance distribution observed in the 2025 cycles and the corresponding percentile cutoffs. Locations falling beyond the 98th percentile are treated as a standalone category to ensure that the analysis remains sensitive to infrequent long-distance discretionary trips.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 16\u003c/strong\u003e shows the relationship between the average entropy of location choice and sample size. Consistent with the patterns observed for other behavioural choices, longer observation periods consistently yield higher entropy values. Entropy increases sharply when extending from single-day to multi-day observations, but the incremental gain beyond five weekdays is minimal, suggesting diminishing returns with extended survey duration.\u003c/p\u003e\n \u003cp\u003eHowever, the standard deviation analysis highlights the trade-off between observation length and sample size. Approximately 600 respondents in a five-day survey are required to achieve a level of estimate stability comparable to that of 200 respondents in a ten-day study, demonstrating the benefits of longer observation periods.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.4 Discussion\u003c/h2\u003e\n \u003cp\u003eThe entropy analysis demonstrates that longer observation periods and larger sample sizes are always more effective in capturing variability across different dimensions of travel behaviour. Although extending the observation length and increasing the sample size generally improve information richness, the benefits are subject to diminishing returns. The analysis indicates that the incremental value of expanding the observation length from five to ten weekdays is relatively minor compared to the substantial improvement obtained when extending from a single weekday to five weekdays. In other words, most of the variability in travel behaviour is captured within the first week, and beyond that, the added effort to collect longer multi-week data yields progressively smaller additional insights. These findings are consistent with early multi-week survey studies (Schlich \u0026amp; Axhausen, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e) and provide practical guidance for balancing observation length, sample size, and respondent burden in future GPS-based travel surveys. Based on the empirical results, a minimum observation length of five weekdays, combined with a sample of approximately 600 respondents, is required to capture sufficient behavioural variability for robust travel behaviour analysis. This threshold ensures that both core travel routines and occasional, discretionary trips are adequately represented.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study advances the application of the core\u0026ndash;satellite paradigm in travel survey design by demonstrating how targeted, multi-day GPS-based satellite surveys can complement large-scale, single-day surveys to capture richer behavioural insights. Using the 2022 Transportation Tomorrow Survey (TTS) as the core dataset and two satellite survey cycles conducted in 2023 and 2025, we assessed how extended observation periods improve our understanding of day-to-day variability in travel behaviour.\u003c/p\u003e\u003cp\u003eThrough the application of Shannon\u0026rsquo;s entropy, the study quantifies the gains in behavioural information as a function of both observation length and sample size. The results reveal that while longer tracking windows and larger samples enhance data quality, the marginal benefits taper off beyond a certain point. Most day-to-day variability is captured within the first five weekdays, suggesting that weeklong data\u0026mdash;particularly covering typical workweek patterns\u0026mdash;is essential for accurately representing behavioural diversity, mode switching, and trip chaining that are not visible in single-day surveys.\u003c/p\u003e\u003cp\u003eHowever, rather than extending the length of core surveys\u0026mdash;an approach that risks increasing respondent burden, reducing compliance, and raising costs\u0026mdash;we demonstrate the value of using efficiently designed satellite surveys to capture this extended information. By observing a smaller, targeted sample (e.g., 600 individuals) for five weekdays, transportation agencies can generate the behavioural depth needed to support advanced modelling, policy analysis, and service design, without compromising the scale and representativeness of the core survey.