Typology of Transit-Oriented Development to Promote Sustainable Urban Structure

Typology of Transit-Oriented Development to Promote Sustainable Urban Structure | 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 Typology of Transit-Oriented Development to Promote Sustainable Urban Structure Akbar Hamidi, Zahra Rasoulzade, Abolfazl Ghanbari, Hossein Tahmasebi Moghaddam, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6941040/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Transit-Oriented Development (TOD) has emerged as a transformative strategy in sustainable urban planning, aiming to integrate land use and public transportation to foster compact, walkable, and mixed-use communities centered around high-capacity transit systems. This study explores the spatial characteristics of TOD along the Bus Rapid Transit (BRT) corridor in Tabriz, Iran—a rapidly growing metropolitan area facing challenges such as urban sprawl, traffic congestion, and inefficient land use. The analysis focuses on four fundamental dimensions of TOD: density, diversity, design, and distance to transit stations. A 600-meter buffer zone was delineated around each BRT station, following international TOD planning standards, to assess the spatial structure and performance of the corridor. Spatial analyses were conducted using ArcGIS, while K-means cluster analysis was performed in SPSS to classify and interpret TOD typologies. The findings indicate significant spatial heterogeneity in TOD indicators across the BRT network. Four distinct TOD typologies were identified, ranging from active TOD zones with high density and land-use mix to areas lacking TOD features entirely. The study highlights the uneven integration of transit infrastructure with surrounding land uses and identifies critical zones with potential for TOD-based redevelopment. By contextualizing TOD principles in a Middle Eastern urban setting, this research contributes to the global discourse on sustainable transport and urban structure. It offers practical insights for urban planners, local authorities, and policymakers seeking to improve land use efficiency, enhance transit accessibility, and promote balanced urban growth in developing cities. Transit-Oriented Development (TOD) TOD Typology Sustainable Urban Structure Bus Rapid Transit (BRT) Urban Planning Spatial Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1- Introduction Transit-Oriented Development (TOD) has emerged as a pivotal concept in contemporary urban planning, aiming to create compact, sustainable, and livable urban environments through the integration of land use and transportation systems. As urban challenges such as sprawl, traffic congestion, environmental degradation, and inefficient land use become more pressing—particularly in rapidly urbanizing regions—TOD is increasingly seen as a viable framework for addressing these issues. The core principles of TOD, including high-density development, mixed land uses, pedestrian accessibility, and proximity to high-capacity transit systems, are designed to encourage public transportation use and reduce reliance on private automobiles (Cervero & Kockelman, 1997; Curtis et al., 2009). Over the past decade, the TOD approach has gained significant traction in both academic research and professional practice, with cities across the globe applying TOD principles to guide sustainable urban growth (Zhao et al., 2024; Abdullah et al., 2024; Arliani et al., 2024; Liu et al., 2024; Mangu et al., 2025; Ali et al., 2025). The benefits of TOD are well documented in the literature, including enhanced land use efficiency, improved transit ridership, reduced greenhouse gas emissions, revitalization of urban centers, and better quality of life (Cervero, 2013; Singh et al., 2014; Papa & Bertolini, 2015; Sung & Eom, 2024). These attributes make TOD particularly appealing for cities in developing countries that are experiencing rapid population growth, uncontrolled urban expansion, and infrastructure deficiencies. Iranian metropolises such as Tabriz face similar urban challenges, including horizontal expansion, poor transit integration, inadequate land use planning, and rising environmental and social pressures (Ghanbari et al., 2019; Shahabian & Asadi, 2017). Yet, despite growing awareness of TOD as a potential solution, the implementation of TOD principles in Iranian cities remains limited and underexplored. In particular, there is a critical research gap in the development and classification of TOD typologies in the Iranian context. Most studies in Iran have focused on general transit infrastructure or land-use patterns, without systematically analyzing the spatial characteristics and performance of TOD at the corridor or station level. To address this gap, the present study focuses on Tabriz, one of Iran’s largest and most strategically significant cities. Specifically, the research investigates the spatial patterns of TOD along the city’s Bus Rapid Transit (BRT) corridor—an emerging transit system designed to improve mobility and access across the urban fabric. Drawing on TOD literature and international best practices, this study evaluates four key dimensions of TOD: density, diversity, design, and distance to transit. These dimensions represent the “4Ds” commonly used to operationalize TOD in empirical research (Sahu, 2018; Kamruzzaman et al., 2014; Kumar et al., 2018). The core hypothesis guiding this research is that there is substantial spatial heterogeneity in TOD characteristics along the BRT corridor in Tabriz, reflecting uneven levels of land use integration, infrastructure provision, and transit accessibility. This hypothesis is grounded in the assumption that TOD performance varies across urban areas depending on contextual factors such as built environment, planning policies, and socio-economic conditions. Accordingly, the following research questions are posed: 1. What are the dominant patterns of TOD along the BRT corridor in Tabriz? 2. How do these patterns align or diverge from the indicators of sustainable urban structure? 3. Which BRT station areas show the greatest potential for TOD-based redevelopment? To answer these questions, the study employs spatial analysis using GIS tools and statistical classification through K-means cluster analysis in SPSS. A 600-meter buffer zone—consistent with international TOD standards—is applied around each BRT station to delineate the study area and capture key variables related to land use, density, and design features (Guerra et al., 2012; Sohoni et al., 2007; Kamruzzaman et al., 2014). By analyzing these data, the study develops a typology of TOD patterns that reflects the diversity and complexity of Tabriz’s urban structure. This research contributes to the growing body of TOD literature by providing empirical evidence from a Middle Eastern context, where TOD is still an emerging concept. The typological framework developed here can inform urban policymakers, planners, and transportation authorities in identifying priority areas for TOD enhancement and integrating transit planning with land use strategies. Moreover, the study offers methodological insights for conducting TOD evaluations in other cities facing similar challenges in the Global South. The remainder of the paper is structured as follows: Section 2 reviews the relevant literature and theoretical background. Section 3 describes the study area, data sources, and research methodology. Section 4 presents the results of the spatial and statistical analyses. Section 5 discusses the findings in light of TOD theory and practice. Finally, Section 6 concludes the paper with recommendations and directions for future research. 2- Research Background This section highlights key previous studies that share significant conceptual and thematic similarities with the present research. Transit-Oriented Development (TOD), with a focus on promoting sustainable urban structures, has been empirically examined in numerous studies (Sung & Choi, 2017; Ghanbari et al., 2019; Shirke et al., 2017; Noland et al., 2017; Higgins & Kanaroglou, 2016; Papa & Bertolini, 2015; Galelo et al., 2014). For instance, Sung and Choi (2019) investigated the relationship between metropolitan planning and TOD, emphasizing the Rosario–Seoul project. Their study highlights a deliberate attempt to decentralize the spatial structure of metropolitan areas by promoting transit hubs. Through the development of an integrated approach, the project aimed to address various urban problems. Similarly, Ghanbari et al. (2019) found that the proposed master plans for the Railway Center and University of Tabriz were not aligned with TOD indicators, suggesting that major spatial revisions are required to meet TOD standards. Shirke et al. (2017) argued that TOD serves as an effective tool for achieving sustainable development in densely populated Indian cities. Noland et al. (2017), examining TODs in New Jersey through both expert and resident perspectives, found a strong alignment in stakeholder views—both indicating positive impacts of TODs on traffic reduction and accessibility improvement. Higgins and Kanaroglou (2016), in their study of the Toronto metropolitan area, concluded that stations with higher TOD scores were associated with increased transit use, walking, and cycling, and lower vehicle kilometers traveled (VKT). Kumar et al. (2018) introduced a four-tier typology of TODs in Delhi, focusing on residential heterogeneity. Their results showed that TOD policies influenced residential diversity, with an average effect of 0.5 units and a 26% change in neighborhood heterogeneity. Similarly, Rodriguez and Vergel-Tovar (2018) analyzed 81 BRT stations in seven Latin American cities and identified 10 station typologies, several of which reflected typical TOD characteristics. In the context of Denver, Ratner and Goetz (2010) found that TOD implementation transformed land use and urban form. Their results showed residential developments dominating neighborhood stations, while downtown stations exhibited mixed uses including retail, office, and government buildings. Al-Hesabi and Moradi (2019) proposed a typology for metro stations in Shiraz (Metro Line 1), categorizing them into six types (A–F). Type A stations demonstrated the greatest potential to serve as TOD centers due to their dual ability to influence both city structure and adaptive capacity. Based on the review of these studies, three key observations emerge: 3- Typological Foundations: Most existing TOD typologies are grounded in physical or built environment characteristics—such as density, land-use diversity, proximity to transit, and design features. However, some recent research has expanded the scope to include socio-demographic characteristics of residents and transit users, thereby offering a more integrated analysis of TOD potential. The present study aligns with this body of literature by focusing on widely accepted TOD indicators. 4- Performance Determinants: Prior studies have consistently emphasized variables such as mode choice, accessibility, and parking supply/demand as critical determinants of TOD performance and their broader impact on sustainable urban development. However, many of these studies lack a clear, integrated framework for assessing how these TOD indicators reshape urban structure—an analytical gap that this study aims to address. 5- Iranian Context Gap: Within Iranian urban studies, TOD typology research is scarce and often lacks methodological rigor. Existing typological criteria tend to be derived from development plans with small sample sizes and limited theoretical grounding. Moreover, the role of TODs in promoting a sustainable urban structure remains underexplored. This study seeks to overcome these limitations by developing a systematic TOD typology tailored to the urban fabric of Tabriz. Undoubtedly, transit has long been one of the key pillars of spatial transformation, urban structure, and land use management, contributing to the evolution and emergence of diverse spatial organizations within cities. The recognized advantages of transit systems have led to the widespread acceptance of the Transit-Oriented Development (TOD) model as one of the most comprehensive approaches to contemporary urban development (Dargahi et al., 2016). As Rodriguez and Vergel-Tovar (2018) state, “land use strategy and transit strategy are the cornerstones of TOD,” emphasizing its crucial role in shaping sustainable urban structures. In light of the research concepts and the issues discussed in the previous sections, the conceptual framework of this study is illustrated in the following diagram (Figure 1). 3- Methodolgy 3 − 1 Study area Tabriz, the capital of East Azarbaijan province is located in the northwest, 526 km northwest of Tehran. The city is located at 46 degrees and 18 minutes’ east longitude and 38 degrees and 4 minutes’ north latitude (Fig. 2 ). The population of this city according to the census of 2016 was equal to 1508993 people (Statistics Center of Iran, 2016).Currently, Tabriz is the sixth-largest city in Iran with a population of 1.6 million after Tehran, Mashhad, Isfahan, Karaj, and Shiraz (Statistics Center of Iran, 2016). The Bus Rapid Transit (BRT) system of Tabriz metropolis consists of two routes, which were launched in September 2008. Lane One of BRT Tabriz, the main public transport corridor and the focus of this study, spans 18 kilometers. This route initially extended from the railway station in the west of Tabriz to the Abresan Crossroads in the east, and was later extended to the Tabriz International Exhibition Center at Basij Square ( https://bus.tabriz.ir , 2019). Serving as a vital west–east corridor, this BRT lane functions as a major urban artery, accommodating 27 stations along its path (Fig. 3 : Picture A). It is noteworthy that 174 cities worldwide currently operate newly established or expanded BRT systems, with Tabriz among them (Global BRT Data, 2020). Presently, 105 buses operate on this route, including 96 double-decker and 9 single-decker buses. Collectively, lines 1 and 2 transport over 100,000 passengers daily ( https://bus.tabriz.ir , 2019). Tabriz is administratively divided into 10 municipal districts, all of which are influenced by the operation of BRT routes (Tabriz Municipality, 2016). Furthermore, the new comprehensive plan of Tabriz, originally drafted in 2012 and officially approved with revisions by the Supreme Council of Urban Planning and Architecture of Iran in 2017, outlines a transit-oriented development (TOD) and node-based traffic strategy. In addition to the historical city center, the plan proposes two new urban centers and a sub-core referred to as “inner edge cities.”These areas are the west of Tabriz, Kargar Boulevard to the center of the railway station, and the area of East Tabriz and 29 Bahman Boulevard to the center of Tabriz University (Ghanbari et al, 2019). Therefore, the present study tries to measure a BRT lane in Tabriz to stabilize its structure by focusing on TOD types. Figures (3) shows a lane of a BRT in the city of Tabriz (Figure A) with its 600-meter radius (Figure B). 3 − 2 Data collection Survey and documentary methods were used to collect data for this study. Both primary and secondary data sources were utilized to describe and analyze the environment surrounding each BRT station. Primary data were gathered through field visits and direct observations at each station. Secondary data were compiled based on the concepts and principles of Transit-Oriented Development (TOD), with a specific focus on the city of Tabriz in terms of land use, population density, accessibility, infrastructure, and overall spatial structure. These data were obtained by referring to urban development organizations, comprehensive and detailed urban plans, and other official planning documents. The secondary data used in this study can be classified as follows: a) Population and employment data based on census information from statistical blocks (Iran, Detailed Results of the General Census of Population and Housing, 2016); b) Spatial datasets used to extract TOD typologies along the BRT corridor in Tabriz (Iran, Detailed Results, 2016); c) Data from the Tabriz Transit Department and the urban communication network (Tabriz and Suburbs Bus Company, 2019). The compiled dataset for each station includes demographic, employment, land use, transit, and geospatial information. In this study, a service radius of 600 meters was defined for each BRT station. This decision aligns with most prior TOD research, which identifies a 600-meter buffer as a standard functional range for TOD centers (Kamruzzaman et al., 2014). Other studies have suggested different radii, such as 800 meters (Galelo et al., 2014; Gu et al., 2019) or even 1000 meters, which some researchers consider more appropriate for capturing broader urban structural characteristics (Chen et al., 2017). According to Seruro (2004) and Guerra et al. (2012), a half-mile buffer (approximately 800 meters) is commonly used as a standard for assessing walkability and bicycle accessibility in TOD studies. Evans (2007) proposed a TOD buffer ranging between 400 and 800 meters. Ultimately, this study adopted a 600-meter radius—hereafter referred to as the TOD buffer—as the service area for each BRT station. This range is consistent with TOD planning and design guidelines commonly applied to bus and rail stations in Iran and reflects the average distance for pedestrian and bicycle access 3–3 Designing Indicators and Research Analysis Methods For this study, four main parameters have been defend of our current case study(Table 1). Table 1. Framework of indicators for assessing TOD in terms of sustainable urban structure TOD Principle Indicator Formula and Evaluation Method References Density Population density Number of inhabitants per hectare Nasri & Zhang, 2014; Sohoni et al., 2017; Galelo et al., 2014; Sahu, 2018; Ewing et al., 2017; Cervero et al., 2004; Kumar et al., 2018; Translink, 2012; Gu et al., 2019; Reusser et al., 2008; Ogra & Ndebele, 2014; Al-Harami A. F., 2020; Ratner & Goetz, 2013 Employment density Net employment density calculated as the number of jobs per unit area of employment-generating land uses (e.g., commercial, industrial) within designated radius Same as above Residential density Number of people in the designated radius per hectare of residential area Galelo et al., 2014; Kumar et al., 2018; Translink, 2012; Kamruzzaman et al., 2014; Reusser et al., 2008; Ogra & Ndebele, 2014; Al-Harami A. F., 2020; Ratner & Goetz, 2013 Number of residential units Number of housing/apartment units intended for family living Riggs & Chamberlain, 2018; Kumar et al., 2018; Translink, 2012; Zaina et al., 2016 Commercial space Percentage of commercial land area to total land area within designated radius Riggs & Chamberlain, 2018; Translink, 2012; Ogra & Ndebele, 2014 Diversity Mixed land use Entropy index coefficient Singh et al., 2014; Sohoni et al., 2017; Sahu, 2018; Cervero & Kockelman, 1997; Ewing et al., 2017; Higgins & Kanaroglou, 2016; Kumar et al., 2018; Translink, 2012; Nasri & Zhang, 2014; Kamruzzaman et al., 2014; Gu et al., 2019; Reusser et al., 2008; Ogra & Ndebele, 2014; Al-Harami A. F., 2020; Zaina et al., 2016; Ratner & Goetz, 2013 Design Bicycle routes Presence or absence of a dedicated bike path Sohoni et al., 2017; Cervero & Kockelman, 1997; Translink, 2012; Kamruzzaman et al., 2014; Gu et al., 2019; Schlossberg & Brown, 2004; Ogra & Ndebele, 2014; Al-Harami A. F., 2020 Other design elements (e.g., parking) Counting the number of parking lots Staricco & Brovarone, 2018; Higgins & Kanaroglou, 2016; Kumar et al., 2018; Ewing et al., 2017; Al-Harami A. F., 2020 Distance Distance in meters or miles to station Accessible area determined by a 10-minute walking distance using network analysis in ArcGIS Papa & Bertolini, 2015; Sahu, 2018; Ewing et al., 2017; Cervero et al., 2004; Translink, 2012; Gu et al., 2019; Schlossberg & Brown, 2004; Ogra & Ndebele, 2014 Reference: (Authors) The stations studied as "transit-oriented development" have been selected based on cluster analysis to select the typology of transit-oriented development centers using the four main pillars of urban structure and TOD, namely density, diversity, design, and distance (D). Spatial analysis was performed in Arc GIS version 10.3; In the first stage, using the information received from the responsible institutions such as the municipality and the General Directorate of Roads and Urban Development in the form of urban maps 2000/1, GPS mapping, Google Earth images, and Cad Map, Global Mapper and ArcGis software, communication network map of Tabriz (main and secondary) was drawn and after preparing the data (georeferencing and topology), it was entered into Arc Catalo software to convert it into a network structure. In the second step, the buffers in GIS were calculated using direct distances and all or none functions instead of other methods of calculating distances such as network distance and distance reduction functions discussed in the relevant literature. Finally, in the mentioned software, a network structure was created by defining relationships and using the functions of network speed, network length, network type, apparent network resistance, and network barriers. Then, in Arc Map software, the initial analysis was performed using the New Service Area command in the Analyst Network menu. In this paper, in addition to the characteristics of the stations, the relationship between the major TOD indices was measured through K-mean cluster analysis tools in SPSS software. In the cluster analysis method, an attempt is made to divide the observations into homogeneous groups, so that the observations of all groups are similar to each other and have the least similarity with the observations of other groups (Akbari & Zahedi, 2008). Therefore, in this study, the analysis of these clusters was used to determine how the stations can be grouped to minimize the internal heterogeneity of the clusters and maximize the heterogeneity between the groups. As a result, not only the dominant patterns of TOD but also its impact on the spatial structure of Tabriz was determined. 4- Results 4- 1 Analysis of TOD Indicators: Land Use Pattern, Entropy, and Density Distribution The main findings of the study to measure the type and level of TOD in the urban structure of Tabriz according to the indicators discussed (Table 1) were obtained in the form of tables, charts, and maps that are mentioned below. First of all, it is necessary to study land use pattern within study area, and through it, “propose the redevelopment to transform the land use into transit supportive form” (Shirke et al., 2017). In connection with this issue, the highest entropy coefficient among all stations of Tabriz BRT line 1 was estimated for Tabriz University stations (0.77) and Basij terminal (0.75), respectively. In contrast, the lowest coefficients belong to the alley station stations (0.28) and the pole square (0.29) (Figure 5). Therefore, the majority of landfills are in the "0.35 -0.65" range, which most of the stations in the route, and most of the TODs with mixed-use are observed in the middle tissues (Table 2). But the central sector lacks these features despite tackling the many challenges of access, traffic, and lack of public services. The second indicator that is more emphasized in the literature is population density. According to the estimates made in a radius of 600 meters per person per hectare, among the 26 stations, the highest calculated density was observed for the stations of Khatib (42.53) and Kalantar Alley (36.21). In this regard, the lowest number of people per hectare was calculated for the end stations of Basij (0) and Tehran Road (0.44), respectively. On the one hand, the density in question has been very low in stations with a marginal and new location. On the other hand, the functions of centralization have led to a lack of population development in some central context stations (Figure 5). Regarding the housing density index, the following points can be noted: First, a total of four stations with a population of more than 100 people per hectare, including 29 Bahman Hospital (414.22 people/hectare), Jam Jam (322.63 people/hectare), Tehran Road (135.27) N / ha) and guidance (125.21 n / ha). Second, 5 stations with less than 30 people per hectare were obtained: Basij Terminal (0), Shariati Crossroads (21.82 people / hectare), Ferdowsi High School (24.07 people / hectare) and Shahid Beheshti (27.63 people / hectare). Third, of the other stations, 11 were distributed in the "50-30" cluster and 6 in the "50-100" cluster. Therefore, more than 60% of the study area does not have the desired residential density for transit-oriented development. As can be deduced from Table (2), the bicycle path in a radius of 600 meters is not designed for stations and can be seen only in a small number of major centers such as Saat Square station and Shahid Beheshti (Mansour) crossroads with a limited area. Table 2. Estimation and initial calculation of the studied indicators in the current situation Station name entropy population density Residential density bike route Commercial space Building Density Employment density pedestrian access Residential unit Parking Rah Ahan 0.65 21.5 49.83 0 2.34 99.89 0.13 23636 5354 6 Darya 0.49 34.47 51.4 0 3.22 109.32 0.12 23827 7931 4 Khatib Crossroads 0.35 42.53 53.45 0 3.95 131.67 0.12 24494 9836 0 Ashkan 0.49 32.07 47.08 0 6.91 146.27 0.09 24494 9361 1 Sherkat Naft 0.6 32.05 57.46 0 8.2 142.71 0.09 24866 7625 1 Silo 0.6 29.12 54.36 0 7.27 115.91 0.1 25032 7111 1 Ghatran 0.5 34.99 54.28 0 7.2 117.48 0.1 25624 8182 3 Bazar Milad 0.39 30.6 41.78 0 11.07 113.93 0.1 25624 5376 1 Golestan Garden 0.36 17.84 34.81 0 23.02 129.81 0.12 25685 4510 2 Ferdowsi High School 0.29 9.71 21.82 0 24.59 146.89 0.13 25315 3106 7 Shariati Crossroads 0.61 9.71 21.28 0 24.29 171.2 0.13 24831 2894 8 Saate Square 0.65 10.43 24.07 0 20.1 142.18 0.12 24824 5374 12 Shahid beheshti 0.47 18.45 27.63 0 11.01 133.02 0.12 23892 7304 2 Haj Ahmad Mosque 0.38 22.85 30.6 0 9.61 133.84 0.11 23456 8953 0 Ghotbe Square 0.29 31.26 37.75 0 6.28 127.14 0.09 22579 10016 2 kallantar Alley 0.28 36.21 43.51 0 5.57 134.19 0.09 21905 4450 3 Abrasan Intersection 0.61 23.83 42.9 0 8.64 176.88 0.12 20104 3192 2 Tabriz University 0.77 14.78 73.25 0 3.94 167.57 0.11 19777 2248 1 Jame Jam 0.64 10.39 322.63 0 1.9 140.4 0.09 19047 1689 3 29 Bahman Hospital 0.73 15.48 414.23 0 1.07 36.52 0.08 15288 1095 1 Rahnamayi 0.72 17.76 125.21 0 3.06 61.92 0.1 13108 1327 3 Azari 0.66 20.93 49.81 0 14.47 127.16 0.11 11133 3684 2 Serahi Valiasr 0.54 24.37 38.32 0 12.47 144.29 0.09 10792 5651 0 Ostad Shahriyar 0.66 16.17 39.29 0 4.01 136.31 0.08 9598 4828 0 Jade Tehran 0.62 0.44 135.27 0 1.95 38.29 0.04 6390 21 0 Payene Basije 0.75 0 0 0 7.11 9.82 0.06 3013 0 0 Resource: (Research finding, 202) 4-2 Evaluation of Commercial Intensity, Building Density, and Employment Distribution Across TOD Zones In the next step, the commercial space index was calculated, in which the stations of the central part of the city (CBD) benefited from a higher ratio than the middle and marginal stations. For example, the highest coefficients are assigned to Ferdowsi High School stations (24.59) and Shariati Crossroads (24.29), both of which belong to the central context. Also, it seems that the coefficients of commercial space are relatively completely adapted to the location of most stations (Table 2). Another indicator that is used to measure the degree of compaction and density around public transit centers is building density. The index was examined in four categories; A) less than 50%; B) 100-50%; C) 150-100%: Most of the stations analyzed are in this scale, the most important of which are Ferdowsi High School (146.89%), Ashkan (146.27%), Valiasr Highway (144.29%) and Saat Square (142.18%), respectively. D) More than 150%: The most important urban nuclei of Tabriz are stations of this category, which include Abersan crossroads (176.87%), Shariati crossroads (171.20%), and Tabriz University (167.57%). Therefore, it is worth mentioning a few points that, firstly, the stations in the western part have a higher building density than the eastern part (marginal and new textures). Second, the main centers that play a role as multi-core centers in the spatial structure of the city of Tabriz, have an acceptable level of compaction and density. Another indicator is the employment density, which among the studied stations, the best situation is related to Shariati, Ferdowsi High School and Railway stations with a coefficient of 0.13. In contrast, the lowest coefficients for Tehran Road and Basij Square stations were estimated at 0.04 and 0.06, respectively. To calculate the pedestrian access index, the distance traveled and the distance from the center of the neighborhood to the desired station were taken into account. Findings indicate that in most places pedestrian access to stations is significantly different from the standard access radius. Finally, the number of residential units that show the tangible dimensions of residential development around the stations was calculated (Table 2). Parking was also an important design element in this study. The findings indicate that there are more in the central part of the city. Some stations such as Khatib, Valiasr, Ostad Shahriyar, Tehran Road and Basij terminal do not have parking within a radius of 600 meters of BRTs (Figure 6). 