\u003c/p\u003e\u003cp\u003eIn summary, this study underscores the necessity of weeklong travel data for capturing intra-individual variability in travel patterns. It supports a practical, scalable approach: a core\u0026ndash;satellite survey structure where lean, multi-day GPS-based satellite samples enhance large-scale, typical-day data. This framework ensures both statistical robustness and behavioural richness, empowering transportation planners with the insights needed for more responsive, equitable, and effective decision-making in complex urban regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe authors confirm their contribution to the paper as follows: Study conception and design: K. Wang, K.M.N. Habib; Data collection: K. Wang, M. Li, S. Afrose Suma, F. Ong, K.M.N. Habib; Analysis and interpretation of results: K. Wang, M. Li, S. Afrose Suma, F. Ong, K.M.N. Habib; Draft manuscript preparation: K. Wang, M. Li, S. Afrose Suma, F. Ong, K.M.N. Habib; Overall project supervision: K. Wang, K.M.N. Habib. All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e\u003cp\u003eThe 2025 cycle was funded by the City of Toronto, York Region, and Toronto Transit Commission (TTC). The authors are responsible for all results, interpretations, and comments the paper reports.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAschauer, F., H\u0026ouml;ssinger, R., Axhausen, K. W., Schmid, B., \u0026amp; Gerike, R. (2018). Implications of survey methods on travel and non-travel activities: A comparison of the Austrian national travel survey and an innovative mobility-activity-expenditure diary (MAED). European Journal of Transport and Infrastructure Research, 18(1).\u003c/li\u003e\n \u003cli\u003eAxhausen, K. W., L\u0026ouml;chl, M., Schlich, R., Buhl, T., \u0026amp; Widmer, P. (2006). Fatigue in long-duration travel diaries. Transportation, 34(2), 143\u0026ndash;160. https://doi.org/10.1007/s11116-006-9106-4\u003c/li\u003e\n \u003cli\u003eAxhausen, K. W., Zimmermann, A., Sch\u0026ouml;nfelder, S., Rindsf\u0026uuml;ser, G., \u0026amp; Haupt, T. (2002). Observing the rhythms of daily life: A six-week travel diary. Transportation, 29(2), 95\u0026ndash;124. https://doi.org/10.1023/a:1014247822322\u003c/li\u003e\n \u003cli\u003eArentze, T. T., \u0026amp; Timmermans, H. H. (2009). A need-based model of multi-day, multi-person activity generation. Transportation Research Part B: Methodological, 43(2), 251\u0026ndash;265. https://doi.org/10.1016/j.trb.2008.05.007\u003c/li\u003e\n \u003cli\u003eAuld, J., \u0026amp; Mohammadian. (2012). Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model. Transportation Research Part a Policy and Practice, 46(8), 1386\u0026ndash;1403.\u003c/li\u003e\n \u003cli\u003eAllstr\u0026ouml;m, A., Kristoffersson, I., \u0026amp; Susilo, Y. O. (2017). Smartphone based travel diary collection: experiences from a field trial in Stockholm. Transportation Research Procedia, 26, 32\u0026ndash;38.\u003c/li\u003e\n \u003cli\u003eAmerican Association for Public Opinion Research. (2023). Retrieved July 2, 2025, from Standards and definitions: 10th edition: https://aapor.org/wp-content/uploads/2023/05/Standards-Definitions-10th-edition.pdf\u003c/li\u003e\n \u003cli\u003eBuliung, R., Roorda, M. J., \u0026amp; Remmel, T. K. (2008). Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS). Transportation, 35(6), 697\u0026ndash;722. https://doi.org/10.1007/s11116-008-9178-4\u003c/li\u003e\n \u003cli\u003eChen, C., Gong, H., Lawson, C. T., \u0026amp; Bialostozky, E. (2010). Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study. Transportation Research Part A-policy and Practice, 44(10), 830\u0026ndash;840.\u003c/li\u003e\n \u003cli\u003eCools, D., McCallum, S. C., Rainham, D., Taylor, N., \u0026amp; Patterson, Z. (2021). Understanding Google Location History as a tool for travel diary data acquisition. Transportation Research Record Journal of the Transportation Research Board, 2675(5), 238\u0026ndash;251. https://doi.org/10.1177/0361198120986169\u003c/li\u003e\n \u003cli\u003eData Management Group. (2024). 2022 TTS: Data Guide (Issue November). https://dmg.utoronto.ca/wp-content/uploads/2024/12/2022TTS_Data_Guide-1.pdf\u003c/li\u003e\n \u003cli\u003eDianat, L., Habib, K.M.N., \u0026amp; Miller, E. J. (2017). Two-Level, Dynamic, Week-Long Work episode Scheduling model. Transportation Research Record.\u003c/li\u003e\n \u003cli\u003eGoulias, K. G., Pendyala, R. M., \u0026amp; Bhat, C. R. (2013). Keynote \u0026mdash; Total Design Data Needs for the New Generation Large-Scale Activity Microsimulation Models. Transport Survey Methods: Best Practice for Decision Making, 21-46. doi:10.1108/9781781902882-002\u003c/li\u003e\n \u003cli\u003eHanson, S., \u0026amp; Huff, O. J. (1988). Systematic variability in repetitious travel. Transportation, 15(1\u0026ndash;2).