4-3 TOD Typology Based on K-Means Clustering: Classification and Comparative Analysis of BRT Station Characteristics At this stage, based on the information obtained from the study of the characteristics of bus stations through K-mean cluster analysis, the typology and analysis of TODs were performed. Since this method assumes that the data are classified into a specified number of clusters (Habibpour & Safari Shali, 2009). Therefore, following the common typology in the cities of the developing and developed world, the number of clusters was limited to 4. To achieve specific results in the first place by updating the cluster centers frequently, the initial typology of the stations was obtained by considering the discussed indicators (Table 3). As can be seen, four clusters are formed for the TOD quantity. Euclidean distance method was used to determine the distance between two indicators. Table (3) shows the initial average of each indicator within each cluster. More precisely, preliminary comparisons have been made between BRT stations in terms of TOD indices in Tabriz. Due to the quality of TOD, the imbalance of residential density and building density between the four clusters in the service radius of BRTs is relatively high. Besides, in all clusters in terms of design, the areas around the BRT stations are not suitable for pedestrians considering the access distance, parking amount, and quality of the sidewalk and bicycle. But clusters are one exception. Thus, in more than half of the cluster elements, the radius around BRT stations has higher street and highway densities and less cycling and walking capacity. In research also points to the inadequacy of pedestrian capacity in the radius of the stations and also emphasizes the lack of complimentary bus services in the BRT systems of Chinese cities. Nearly 30% of stations experience land use uniformity around the station (low diversity). Also, low residential density is seen in half of the clusters and unfavorable population, and construction density is seen concerning most of the BRT stations in Tabriz (Table 3). Therefore, the common problem in the field of transit-oriented development of Asian cities (Sahu, 2018) also applies to Iranian cities. Table 3. Initial Cluster Centers Clusters Indicators 4 3 2 1 0.29 0.29 0.75 0.64 Entropy 9.71 31.26 0.00 10.39 Population density 21.82 37.75 0.00 322.62 Residential density 0.00 0.00 0.00 0.00 Bike route 24.59 6.28 7.11 1.90 Commercial space 146.89 127.14 9.82 140.40 Building density 0.13 0.09 0.06 0.09 Employment density 7.00 2.00 0.00 3.00 Parking ratio 3106.00 10016.00 0.00 168.900 Residential unit 25315.00 22579.00 3013.00 19047.00 Pedestrian access Source: (Research Findings, 2020) In the next step, after 10 repetitions, the centers of the clusters did not change and the average of the clusters reached an acceptable level. The maximum change in absolute coordinates for each center is 0.057. The current repetition is 10. The minimum distance between the primary centers is 6433.255. As a result, to achieve a suitable typology, the degree of similarity of 26 BRT stations in Tabriz was compared through a clustering algorithm. The values obtained show how much the center size of all four clusters has changed for each repetition. Table (4) indicates which cluster membership of each station. For example, Golestan Garden Station is located in Cluster 1, Qatran Station in Cluster 2, 29 Bahman Hospital Station in Cluster 3, and Tehran Road Station in Cluster 4. It is clear that the distance between each station is less with itself (convergence and similarity) and with other clusters is greater (divergence and difference). Since the distance of most of the analyzed stations from the center of their cluster is less, it indicates the suitability and comprehensiveness of the cluster for its elements. Therefore, based on the distance (third and sixth columns of Table 4), the highest distance and the lowest amount belong to Khatib (7890.117) and Oil Company (914.554) intersection stations in cluster three, respectively. Table 4. Cluster membership Station brt Cluster interval Station brt Cluster interval Rah Ahan 1 5438.160 Haj Ahmad Mosque 3 7755.962 Darya 3 541.516 Ghotbe Square 3 2814.148 KhatibCrossroads 3 7890.117 kallantar Alley 3 4910.576 Ashkan 3 1569.117 Abrasan Intersection 1 7298.253 Sherkat Naft 3 914.554 Tabriz University 1 3677.911 Silo 3 2632.265 Jame Jam 1 5499.619 Ghatran 3 2981.010 29 Bahman Hospital 2 4275.328 Bazar Milad 3 3250.225 Rahnamayi 2 2418.215 Golestan Garden 1 3336.131 Azari 2 1548.202 Ferdowsi High School 1 5591.993 Serahi Valiasr 2 4694.241 Shariati Crossroads 1 5448.378 Ostad Shahriyar 2 3986.412 Saate Square 1 4848.384 Jade Tehran 4 1703.853 Shahid beheshti 1 4731.520 Payene Basije 4 1703.795 Source: (Research Findings, 2020). Following the previous steps, the final cluster centers were identified after the clustering iteration algorithm. In the final cluster centers stage, the average of the ten research indices was reflected within each of the four clusters. The mean values obtained in the last stage indicate that it is high in clusters 3 and 1 in most indices. In other words, in clusters three and one, the indicators of transit-oriented development have appeared better than clusters four and two. Finally, by comparing the table of initial and final cluster centers, it can be said that after passing the iteration algorithm, the centers of the clusters are closer to each other. Here, where the Euclidean distances between the centers of the final cluster are expressed (Table 5), we see that there is a dissimilarity between the clusters. For example, the distance between clusters one and four is 22941.327. Table 5. Distances between Final Cluster Centers Cluster 1 2 3 4 1 * 11495.695 15369.260 22941.327 2 11495.696 * 17451.063 18265.217 3 15369.260 17451.063 * 35041.412 4 22941.327 18265.217 35041.412 * Source: (Research Findings, 2020). 4-4 Statistical Validation of TOD Clusters Using ANOVA Analysis In the next step, the results of ANOVA test indicate the heterogeneity and similarity of clusters, the level of significance, and the role of each of the ten indicators in the clustering of BRT stations in Tabriz. As a result, pedestrian access indicators, number of housing units, employment density, and building density have played the most important role. In contrast, bicycle path indicators, population density, and commercial space with coefficients of 0.235, 0.978, and 1.582 had the lowest share in determining the clusters, respectively. At the 5% error level, only three variables (low performance) were not significant (Table6). Therefore, the clusters are completely different from each other and the results are acceptable. Table 6. ANOVA test principles Indicators Error [1] F Sig Mean Square df Mean Square df entropy 0.057 3 0.018 22 3.144 0.046 population density 606.006 3 56.476 22 10.730 0.000 Residential density 8375.923 3 8562.361 22 0.978 0.421 bike route 0.019 3 0.081 22 0.235 0.871 Commercial space 72.937 3 46.118 22 1.582 0.222 Building Density 8599.035 3 756.370 22 11.369 0.000 Employment density 0.002 3 0.000 22 13.082 0.000 Parking 22.993 3 6.342 22 3.625 0.029 Residential unit 62277252.17 3 1962521.068 22 31.733 0.000 pedestrian access 347916557.1 3 4288731.768 22 81.123 0.000 Source: (Research Findings, 2020). In other words, the significance value (Sig.) is very small for both criteria (P-value < 0.001), indicating that the assumption of homogeneity among clusters is rejected. This confirms the statistical validity of the clustering results. In the final step, the number of stations in each cluster was determined (Table 7). Based on the derived coefficients, mean values, and the implementation of the K-means cluster analysis procedure, the distribution of BRT stations in Tabriz was classified into four major clusters. The first cluster includes nine stations: Rah Ahan, Golestan Garden, Ferdowsi High School, Shariati (Shahnaz) Intersection, Saat Square, Shahid Beheshti (Mansour), Abresan Intersection, Tabriz University, and Jam-e Jam. Together, they comprise approximately 35% of the BRT stations in Tabriz. The second cluster includes five stations: 29 Bahman Hospital, Rahnamayi, Azari, Serahi Valiasr, and Ostad Shahriyar. These stations account for approximately 19% of the total.The third cluster comprises a larger group: Darya, Khatib Crossroads, Ashkan (Halfway), Sherkat Naft, Silo, Ghatran, Milad Market, Haj Ahmad Mosque, Ghotbe Square, and Kalantar Alley. This cluster represents over 38% of the stations Finally, the fourth cluster consists of only two stations—Jadeh Tehran (Tehran Road) and Payaneh Basij (Basij Terminal)—which together account for less than 8% of all stations studied (refer to Tables 4 and 7). Table 7. Number of stations in each cluster concepts Row Abundance percent Clusters 1 9 2 5 3 10 4 2 Authentic items 26.000 Missing items 1.000 Source: (Research Findings, 2020) By summarizing the results obtained from the multivariate clustering analysis, alongside established concepts and classification criteria for Transit-Oriented Developments (TODs), the four clusters identified in this study can be named and interpreted as follows: The first cluster includes most of the major and strategic BRT stations in Tabriz. These stations are surrounded by high-density, compact areas featuring a mix of administrative-institutional, educational, healthcare, commercial, residential, and tourism-related land uses. For example, landmarks such as Imam Reza Hospital, the University of Tabriz, cultural centers, renowned bookstores, high-end service towers (e.g., Bolour Tower, Abrisham Tower), international hotels, the Tabriz Indoor Bazaar, Golestan Garden, historical museums, the Alishah Citadel, the Imam Khomeini Prayer Hall, and the Tarbiat Pedestrian Street are located within a 600-meter service radius. These diverse functions make this cluster highly dynamic and active. Notably, most of these stations are situated in the historic core of Tabriz, which includes UNESCO World Heritage Sites such as the Tabriz Bazaar. Furthermore, this cluster spans the entire BRT corridor from west to east, making it the only TOD type with a continuous presence. Accordingly, this group is best classified as an Active TOD. The second cluster, consisting of five stations, is mainly distributed across the eastern and central parts of Tabriz. These stations are located near affluent residential neighborhoods, including the Valiasr Dormitory area, and benefit from robust communication infrastructure, elevated highways, government institutions, and large-scale real estate developments. These factors have positively influenced several TOD indicators. The stations in this cluster exhibit a growing degree of compaction and relatively strong values for residential, population, and employment densities, along with moderate accessibility and design characteristics. As such, this group can be referred to as a Potential or Balanced TOD. The third cluster, comprising 10 stations, represents the largest segment of the study area. It is characterized primarily by residential land use, with a mix of high-density housing developments, yet it suffers from limited and incomplete sidewalk infrastructure, a shortage of quality public spaces, and relatively weak orientation to BRT services. Most of these stations are located between the central and western portions of the urban structure of Tabriz. Given the dominance of housing-related functions, this cluster is appropriately categorized as a Residential TOD. The fourth cluster includes only two stations—Tehran Road and Payaneh Basij. These areas are marked by industrial and workshop zones, mixed-use commercial factories, and extensive vacant or undeveloped land within the 600-meter buffer. The absence of essential TOD features and supporting infrastructure renders these stations ineffective as transit-oriented nodes. Consequently, this cluster is classified as Non-TOD.The spatial distribution of these four clusters and their associated TOD types within the Tabriz BRT network are visually represented in Figure 7, based on the analytical results discussed. The typology of BRT stations, based on the type of urban context, illustrates their spatial and geographical distribution within the urban structure of Tabriz. This classification reveals notable insights into the compatibility between the service radii of the studied stations and the core indicators of Transit-Oriented Development (TOD). From a comprehensive perspective, four distinct urban textures can be identified to evaluate the influence of BRT on the sustainable development of Tabriz: a) Central Texture: This area includes seven stations, of which five (Ferdowsi, Shariati Intersection, Saat Square, and Shahid Beheshti Intersection) correspond to the Active TOD type. The remaining two stations (Haj Ahmad Mosque and Milad Bazaar) align with the Residential TOD type. Altogether, this texture accounts for approximately 27% of the total stations analyzed. b) Transition Zone: This zone includes three stations, all of which display the characteristics of Residential TOD. Approximately 11% of Tabriz’s BRT stations fall within this urban texture. c) Middle Texture: This is the largest and most extensive urban zone in terms of both area and the number of BRT stations. It contains 14 stations, representing more than 54% of the stations in the study area. Of these, five exhibit Residential TOD characteristics, five are classified as Potential TODs, and four demonstrate Active TOD capabilities. This texture plays a crucial role in shaping the TOD landscape of Tabriz. d) Peripheral and Suburban Texture: This texture includes the only two stations classified as Non-TODs—Basij Square and Tehran Road. These peripheral stations serve less than 8% of the total BRT network and are characterized by weak integration with TOD principles. The spatial distribution and classification of these urban textures, along with their associated TOD types, are visually represented in Figure 8. 5- Discussion and Conclusion The analysis of the selected indicators clearly demonstrates that TOD (Transit-Oriented Development) patterns across different segments of the BRT network in Tabriz vary significantly, affecting the urban structure in multiple dimensions. These patterns range from active TODs (such as in central areas and western entry points of the city) to areas lacking TOD features (notably the eastern gateways of Tabriz). The most notable finding is the stronger impact of TOD in the city center, where land use diversity and urban compactness dominate. This observation aligns with the findings of Ratner and Goetz (2013) regarding the city of Denver. Moreover, the study reveals substantial differences between the four clusters, indicating a lack of homogeneity. Based on the degree of similarity and heterogeneity among the indicators, BRT stations in Tabriz are classified into four primary clusters: (1) Active TOD, (2) Potential TOD, (3) Residential TOD, and (4) Lack of TOD. Similar classifications are found in previous studies (Kamruzzaman et al., 2014; Kumar et al., 2018), albeit with minor differences. A critical point is the inconsistency and stark contrasts in urban contexts across Tabriz concerning TOD functionality. The central areas show greater readiness for TOD development compared to suburban and mid-urban textures. Although many attractions, residential neighborhoods, and multifunctional complexes are located outside the city center, these areas predominantly rely on private vehicles, with limited access to transit-oriented centers. The study further highlights that TOD development is concentrated in the historical and central areas of Tabriz, while marginal areas suffer from poor transit infrastructure and access. In contrast, new and peripheral neighborhoods are dominated by car-dependent infrastructure. Notably, about half of Tabriz’s BRT stations fall into the latter category, suggesting that the TOD framework in the city is far from stable and necessitates a reassessment of current transit policies and urban development approaches. The BRT’s east-west alignment limits its integration into the overall urban structure. As suggested by Sung and Choi (2017), TOD policy should also be applied at broader regional scales. Land use zoning along the BRT corridor varies: the eastern part features a mix of residential, commercial, and vacant land; the central part is dominated by mixed-use development; and the western part emphasizes commercial, residential, warehouse, and parking uses. Mixed land use plays a crucial role in shaping the four TOD clusters, similar to the role it has played in TODs identified in Latin America, the United States, and Singapore (Rodriguez & Vergel-Tovar, 2018; Cervero & Murakami, 2009; Asghar et al., 2025; Moberg, 2025). This study aimed to assess employment density, residential density, and mixed land use patterns across the east-west corridor of Tabriz to enhance and optimize these variables. In line with the findings of Sahu (2018), this research prioritizes density and diversity, which have a significant influence on urban form and functionality (Riggs & Chamberlain, 2018; Papa & Bertolini, 2015; Ewing et al., 2017). Overall, Residential TOD emerged as the most prevalent development type in the study area, while the University of Tabriz station was identified as the most prominent example of an Active TOD. Nonetheless, this study has certain limitations that future research should address. First, the use of a 600-meter service radius, while consistent with previous research, may not fully capture TOD functionality compared to larger radii (e.g., 1000 meters), possibly resulting in limited findings. Second, TOD typologies were determined using mean values and cluster centroids, which may not comprehensively reflect the complex spatial realities of Tabriz. Third, demographic characteristics of users were not incorporated into the typology, as the study focused on urban structural indicators with accessible quantitative data. Future studies should consider integrating demographic variables with physical indicators for a more holistic evaluation. Finally, because the current analysis is limited to the east-west corridor of Tabriz, its conclusions do not extend to the entire urban network. However, the TOD typology is expected to evolve substantially with the planned implementation of a north-south BRT line and the full operation of the Tabriz metro system. Abbreviations Abbreviation Full Term TOD Transit-Oriented Development BRT Bus Rapid Transit CBD Central Business District SPSS Statistical Package for the Social Sciences GIS Geographic Information System GPS Global Positioning System VKT Vehicle Kilometers Traveled ANOVA Analysis of Variance DTP Detailed Transportation Plan (implied in “comprehensive and detailed”) ArcGIS A GIS software for working with maps and geographic information BRT Lane Specific route or corridor designated for Bus Rapid Transit Declarations Funding Declaration : The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution Author Contributions: Akbar Hamidi and Zahra Rasoulzadeh contributed to the conceptual design, data collection, and analysis. Akbar Hamidi , Zahra Rasoulzadeh Abolfazl Ghanbari, Hossein Tahmasebi Moghaddam, Golzar Einali, and Khalil Gholamnia were responsible for GIS modeling, statistical analysis, and visualization. Khalil Gholamnia, Omid Ghorbanzadeh, and Sarbast Moslem supervised the research, reviewed the manuscript, and contributed to the interpretation of the results. All authors contributed to writing, reviewing, and approving the final manuscript. Acknowledgement The authors would like to express their gratitude to the Tabriz Urban Planning and Transportation Organization and the Tabriz and Suburbs Bus Company for providing access to valuable spatial and transit-related data. We also appreciate the support of colleagues and academic mentors who contributed insightful feedback during the development of this research. Special thanks are extended to Charles University, Prague, and the University of Natural Resources and Life Sciences, Vienna (BOKU), for their academic support and institutional collaboration that enriched the quality of this study. References Abdullah, R., Xavier, B. D., Namgung, H., Varghese, V., & Fujiwara, A. (2024). 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The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster means are equal. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6941040","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478223940,"identity":"8e25f73a-33a3-448f-9053-b5f93b0ca98f","order_by":0,"name":"Akbar Hamidi","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Akbar","middleName":"","lastName":"Hamidi","suffix":""},{"id":478223941,"identity":"6def4fec-1d41-46ab-bdf6-2567689f736f","order_by":1,"name":"Zahra Rasoulzade","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Rasoulzade","suffix":""},{"id":478223942,"identity":"f4a44b40-98d9-40f2-82e3-f022c6a92d83","order_by":2,"name":"Abolfazl Ghanbari","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Abolfazl","middleName":"","lastName":"Ghanbari","suffix":""},{"id":478223943,"identity":"7fde44bb-80a4-41ca-8e31-f037e49459b0","order_by":3,"name":"Hossein Tahmasebi Moghaddam","email":"","orcid":"","institution":"University of Zanjan","correspondingAuthor":false,"prefix":"","firstName":"Hossein","middleName":"Tahmasebi","lastName":"Moghaddam","suffix":""},{"id":478223944,"identity":"b84a874c-ecaf-40ca-beac-67888b4ff314","order_by":4,"name":"Golzar Einali","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Golzar","middleName":"","lastName":"Einali","suffix":""},{"id":478223945,"identity":"d8f33704-236e-4cb7-9d73-e5c6540d0e95","order_by":5,"name":"Khalil Gholamnia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie2QOwoCMRCGs9j6aGMhe4UJC1t5E5uI4DZpvEFgW8V2BQ9htZXFhIBnWHALRdBGQTsFBeMDFcFoaZGvGUjmy58ZQhyOf4QSD02ByvPIk98UclWq8tH6qwL4tfWO34gRDyIPgmm8WpTGxIdpLMn++Flh+YSrXroMw3zC4uKSsFGupNctWpREAJZSXQ8zbhQkHLKmLFz+a1PUyShBEu1eFLDMQgVokxICFa8p/LMCtM11LdUBzURnMETKBkZRXbSkJC0136Sa9ZNotF1j3S9n0Xxm2xi8PXcb25JhUqTt1uFwOBwXzo96WnqyX185AAAAAElFTkSuQmCC","orcid":"","institution":"Charles University","correspondingAuthor":true,"prefix":"","firstName":"Khalil","middleName":"","lastName":"Gholamnia","suffix":""},{"id":478223946,"identity":"9e7d440f-9324-41e8-85e9-8314bb9a247b","order_by":6,"name":"Sarbast Moslem","email":"","orcid":"","institution":"Trinity College Dublin, the University of Dublin","correspondingAuthor":false,"prefix":"","firstName":"Sarbast","middleName":"","lastName":"Moslem","suffix":""},{"id":478223947,"identity":"c8fa71cf-ec22-40f7-add8-44f8366e3e05","order_by":7,"name":"Omid Ghorbanzadeh","email":"","orcid":"","institution":"University of Natural Resources and Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Omid","middleName":"","lastName":"Ghorbanzadeh","suffix":""}],"badges":[],"createdAt":"2025-06-20 18:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6941040/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6941040/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85841899,"identity":"b0274561-456a-4948-9365-a3a555fa41ec","added_by":"auto","created_at":"2025-07-02 09:12:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":118607,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Model of Research (Authors).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6941040/v1/ec39336088b4d45d64e633d7.png"},{"id":85841902,"identity":"9e1da93a-f7d2-404d-b79b-73a0ce915ae1","added_by":"auto","created_at":"2025-07-02 09:12:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":294142,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical Location of Tabriz.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6941040/v1/4a7d3091099dc619c8bcd9ae.png"},{"id":85841915,"identity":"2e5bb080-184d-44a2-a35e-7de350a324e8","added_by":"auto","created_at":"2025-07-02 09:12:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":749808,"visible":true,"origin":"","legend":"\u003cp\u003eLane 1 of a BRT in Tabriz (Figure A); Land use in 600-meter radius of BRT network (Figure B).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6941040/v1/84bc107ca7b0a04c675cf486.png"},{"id":85842245,"identity":"6aca67ba-7656-4aaa-84ca-0e7c3224f918","added_by":"auto","created_at":"2025-07-02 09:20:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":156759,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Methodological Process (Authors)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6941040/v1/5e26710e94c5debeb88234e7.png"},{"id":85844474,"identity":"c2d16159-4889-4ef5-9e91-9e754d6a9b8c","added_by":"auto","created_at":"2025-07-02 09:28:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":713278,"visible":true,"origin":"","legend":"\u003cp\u003eLand use diversity using entropy index (Figure A) and population density (Figure B).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6941040/v1/4bfe16cfaf9cb8270b189741.png"},{"id":85841907,"identity":"c46f1d66-0165-441d-9119-a77e219641fb","added_by":"auto","created_at":"2025-07-02 09:12:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":994648,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution and dispersion of parking lots in Tabriz\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6941040/v1/bca4603c2b3808b087e2c7c9.png"},{"id":85841918,"identity":"f4324a1e-b168-4386-9ab7-e14e6d952b1f","added_by":"auto","created_at":"2025-07-02 09:12:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":743734,"visible":true,"origin":"","legend":"\u003cp\u003eTOD typology map of Tabriz based on function\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6941040/v1/44016fa6375d751b9cff30c2.png"},{"id":85841905,"identity":"f4c2df2c-df90-408a-979f-df44ced07007","added_by":"auto","created_at":"2025-07-02 09:12:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":858655,"visible":true,"origin":"","legend":"\u003cp\u003eClassification map of Tabriz TODs according to scale and types of urban context\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6941040/v1/592593c3f7cb2f53fc3f13ed.png"},{"id":93358055,"identity":"e5b7393d-3e3f-4268-b4bb-a2576f810c2b","added_by":"auto","created_at":"2025-10-13 02:16:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5274076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6941040/v1/abdfb673-d348-4835-9f89-8ce5690f2c27.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Typology of Transit-Oriented Development to Promote Sustainable Urban Structure","fulltext":[{"header":"1- Introduction","content":"\u003cp\u003eTransit-Oriented Development (TOD) has emerged as a pivotal concept in contemporary urban planning, aiming to create compact, sustainable, and livable urban environments through the integration of land use and transportation systems. As urban challenges such as sprawl, traffic congestion, environmental degradation, and inefficient land use become more pressing\u0026mdash;particularly in rapidly urbanizing regions\u0026mdash;TOD is increasingly seen as a viable framework for addressing these issues. The core principles of TOD, including high-density development, mixed land uses, pedestrian accessibility, and proximity to high-capacity transit systems, are designed to encourage public transportation use and reduce reliance on private automobiles (Cervero \u0026amp; Kockelman, 1997; Curtis et al., 2009). Over the past decade, the TOD approach has gained significant traction in both academic research and professional practice, with cities across the globe applying TOD principles to guide sustainable urban growth (Zhao et al., 2024; Abdullah et al., 2024; Arliani et al., 2024; Liu et al., 2024; Mangu et al., 2025; Ali et al., 2025). The benefits of TOD are well documented in the literature, including enhanced land use efficiency, improved transit ridership, reduced greenhouse gas emissions, revitalization of urban centers, and better quality of life (Cervero, 2013; Singh et al., 2014; Papa \u0026amp; Bertolini, 2015; Sung \u0026amp; Eom, 2024). These attributes make TOD particularly appealing for cities in developing countries that are experiencing rapid population growth, uncontrolled urban expansion, and infrastructure deficiencies. Iranian metropolises such as Tabriz face similar urban challenges, including horizontal expansion, poor transit integration, inadequate land use planning, and rising environmental and social pressures (Ghanbari et al., 2019; Shahabian \u0026amp; Asadi, 2017). Yet, despite growing awareness of TOD as a potential solution, the implementation of TOD principles in Iranian cities remains limited and underexplored. In particular, there is a critical research gap in the development and classification of TOD typologies in the Iranian context. Most studies in Iran have focused on general transit infrastructure or land-use patterns, without systematically analyzing the spatial characteristics and performance of TOD at the corridor or station level. To address this gap, the present study focuses on Tabriz, one of Iran\u0026rsquo;s largest and most strategically significant cities. Specifically, the research investigates the spatial patterns of TOD along the city\u0026rsquo;s Bus Rapid Transit (BRT) corridor\u0026mdash;an emerging transit system designed to improve mobility and access across the urban fabric. Drawing on TOD literature and international best practices, this study evaluates four key dimensions of TOD: density, diversity, design, and distance to transit. These dimensions represent the \u0026ldquo;4Ds\u0026rdquo; commonly used to operationalize TOD in empirical research (Sahu, 2018; Kamruzzaman et al., 2014; Kumar et al., 2018). The core hypothesis guiding this research is that there is substantial spatial heterogeneity in TOD characteristics along the BRT corridor in Tabriz, reflecting uneven levels of land use integration, infrastructure provision, and transit accessibility. This hypothesis is grounded in the assumption that TOD performance varies across urban areas depending on contextual factors such as built environment, planning policies, and socio-economic conditions. Accordingly, the following research questions are posed:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1. What are the dominant patterns of TOD along the BRT corridor in Tabriz?\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e2. How do these patterns align or diverge from the indicators of sustainable urban structure?\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3. Which BRT station areas show the greatest potential for TOD-based redevelopment?