\u003c/li\u003e\n \u003cli\u003eHabib, K.M.N., Miller, E.J., Srikukenthrian, S., Lee-Gosselin, M., Morency, C., Roorda, M., \u0026amp; Shalaby, A. (2018). TTS2.0: A research and development (R\u0026amp;D) project on passenger travel survey methods. Retrieved July 03, 2020, from\u003c/li\u003e\n \u003cli\u003eHuff, J. O., \u0026amp; Hanson, S. (1986). Repetition and variability in urban travel. Geographical Analysis, 18(2), 97\u0026ndash;114. https://doi.org/10.1111/j.1538-4632.1986.tb00085.x\u003c/li\u003e\n \u003cli\u003eHanson, S., \u0026amp; Huff, O. J. (1988). Systematic variability in repetitious travel. Transportation, 15(1\u0026ndash;2). https://doi.org/10.1007/bf00167983\u003c/li\u003e\n \u003cli\u003eHorton, F. E. (1968). Geographic studies of urban transportation and network analysis. https://ci.nii.ac.jp/ncid/BA38734543\u003c/li\u003e\n \u003cli\u003eItsubo, S., \u0026amp; Hato, E. (2006). Effectiveness of Household Travel Survey Using GPS-Equipped Cell Phones and Web Diary: Comparative Study with Paper-Based Travel Survey. Transportation Research Board 85th Annual MeetingTransportation Research Board. https://trid.trb.org/view/776475\u003c/li\u003e\n \u003cli\u003eJara-D\u0026iacute;az, S., \u0026amp; Rosales-Salas, J. (2014). Understanding time use: Daily or weekly data? Transportation Research Part a Policy and Practice, 76, 38\u0026ndash;57. https://doi.org/10.1016/j.tra.2014.07.009\u003c/li\u003e\n \u003cli\u003eKuhnimhof, T., \u0026amp; Gringmuth, C. (2009). Multi-day multiagent model of travel behavior with activity scheduling. Transportation Research Record, 2134(1), 178\u0026ndash;185.\u003c/li\u003e\n \u003cli\u003eLi, M., Wang, K., Liu, Y., \u0026amp; Habib, K.M.N. (2024). Deriving weeklong activity-travel dairy from Google Location History: survey tool development and a field test in Toronto. Transportation.\u0026nbsp;\u003ca href=\"https://doi.org/10.1007/s11116-024-10523-3\"\u003ehttps://doi.org/10.1007/s11116-024-10523-3\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eLi, W., Houston, D., Boarnet, M. G., \u0026amp; Park, H. (2018). Intrapersonal day-to-day travel variability and duration of household travel surveys: Moving beyond the One-day convention. Journal of Transport and Land Use, 11(1).\u0026nbsp;\u003ca href=\"https://doi.org/10.5198/jtlu.2018.984\"\u003ehttps://doi.org/10.5198/jtlu.2018.984\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eLi, M., Wang, K., Liu, Y., \u0026amp; Habib, K.M.N. (2024). Deriving weeklong activity-travel dairy from Google location history: Survey tool development and a field test in Toronto. Transportation, 1-22. Retrieved from\u0026nbsp;\u003ca href=\"https://doi.org/10.1007/s11116-024-10523-3\"\u003ehttps://doi.org/10.1007/s11116-024-10523-3\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eLi, M., Wang, K., \u0026amp; Habib, K.M.N. (2025). Investigating recall \u0026amp; proxy bias in household travel surveys under the core-satellite fusion paradigm. Transportation. https://doi.org/10.1007/s11116-025-10654-1\u003c/li\u003e\n \u003cli\u003eKelly, P., Krenn, P., Titze, S., Stopher, P. R., \u0026amp; Foster, C. (2013). Quantifying the Difference between Self-Reported and Global Positioning Systems-Measured Journey Durations: A Systematic review. Transport Reviews, 33(4), 443\u0026ndash;459.\u003c/li\u003e\n \u003cli\u003eM\u0026auml;rki, F., Charypar, D., \u0026amp; Axhausen, K. W. (2014). Agent-based model for continuous activity planning with an open planning horizon. Transportation, 41(4), 905\u0026ndash;922. https://doi.org/10.1007/s11116-014-9512-y\u003c/li\u003e\n \u003cli\u003eMurakami, E., \u0026amp; Wagner, D. P. (1999). Can using global positioning system (GPS) improve trip reporting? Transportation research part c: emerging technologies, 7(2-3), 149-165.\u003c/li\u003e\n \u003cli\u003eMolloy, J., Castro, A., G\u0026ouml;tschi, T., Schoeman, B., Tchervenkov, C., Tomic, U., Hintermann, B., \u0026amp; Axhausen, K. W. (2022). The MOBIS dataset: a large GPS dataset of mobility behaviour in Switzerland. Transportation, 50(5), 1983\u0026ndash;2007.\u003c/li\u003e\n \u003cli\u003eMoeckel, R., Huang, W., Ji, J., Llorca, C., Moreno, A. T., Staves, C., Zhang, Q., \u0026amp; Erhardt, G. D. (2024). The Activity-based model ABIT: Modeling 24 hours, 7 days a week. Transportation Research Procedia, 78, 499\u0026ndash;506.\u003c/li\u003e\n \u003cli\u003ePougala, J., Hillel, T., \u0026amp; Bierlaire, M. (2023). From one-day to multi-day activity scheduling: Extending the OASIS Framework. 23rd Swiss Transport Research Conference. https://discovery.ucl.ac.uk/id/eprint/10174111/1/Pougala_EtAl.pdf\u003c/li\u003e\n \u003cli\u003eShen, L., \u0026amp; Stopher, P. R. (2014). Review of GPS travel survey and GPS Data-Processing Methods. Transport Reviews, 34(3), 316\u0026ndash;334.\u003c/li\u003e\n \u003cli\u003eSchlich, R., \u0026amp; Axhausen, K. W. (2003). Habitual travel behaviour: Evidence from a six-week travel diary. Transportation, 30(1), 13\u0026ndash;36. https://doi.org/10.1023/a:1021230507071\u003c/li\u003e\n \u003cli\u003eShankari, K., Bouzaghrane, M. A., Maurer, S. M., Waddell, P., Culler, D. E., \u0026amp; Katz, R. H. (2018). E-mission: An open-source, smartphone platform for collecting human travel data. Transportation Research Record: Journal of the Transportation Research Board, 2672(42), 1\u0026ndash;12. https://doi.org/10.1177/0361198118770167\u003c/li\u003e\n \u003cli\u003eSiripanich, A., Rashidi, T. H., Kalyanaraman, S., Waller, T. S., Saberi, M., Dixit, V., \u0026amp; Nair, D. (2024). Using facebook to recruit urban respondents for Smartphone-based travel surveys. Transportation Research Interdisciplinary Perspectives, 25, 101116. https://doi.org/10.1016/j.trip.2024.101116\u003c/li\u003e\n \u003cli\u003eShannon, C. E. (1948). A mathematical theory of communication. In The Bell System Technical Journal (Vol. 27, pp. 379\u0026ndash;423) [Journal-article].