\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTo answer these questions, the study employs spatial analysis using GIS tools and statistical classification through K-means cluster analysis in SPSS. A 600-meter buffer zone\u0026mdash;consistent with international TOD standards\u0026mdash;is applied around each BRT station to delineate the study area and capture key variables related to land use, density, and design features (Guerra et al., 2012; Sohoni et al., 2007; Kamruzzaman et al., 2014). By analyzing these data, the study develops a typology of TOD patterns that reflects the diversity and complexity of Tabriz\u0026rsquo;s urban structure. This research contributes to the growing body of TOD literature by providing empirical evidence from a Middle Eastern context, where TOD is still an emerging concept. The typological framework developed here can inform urban policymakers, planners, and transportation authorities in identifying priority areas for TOD enhancement and integrating transit planning with land use strategies. Moreover, the study offers methodological insights for conducting TOD evaluations in other cities facing similar challenges in the Global South. The remainder of the paper is structured as follows: Section 2 reviews the relevant literature and theoretical background. Section \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e describes the study area, data sources, and research methodology. Section 4 presents the results of the spatial and statistical analyses. Section 5 discusses the findings in light of TOD theory and practice. Finally, Section 6 concludes the paper with recommendations and directions for future research.\u003c/p\u003e"},{"header":"2- Research Background","content":"\u003cp\u003eThis section highlights key previous studies that share significant conceptual and thematic similarities with the present research. Transit-Oriented Development (TOD), with a focus on promoting sustainable urban structures, has been empirically examined in numerous studies (Sung \u0026amp; Choi, 2017; Ghanbari et al., 2019; Shirke et al., 2017; Noland et al., 2017; Higgins \u0026amp; Kanaroglou, 2016; Papa \u0026amp; Bertolini, 2015; Galelo et al., 2014). For instance, Sung and Choi (2019) investigated the relationship between metropolitan planning and TOD, emphasizing the Rosario\u0026ndash;Seoul project. Their study highlights a deliberate attempt to decentralize the spatial structure of metropolitan areas by promoting transit hubs. Through the development of an integrated approach, the project aimed to address various urban problems. Similarly, Ghanbari et al. (2019) found that the proposed master plans for the Railway Center and University of Tabriz were not aligned with TOD indicators, suggesting that major spatial revisions are required to meet TOD standards.\u0026nbsp;Shirke et al. (2017) argued that TOD serves as an effective tool for achieving sustainable development in densely populated Indian cities. Noland et al. (2017), examining TODs in New Jersey through both expert and resident perspectives, found a strong alignment in stakeholder views\u0026mdash;both indicating positive impacts of TODs on traffic reduction and accessibility improvement. Higgins and Kanaroglou (2016), in their study of the Toronto metropolitan area, concluded that stations with higher TOD scores were associated with increased transit use, walking, and cycling, and lower vehicle kilometers traveled (VKT). Kumar et al. (2018) introduced a four-tier typology of TODs in Delhi, focusing on residential heterogeneity. Their results showed that TOD policies influenced residential diversity, with an average effect of 0.5 units and a 26% change in neighborhood heterogeneity. Similarly, Rodriguez and Vergel-Tovar (2018) analyzed 81 BRT stations in seven Latin American cities and identified 10 station typologies, several of which reflected typical TOD characteristics. In the context of Denver, Ratner and Goetz (2010) found that TOD implementation transformed land use and urban form. Their results showed residential developments dominating neighborhood stations, while downtown stations exhibited mixed uses including retail, office, and government buildings. Al-Hesabi and Moradi (2019) proposed a typology for metro stations in Shiraz (Metro Line 1), categorizing them into six types (A\u0026ndash;F). Type A stations demonstrated the greatest potential to serve as TOD centers due to their dual ability to influence both city structure and adaptive capacity.\u003c/p\u003e\n\u003cp\u003eBased on the review of these studies, three key observations emerge:\u003c/p\u003e\n\u003cp\u003e3- Typological Foundations: Most existing TOD typologies are grounded in physical or built environment characteristics\u0026mdash;such as density, land-use diversity, proximity to transit, and design features. However, some recent research has expanded the scope to include socio-demographic characteristics of residents and transit users, thereby offering a more integrated analysis of TOD potential. The present study aligns with this body of literature by focusing on widely accepted TOD indicators.\u003c/p\u003e\n\u003cp\u003e4- Performance Determinants: Prior studies have consistently emphasized variables such as mode choice, accessibility, and parking supply/demand as critical determinants of TOD performance and their broader impact on sustainable urban development. However, many of these studies lack a clear, integrated framework for assessing how these TOD indicators reshape urban structure\u0026mdash;an analytical gap that this study aims to address.\u003c/p\u003e\n\u003cp\u003e5- Iranian Context Gap: Within Iranian urban studies, TOD typology research is scarce and often lacks methodological rigor. Existing typological criteria tend to be derived from development plans with small sample sizes and limited theoretical grounding. Moreover, the role of TODs in promoting a sustainable urban structure remains underexplored. This study seeks to overcome these limitations by developing a systematic TOD typology tailored to the urban fabric of Tabriz.\u003c/p\u003e\n\u003cp\u003eUndoubtedly, transit has long been one of the key pillars of spatial transformation, urban structure, and land use management, contributing to the evolution and emergence of diverse spatial organizations within cities. The recognized advantages of transit systems have led to the widespread acceptance of the Transit-Oriented Development (TOD) model as one of the most comprehensive approaches to contemporary urban development (Dargahi et al., 2016). As Rodriguez and Vergel-Tovar (2018) state, \u0026ldquo;land use strategy and transit strategy are the cornerstones of TOD,\u0026rdquo; emphasizing its crucial role in shaping sustainable urban structures. In light of the research concepts and the issues discussed in the previous sections, the conceptual framework of this study is illustrated in the following diagram (Figure 1).\u003c/p\u003e"},{"header":"3- Methodolgy","content":"\u003ch3\u003e3\u0026thinsp;\u0026minus;\u0026thinsp;1 Study area\u003c/h3\u003e\n\u003cp\u003eTabriz, the capital of East Azarbaijan province is located in the northwest, 526 km northwest of Tehran. The city is located at 46 degrees and 18 minutes\u0026rsquo; east longitude and 38 degrees and 4 minutes\u0026rsquo; north latitude (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The population of this city according to the census of 2016 was equal to 1508993 people (Statistics Center of Iran, 2016).Currently, Tabriz is the sixth-largest city in Iran with a population of 1.6 million after Tehran, Mashhad, Isfahan, Karaj, and Shiraz (Statistics Center of Iran, 2016).\u003c/p\u003e\n\u003cp\u003eThe Bus Rapid Transit (BRT) system of Tabriz metropolis consists of two routes, which were launched in September 2008. Lane One of BRT Tabriz, the main public transport corridor and the focus of this study, spans 18 kilometers. This route initially extended from the railway station in the west of Tabriz to the Abresan Crossroads in the east, and was later extended to the Tabriz International Exhibition Center at Basij Square (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bus.tabriz.ir\u003c/span\u003e\u003c/span\u003e, 2019). Serving as a vital west\u0026ndash;east corridor, this BRT lane functions as a major urban artery, accommodating 27 stations along its path (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e: Picture A). It is noteworthy that 174 cities worldwide currently operate newly established or expanded BRT systems, with Tabriz among them (Global BRT Data, 2020). Presently, 105 buses operate on this route, including 96 double-decker and 9 single-decker buses. Collectively, lines 1 and 2 transport over 100,000 passengers daily (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bus.tabriz.ir\u003c/span\u003e\u003c/span\u003e, 2019). Tabriz is administratively divided into 10 municipal districts, all of which are influenced by the operation of BRT routes (Tabriz Municipality, 2016). Furthermore, the new comprehensive plan of Tabriz, originally drafted in 2012 and officially approved with revisions by the Supreme Council of Urban Planning and Architecture of Iran in 2017, outlines a transit-oriented development (TOD) and node-based traffic strategy. In addition to the historical city center, the plan proposes two new urban centers and a sub-core referred to as \u0026ldquo;inner edge cities.\u0026rdquo;These areas are the west of Tabriz, Kargar Boulevard to the center of the railway station, and the area of East Tabriz and 29 Bahman Boulevard to the center of Tabriz University (Ghanbari et al, 2019). Therefore, the present study tries to measure a BRT lane in Tabriz to stabilize its structure by focusing on TOD types. Figures (3) shows a lane of a BRT in the city of Tabriz (Figure A) with its 600-meter radius (Figure B).\u003c/p\u003e\n\u003ch3\u003e3\u0026thinsp;\u0026minus;\u0026thinsp;2 Data collection\u003c/h3\u003e\n\u003cp\u003eSurvey and documentary methods were used to collect data for this study. Both primary and secondary data sources were utilized to describe and analyze the environment surrounding each BRT station. Primary data were gathered through field visits and direct observations at each station. Secondary data were compiled based on the concepts and principles of Transit-Oriented Development (TOD), with a specific focus on the city of Tabriz in terms of land use, population density, accessibility, infrastructure, and overall spatial structure. These data were obtained by referring to urban development organizations, comprehensive and detailed urban plans, and other official planning documents. The secondary data used in this study can be classified as follows:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003ea) Population and employment data based on census information from statistical blocks (Iran, Detailed Results of the General Census of Population and Housing, 2016);\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003eb) Spatial datasets used to extract TOD typologies along the BRT corridor in Tabriz (Iran, Detailed Results, 2016);\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003ec) Data from the Tabriz Transit Department and the urban communication network (Tabriz and Suburbs Bus Company, 2019).\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe compiled dataset for each station includes demographic, employment, land use, transit, and geospatial information.\u003c/p\u003e\n\u003cp\u003eIn this study, a service radius of 600 meters was defined for each BRT station. This decision aligns with most prior TOD research, which identifies a 600-meter buffer as a standard functional range for TOD centers (Kamruzzaman et al., 2014). Other studies have suggested different radii, such as 800 meters (Galelo et al., 2014; Gu et al., 2019) or even 1000 meters, which some researchers consider more appropriate for capturing broader urban structural characteristics (Chen et al., 2017). According to Seruro (2004) and Guerra et al. (2012), a half-mile buffer (approximately 800 meters) is commonly used as a standard for assessing walkability and bicycle accessibility in TOD studies. Evans (2007) proposed a TOD buffer ranging between 400 and 800 meters. Ultimately, this study adopted a 600-meter radius\u0026mdash;hereafter referred to as the TOD buffer\u0026mdash;as the service area for each BRT station. This range is consistent with TOD planning and design guidelines commonly applied to bus and rail stations in Iran and reflects the average distance for pedestrian and bicycle access\u003c/p\u003e\n\u003ch3\u003e3\u0026ndash;3 Designing Indicators and Research Analysis Methods\u003c/h3\u003e\n\u003cp\u003eFor this study, four main parameters have been defend of our current case study(Table 1).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Framework of indicators for assessing TOD in terms of sustainable urban structure\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"684\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eTOD Principle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eFormula and Evaluation Method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eNumber of inhabitants per hectare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eNasri \u0026amp; Zhang, 2014; Sohoni et al., 2017; Galelo et al., 2014; Sahu, 2018; Ewing et al., 2017; Cervero et al., 2004; Kumar et al., 2018; Translink, 2012; Gu et al., 2019; Reusser et al., 2008; Ogra \u0026amp; Ndebele, 2014; Al-Harami A. F., 2020; Ratner \u0026amp; Goetz, 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eEmployment density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eNet employment density calculated as the number of jobs per unit area of employment-generating land uses (e.g., commercial, industrial) within designated radius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eSame as above\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eResidential density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eNumber of people in the designated radius per hectare of residential area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eGalelo et al., 2014; Kumar et al., 2018; Translink, 2012; Kamruzzaman et al., 2014; Reusser et al., 2008; Ogra \u0026amp; Ndebele, 2014; Al-Harami A. F., 2020; Ratner \u0026amp; Goetz, 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eNumber of residential units\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eNumber of housing/apartment units intended for family living\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eRiggs \u0026amp; Chamberlain, 2018; Kumar et al., 2018; Translink, 2012; Zaina et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eCommercial space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003ePercentage of commercial land area to total land area within designated radius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eRiggs \u0026amp; Chamberlain, 2018; Translink, 2012; Ogra \u0026amp; Ndebele, 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eDiversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eMixed land use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eEntropy index coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eSingh et al., 2014; Sohoni et al., 2017; Sahu, 2018; Cervero \u0026amp; Kockelman, 1997; Ewing et al., 2017; Higgins \u0026amp; Kanaroglou, 2016; Kumar et al., 2018; Translink, 2012; Nasri \u0026amp; Zhang, 2014; Kamruzzaman et al., 2014; Gu et al., 2019; Reusser et al., 2008; Ogra \u0026amp; Ndebele, 2014; Al-Harami A. F., 2020; Zaina et al., 2016; Ratner \u0026amp; Goetz, 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDesign\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eBicycle routes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003ePresence or absence of a dedicated bike path\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eSohoni et al., 2017; Cervero \u0026amp; Kockelman, 1997; Translink, 2012; Kamruzzaman et al., 2014; Gu et al., 2019; Schlossberg \u0026amp; Brown, 2004; Ogra \u0026amp; Ndebele, 2014; Al-Harami A. F., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eOther design elements (e.g., parking)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eCounting the number of parking lots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eStaricco \u0026amp; Brovarone, 2018; Higgins \u0026amp; Kanaroglou, 2016; Kumar et al., 2018; Ewing et al., 2017; Al-Harami A. F., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eDistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eDistance in meters or miles to station\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eAccessible area determined by a 10-minute walking distance using network analysis in ArcGIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003ePapa \u0026amp; Bertolini, 2015; Sahu, 2018; Ewing et al., 2017; Cervero et al., 2004; Translink, 2012; Gu et al., 2019; Schlossberg \u0026amp; Brown, 2004; Ogra \u0026amp; Ndebele, 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eReference: (Authors)\u003c/p\u003e\n\u003cp\u003eThe stations studied as \u0026quot;transit-oriented development\u0026quot; have been selected based on cluster analysis to select the typology of transit-oriented development centers using the four main pillars of urban structure and TOD, namely density, diversity, design, and distance (D). Spatial analysis was performed in Arc GIS version 10.3; In the first stage, using the information received from the responsible institutions such as the municipality and the General Directorate of Roads and Urban Development in the form of urban maps 2000/1, GPS mapping, Google Earth images, and Cad Map, Global Mapper and ArcGis software, communication network map of Tabriz (main and secondary) was drawn and after preparing the data (georeferencing and topology), it was entered into Arc Catalo software to convert it into a network structure. In the second step, the buffers in GIS were calculated using direct distances and all or none functions instead of other methods of calculating distances such as network distance and distance reduction functions discussed in the relevant literature. Finally, in the mentioned software, a network structure was created by defining relationships and using the functions of network speed, network length, network type, apparent network resistance, and network barriers. Then, in Arc Map software, the initial analysis was performed using the New Service Area command in the Analyst Network menu. In this paper, in addition to the characteristics of the stations, the relationship between the major TOD indices was measured through K-mean cluster analysis tools in SPSS software. In the cluster analysis method, an attempt is made to divide the observations into homogeneous groups, so that the observations of all groups are similar to each other and have the least similarity with the observations of other groups (Akbari \u0026amp; Zahedi, 2008). Therefore, in this study, the analysis of these clusters was used to determine how the stations can be grouped to minimize the internal heterogeneity of the clusters and maximize the heterogeneity between the groups. As a result, not only the dominant patterns of TOD but also its impact on the spatial structure of Tabriz was determined.\u003c/p\u003e"},{"header":"4- Results","content":"\u003ch2\u003e4- 1 Analysis of TOD Indicators: Land Use Pattern, Entropy, and Density Distribution\u003c/h2\u003e\n\u003cp\u003eThe main findings of the study to measure the type and level of TOD in the urban structure of Tabriz according to the indicators discussed (Table 1) were obtained in the form of tables, charts, and maps that are mentioned below. First of all, it is necessary to study land use pattern within study area, and through it, \u0026ldquo;propose the redevelopment to transform the land use into transit supportive form\u0026rdquo; (Shirke et al., 2017). In connection with this issue, the highest entropy coefficient among all stations of Tabriz BRT line 1 was estimated for Tabriz University stations (0.77) and Basij terminal (0.75), respectively. In contrast, the lowest coefficients belong to the alley station stations (0.28) and the pole square (0.29) (Figure 5). Therefore, the majority of landfills are in the \u0026quot;0.35 -0.65\u0026quot; range, which most of the stations in the route, and most of the TODs with mixed-use are observed in the middle tissues (Table 2). But the central sector lacks these features despite tackling the many challenges of access, traffic, and lack of public services. The second indicator that is more emphasized in the literature is population density. According to the estimates made in a radius of 600 meters per person per hectare, among the 26 stations, the highest calculated density was observed for the stations of Khatib (42.53) and Kalantar Alley (36.21). In this regard, the lowest number of people per hectare was calculated for the end stations of Basij (0) and Tehran Road (0.44), respectively. On the one hand, the density in question has been very low in stations with a marginal and new location. On the other hand, the functions of centralization have led to a lack of population development in some central context stations (Figure 5). Regarding the housing density index, the following points can be noted: First, a total of four stations with a population of more than 100 people per hectare, including 29 Bahman Hospital (414.22 people/hectare), Jam Jam (322.63 people/hectare), Tehran Road (135.27) N / ha) and guidance (125.21 n / ha). Second, 5 stations with less than 30 people per hectare were obtained: Basij Terminal (0), Shariati Crossroads (21.82 people / hectare), Ferdowsi High School (24.07 people / hectare) and Shahid Beheshti (27.63 people / hectare). Third, of the other stations, 11 were distributed in the \u0026quot;50-30\u0026quot; cluster and 6 in the \u0026quot;50-100\u0026quot; cluster. Therefore, more than 60% of the study area does not have the desired residential density for transit-oriented development. As can be deduced from Table (2), the bicycle path in a radius of 600 meters is not designed for stations and can be seen only in a small number of major centers such as Saat Square station and Shahid Beheshti (Mansour) crossroads with a limited area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Estimation and initial calculation of the studied indicators in the current situation\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"669\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eStation name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eentropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003epopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eResidential density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003ebike route\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eCommercial space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eBuilding Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEmployment density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003epedestrian access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eResidential unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eParking\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRah Ahan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e49.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e99.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e23636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e5354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDarya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e34.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e51.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e109.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e23827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e7931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eKhatib Crossroads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e42.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e53.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e131.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e24494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e9836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAshkan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e32.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e47.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e146.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e24494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e9361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eSherkat Naft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e32.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e57.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e142.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e24866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e7625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eSilo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e29.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e54.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e7.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e115.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e25032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e7111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGhatran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e34.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e54.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e117.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e25624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e8182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eBazar Milad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e41.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e113.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e25624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e5376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGolestan Garden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e17.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e34.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e23.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e129.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e25685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e4510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eFerdowsi High School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e9.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e21.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e24.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e146.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e25315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e3106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eShariati Crossroads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e9.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e21.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e24.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e171.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e24831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e2894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eSaate Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e10.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e24.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e142.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e24824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e5374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eShahid beheshti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e18.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e27.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e11.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e133.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e23892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e7304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eHaj Ahmad Mosque\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e22.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e9.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e133.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e23456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e8953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGhotbe Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e31.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e37.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e127.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e22579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e10016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003ekallantar Alley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e36.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e43.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e134.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e21905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e4450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAbrasan Intersection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e23.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e176.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e20104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e3192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eTabriz University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e14.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e73.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e167.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e19777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e2248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eJame Jam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e10.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e322.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e140.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e19047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e1689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e29 Bahman Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e15.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e414.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e36.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e15288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e1095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRahnamayi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e17.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e125.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e61.