\u003c/li\u003e\n \u003cli\u003eTabasi, M., Siripanich, A., Khan, N. A., Auld, J., \u0026amp; Rashidi, T. H. (2024). GPS-supported smartphone app-based integrated travel diary and time-use data collection: Challenges and lessons learned. Transportation. https://doi.org/10.1007/s11116-024-10546-w\u003c/li\u003e\n \u003cli\u003eWang, K., Mashrur, S.M., Habib, K.M.N. (2024). Developing CUSTOM Framework: Explore Telecommuting-Induced Activity-Travel Demands With Mode Choice. Transportmetrica A: Transport Science, 1\u0026ndash;39. https://doi.org/10.1080/23249935.2024.2438307\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"transportation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"port","sideBox":"Learn more about [Transportation](http://link.springer.com/journal/11116)","snPcode":"11116","submissionUrl":"https://submission.nature.com/new-submission/11116/3","title":"Transportation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"household travel survey, GPS survey, Smartphone survey, Survey observation length, Survey sample size","lastPublishedDoi":"10.21203/rs.3.rs-7273201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7273201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluates the need for extended-duration travel surveys and builds the case for a core-satellite survey design in capturing multi-day travel behaviour. Leveraging the 2022 Transportation Tomorrow Survey (TTS) as the core, two satellite surveys were conducted in 2023 and 2025 in the Greater Toronto and Hamilton Area (GTHA), Canada, using GPS-based data collection methods. These satellite surveys targeted multi-day observations to assess how much additional behavioural insight can be gained beyond a typical one-day survey. This paper details the survey design and implementation of the 2025 cycle and compares travel patterns observed across the core and satellite datasets. To guide efficient multi-day survey design, Shannon\u0026rsquo;s entropy is used to quantify variability in individual travel behaviour across varying observation lengths and sample sizes. The findings reveal that while increasing both the number of observation days and the sample size enhances the informational value of the data, the marginal gains diminish beyond a certain point. Most of the day-to-day variability in travel is captured within the first five weekdays, suggesting that a five-day observation window and a sample size of at least 600 individuals can provide a sufficient balance between behavioural coverage and statistical robustness. These results support the use of targeted satellite surveys to complement large-scale, typical-day surveys in a cost-effective and methodologically sound manner.\u003c/p\u003e","manuscriptTitle":"Beyond the Typical Day: Evaluating Multi-Day Travel Behaviour Through a Core–Satellite Survey Approach in the Greater Toronto and Hamilton Area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 07:19:10","doi":"10.21203/rs.3.rs-7273201/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-21T13:15:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-05T15:32:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175385250272573824737612726321135328735","date":"2026-01-26T22:39:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179279259843367987748484244514704924709","date":"2026-01-26T09:49:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174081987992576037139625033751401547482","date":"2025-09-30T14:32:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125613261563181005879656229079114714876","date":"2025-09-24T14:56:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-24T13:48:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-12T13:21:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-02T17:29:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Transportation","date":"2025-08-01T16:42:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"transportation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"port","sideBox":"Learn more about [Transportation](http://link.springer.com/journal/11116)","snPcode":"11116","submissionUrl":"https://submission.nature.com/new-submission/11116/3","title":"Transportation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"01ecd13e-c0a7-4fce-a676-20ea49e3ae82","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T07:19:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 07:19:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7273201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7273201","identity":"rs-7273201","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}