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e13108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e1327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAzari\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e20.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e49.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e14.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e127.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e11133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e3684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eSerahi Valiasr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e24.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e38.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e12.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e144.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e10792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e5651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eOstad Shahriyar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e16.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e39.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e136.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e9598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e4828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eJade Tehran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e135.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e38.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e6390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003ePayene Basije\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e9.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResource: (Research finding, 202)\u003c/p\u003e\n\u003ch2\u003e4-2 Evaluation of Commercial Intensity, Building Density, and Employment Distribution Across TOD Zones\u003c/h2\u003e\n\u003cp\u003eIn the next step, the commercial space index was calculated, in which the stations of the central part of the city (CBD) benefited from a higher ratio than the middle and marginal stations. For example, the highest coefficients are assigned to Ferdowsi High School stations (24.59) and Shariati Crossroads (24.29), both of which belong to the central context. Also, it seems that the coefficients of commercial space are relatively completely adapted to the location of most stations (Table 2). Another indicator that is used to measure the degree of compaction and density around public transit centers is building density. The index was examined in four categories; A) less than 50%; B) 100-50%; C) 150-100%: Most of the stations analyzed are in this scale, the most important of which are Ferdowsi High School (146.89%), Ashkan (146.27%), Valiasr Highway (144.29%) and Saat Square (142.18%), respectively. D) More than 150%: The most important urban nuclei of Tabriz are stations of this category, which include Abersan crossroads (176.87%), Shariati crossroads (171.20%), and Tabriz University (167.57%). Therefore, it is worth mentioning a few points that, firstly, the stations in the western part have a higher building density than the eastern part (marginal and new textures). Second, the main centers that play a role as multi-core centers in the spatial structure of the city of Tabriz, have an acceptable level of compaction and density. Another indicator is the employment density, which among the studied stations, the best situation is related to Shariati, Ferdowsi High School and Railway stations with a coefficient of 0.13. In contrast, the lowest coefficients for Tehran Road and Basij Square stations were estimated at 0.04 and 0.06, respectively.\u003c/p\u003e\n\u003cp\u003eTo calculate the pedestrian access index, the distance traveled and the distance from the center of the neighborhood to the desired station were taken into account. Findings indicate that in most places pedestrian access to stations is significantly different from the standard access radius. Finally, the number of residential units that show the tangible dimensions of residential development around the stations was calculated (Table 2). Parking was also an important design element in this study. The findings indicate that there are more in the central part of the city. Some stations such as Khatib, Valiasr, Ostad Shahriyar, Tehran Road and Basij terminal do not have parking within a radius of 600 meters of BRTs (Figure 6).\u003c/p\u003e\n\u003ch2\u003e4-3 TOD Typology Based on K-Means Clustering: Classification and Comparative Analysis of BRT Station Characteristics\u003c/h2\u003e\n\u003cp\u003eAt this stage, based on the information obtained from the study of the characteristics of bus stations through K-mean cluster analysis, the typology and analysis of TODs were performed. Since this method assumes that the data are classified into a specified number of clusters (Habibpour \u0026amp; Safari Shali, 2009). Therefore, following the common typology in the cities of the developing and developed world, the number of clusters was limited to 4. To achieve specific results in the first place by updating the cluster centers frequently, the initial typology of the stations was obtained by considering the discussed indicators (Table 3). As can be seen, four clusters are formed for the TOD quantity. Euclidean distance method was used to determine the distance between two indicators. Table (3) shows the initial average of each indicator within each cluster. More precisely, preliminary comparisons have been made between BRT stations in terms of TOD indices in Tabriz. Due to the quality of TOD, the imbalance of residential density and building density between the four clusters in the service radius of BRTs is relatively high. Besides, in all clusters in terms of design, the areas around the BRT stations are not suitable for pedestrians considering the access distance, parking amount, and quality of the sidewalk and bicycle. But clusters are one exception. Thus, in more than half of the cluster elements, the radius around BRT stations has higher street and highway densities and less cycling and walking capacity. In research also points to the inadequacy of pedestrian capacity in the radius of the stations and also emphasizes the lack of complimentary bus services in the BRT systems of Chinese cities. Nearly 30% of stations experience land use uniformity around the station (low diversity). Also, low residential density is seen in half of the clusters and unfavorable population, and construction density is seen concerning most of the BRT stations in Tabriz (Table 3). Therefore, the common problem in the field of transit-oriented development of Asian cities (Sahu, 2018) also applies to Iranian cities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Initial Cluster Centers\u003c/p\u003e\n\u003ctable dir=\"rtl\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 359px;\"\u003e\n \u003cp dir=\"LTR\"\u003eClusters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 227px;\"\u003e\n \u003cp dir=\"LTR\"\u003eIndicators\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp dir=\"LTR\"\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp dir=\"LTR\"\u003e9.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp dir=\"LTR\"\u003e31.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp dir=\"LTR\"\u003e10.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp dir=\"LTR\"\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp dir=\"LTR\"\u003e21.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp dir=\"LTR\"\u003e37.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp dir=\"LTR\"\u003e322.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp dir=\"LTR\"\u003eResidential density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp dir=\"LTR\"\u003eBike route\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp dir=\"LTR\"\u003e24.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp dir=\"LTR\"\u003e6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp dir=\"LTR\"\u003e7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp dir=\"LTR\"\u003eCommercial space\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp dir=\"LTR\"\u003e146.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp dir=\"LTR\"\u003e127.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp dir=\"LTR\"\u003e9.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp dir=\"LTR\"\u003e140.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp dir=\"LTR\"\u003eBuilding density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp dir=\"LTR\"\u003eEmployment density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp dir=\"LTR\"\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp dir=\"LTR\"\u003eParking ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3106.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp dir=\"LTR\"\u003e10016.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp dir=\"LTR\"\u003e168.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp dir=\"LTR\"\u003eResidential unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp dir=\"LTR\"\u003e25315.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp dir=\"LTR\"\u003e22579.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3013.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp dir=\"LTR\"\u003e19047.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp dir=\"LTR\"\u003ePedestrian access\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: (Research Findings, 2020)\u003c/p\u003e\n\u003cp\u003eIn the next step, after 10 repetitions, the centers of the clusters did not change and the average of the clusters reached an acceptable level. The maximum change in absolute coordinates for each center is 0.057. The current repetition is 10. The minimum distance between the primary centers is 6433.255. As a result, to achieve a suitable typology, the degree of similarity of 26 BRT stations in Tabriz was compared through a clustering algorithm. The values obtained show how much the center size of all four clusters has changed for each repetition. Table (4) indicates which cluster membership of each station. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor example, Golestan Garden Station is located in Cluster 1, Qatran Station in Cluster 2, 29 Bahman Hospital Station in Cluster 3, and Tehran Road Station in Cluster 4. It is clear that the distance between each station is less with itself (convergence and similarity) and with other clusters is greater (divergence and difference). Since the distance of most of the analyzed stations from the center of their cluster is less, it indicates the suitability and comprehensiveness of the cluster for its elements. Therefore, based on the distance (third and sixth columns of Table 4), the highest distance and the lowest amount belong to Khatib (7890.117) and Oil Company (914.554) intersection stations in cluster three, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Cluster membership\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eStation brt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eCluster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003einterval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eStation brt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eCluster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003einterval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eRah Ahan\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5438.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eHaj Ahmad Mosque\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7755.962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eDarya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e541.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eGhotbe Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2814.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eKhatibCrossroads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7890.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ekallantar Alley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4910.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eAshkan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1569.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eAbrasan Intersection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7298.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eSherkat Naft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e914.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eTabriz University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3677.911\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eSilo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2632.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eJame Jam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5499.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eGhatran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2981.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e29 Bahman Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4275.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eBazar Milad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3250.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eRahnamayi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2418.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eGolestan Garden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3336.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eAzari\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1548.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eFerdowsi High School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5591.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eSerahi Valiasr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4694.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eShariati Crossroads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5448.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eOstad Shahriyar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3986.412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eSaate Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4848.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eJade Tehran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1703.853\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eShahid beheshti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4731.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ePayene Basije\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1703.795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: (Research Findings, 2020).\u003c/p\u003e\n\u003cp\u003eFollowing the previous steps, the final cluster centers were identified after the clustering iteration algorithm. In the final cluster centers stage, the average of the ten research indices was reflected within each of the four clusters. The mean values obtained in the last stage indicate that it is high in clusters 3 and 1 in most indices. In other words, in clusters three and one, the indicators of transit-oriented development have appeared better than clusters four and two. Finally, by comparing the table of initial and final cluster centers, it can be said that after passing the iteration algorithm, the centers of the clusters are closer to each other. Here, where the Euclidean distances between the centers of the final cluster are expressed (Table 5), we see that there is a dissimilarity between the clusters. For example, the distance between clusters one and four is 22941.327.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Distances between Final Cluster Centers\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eCluster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e11495.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e15369.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e22941.327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e11495.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e17451.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e18265.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e15369.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e17451.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e35041.412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e22941.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e18265.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e35041.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: (Research Findings, 2020).\u003c/p\u003e\n\u003ch2\u003e4-4 Statistical Validation of TOD Clusters Using ANOVA Analysis\u003c/h2\u003e\n\u003cp\u003eIn the next step, the results of ANOVA test indicate the heterogeneity and similarity of clusters, the level of significance, and the role of each of the ten indicators in the clustering of BRT stations in Tabriz. As a result, pedestrian access indicators, number of housing units, employment density, and building density have played the most important role. In contrast, bicycle path indicators, population density, and commercial space with coefficients of 0.235, 0.978, and 1.582 had the lowest share in determining the clusters, respectively. At the 5% error level, only three variables (low performance) were not significant (Table6). Therefore, the clusters are completely different from each other and the results are acceptable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u003c/strong\u003e ANOVA test\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003eprinciples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 24px;\"\u003e\n \u003cp\u003eIndicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003csup\u003e[1]\u003c/sup\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eSig\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eentropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003epopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e606.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e56.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e10.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eResidential density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e8375.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e8562.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003ebike route\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eCommercial space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e72.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e46.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eBuilding Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e8599.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e756.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e11.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eEmployment density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eParking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e22.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e6.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eResidential unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e62277252.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e1962521.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e31.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003epedestrian access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e347916557.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e4288731.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e81.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: (Research Findings, 2020).\u003c/p\u003e\n\u003cp\u003eIn other words, the significance value (Sig.) is very small for both criteria (P-value \u0026lt; 0.001), indicating that the assumption of homogeneity among clusters is rejected. This confirms the statistical validity of the clustering results. In the final step, the number of stations in each cluster was determined (Table 7). Based on the derived coefficients, mean values, and the implementation of the K-means cluster analysis procedure, the distribution of BRT stations in Tabriz was classified into four major clusters. The first cluster includes nine stations: Rah Ahan, Golestan Garden, Ferdowsi High School, Shariati (Shahnaz) Intersection, Saat Square, Shahid Beheshti (Mansour), Abresan Intersection, Tabriz University, and Jam-e Jam. Together, they comprise approximately 35% of the BRT stations in Tabriz. \u0026nbsp;The second cluster includes five stations: 29 Bahman Hospital, Rahnamayi, Azari, Serahi Valiasr, and Ostad Shahriyar. These stations account for approximately 19% of the total.The third cluster comprises a larger group: Darya, Khatib Crossroads, Ashkan (Halfway), Sherkat Naft, Silo, Ghatran, Milad Market, Haj Ahmad Mosque, Ghotbe Square, and Kalantar Alley. This cluster represents over 38% of the stations Finally, the fourth cluster consists of only two stations\u0026mdash;Jadeh Tehran (Tehran Road) and Payaneh Basij (Basij Terminal)\u0026mdash;which together account for less than 8% of all stations studied (refer to Tables 4 and 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7.\u003c/strong\u003e Number of stations in each cluster\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003econcepts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eRow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eAbundance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003epercent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 200px;\"\u003e\n \u003cp\u003eClusters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg 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alt=\"image\" style=\"width: 401px; height: 241.027px;\" width=\"401\" height=\"241.027\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eAuthentic items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e26.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eMissing items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: (Research Findings, 2020)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy summarizing the results obtained from the multivariate clustering analysis, alongside established concepts and classification criteria for Transit-Oriented Developments (TODs), the four clusters identified in this study can be named and interpreted as follows: The first cluster includes most of the major and strategic BRT stations in Tabriz. These stations are surrounded by high-density, compact areas featuring a mix of administrative-institutional, educational, healthcare, commercial, residential, and tourism-related land uses. For example, landmarks such as Imam Reza Hospital, the University of Tabriz, cultural centers, renowned bookstores, high-end service towers (e.g., Bolour Tower, Abrisham Tower), international hotels, the Tabriz Indoor Bazaar, Golestan Garden, historical museums, the Alishah Citadel, the Imam Khomeini Prayer Hall, and the Tarbiat Pedestrian Street are located within a 600-meter service radius. These diverse functions make this cluster highly dynamic and active. Notably, most of these stations are situated in the historic core of Tabriz, which includes UNESCO World Heritage Sites such as the Tabriz Bazaar. Furthermore, this cluster spans the entire BRT corridor from west to east, making it the only TOD type with a continuous presence. Accordingly, this group is best classified as an Active TOD. The second cluster, consisting of five stations, is mainly distributed across the eastern and central parts of Tabriz. These stations are located near affluent residential neighborhoods, including the Valiasr Dormitory area, and benefit from robust communication infrastructure, elevated highways, government institutions, and large-scale real estate developments. These factors have positively influenced several TOD indicators. The stations in this cluster exhibit a growing degree of compaction and relatively strong values for residential, population, and employment densities, along with moderate accessibility and design characteristics. As such, this group can be referred to as a Potential or Balanced TOD. The third cluster, comprising 10 stations, represents the largest segment of the study area. It is characterized primarily by residential land use, with a mix of high-density housing developments, yet it suffers from limited and incomplete sidewalk infrastructure, a shortage of quality public spaces, and relatively weak orientation to BRT services. Most of these stations are located between the central and western portions of the urban structure of Tabriz. Given the dominance of housing-related functions, this cluster is appropriately categorized as a Residential TOD. The fourth cluster includes only two stations\u0026mdash;Tehran Road and Payaneh Basij. These areas are marked by industrial and workshop zones, mixed-use commercial factories, and extensive vacant or undeveloped land within the 600-meter buffer. The absence of essential TOD features and supporting infrastructure renders these stations ineffective as transit-oriented nodes. Consequently, this cluster is classified as Non-TOD.The spatial distribution of these four clusters and their associated TOD types within the Tabriz BRT network are visually represented in Figure 7, based on the analytical results discussed.\u003c/p\u003e\n\u003cp\u003eThe typology of BRT stations, based on the type of urban context, illustrates their spatial and geographical distribution within the urban structure of Tabriz. This classification reveals notable insights into the compatibility between the service radii of the studied stations and the core indicators of Transit-Oriented Development (TOD). From a comprehensive perspective, four distinct urban textures can be identified to evaluate the influence of BRT on the sustainable development of Tabriz:\u003c/p\u003e\n\u003cp\u003ea) Central Texture: This area includes seven stations, of which five (Ferdowsi, Shariati Intersection, Saat Square, and Shahid Beheshti Intersection) correspond to the Active TOD type. The remaining two stations (Haj Ahmad Mosque and Milad Bazaar) align with the Residential TOD type. Altogether, this texture accounts for approximately 27% of the total stations analyzed.\u003c/p\u003e\n\u003cp\u003eb) Transition Zone: This zone includes three stations, all of which display the characteristics of Residential TOD. Approximately 11% of Tabriz\u0026rsquo;s BRT stations fall within this urban texture.\u003c/p\u003e\n\u003cp\u003ec) Middle Texture: This is the largest and most extensive urban zone in terms of both area and the number of BRT stations. It contains 14 stations, representing more than 54% of the stations in the study area. Of these, five exhibit Residential TOD characteristics, five are classified as Potential TODs, and four demonstrate Active TOD capabilities. This texture plays a crucial role in shaping the TOD landscape of Tabriz.\u003c/p\u003e\n\u003cp\u003ed) Peripheral and Suburban Texture: This texture includes the only two stations classified as Non-TODs\u0026mdash;Basij Square and Tehran Road. These peripheral stations serve less than 8% of the total BRT network and are characterized by weak integration with TOD principles.\u003c/p\u003e\n\u003cp\u003eThe spatial distribution and classification of these urban textures, along with their associated TOD types, are visually represented in Figure 8.\u003c/p\u003e"},{"header":" 5- Discussion and Conclusion","content":"\u003cp\u003eThe analysis of the selected indicators clearly demonstrates that TOD (Transit-Oriented Development) patterns across different segments of the BRT network in Tabriz vary significantly, affecting the urban structure in multiple dimensions. These patterns range from active TODs (such as in central areas and western entry points of the city) to areas lacking TOD features (notably the eastern gateways of Tabriz). The most notable finding is the stronger impact of TOD in the city center, where land use diversity and urban compactness dominate. This observation aligns with the findings of Ratner and Goetz (2013) regarding the city of Denver. Moreover, the study reveals substantial differences between the four clusters, indicating a lack of homogeneity. Based on the degree of similarity and heterogeneity among the indicators, BRT stations in Tabriz are classified into four primary clusters: (1) Active TOD, (2) Potential TOD, (3) Residential TOD, and (4) Lack of TOD. Similar classifications are found in previous studies (Kamruzzaman et al., 2014; Kumar et al., 2018), albeit with minor differences. A critical point is the inconsistency and stark contrasts in urban contexts across Tabriz concerning TOD functionality. The central areas show greater readiness for TOD development compared to suburban and mid-urban textures. Although many attractions, residential neighborhoods, and multifunctional complexes are located outside the city center, these areas predominantly rely on private vehicles, with limited access to transit-oriented centers. The study further highlights that TOD development is concentrated in the historical and central areas of Tabriz, while marginal areas suffer from poor transit infrastructure and access. In contrast, new and peripheral neighborhoods are dominated by car-dependent infrastructure. Notably, about half of Tabriz’s BRT stations fall into the latter category, suggesting that the TOD framework in the city is far from stable and necessitates a reassessment of current transit policies and urban development approaches. The BRT’s east-west alignment limits its integration into the overall urban structure. As suggested by Sung and Choi (2017), TOD policy should also be applied at broader regional scales. Land use zoning along the BRT corridor varies: the eastern part features a mix of residential, commercial, and vacant land; the central part is dominated by mixed-use development; and the western part emphasizes commercial, residential, warehouse, and parking uses. Mixed land use plays a crucial role in shaping the four TOD clusters, similar to the role it has played in TODs identified in Latin America, the United States, and Singapore (Rodriguez \u0026amp; Vergel-Tovar, 2018; Cervero \u0026amp; Murakami, 2009; Asghar et al., 2025; Moberg, 2025). This study aimed to assess employment density, residential density, and mixed land use patterns across the east-west corridor of Tabriz to enhance and optimize these variables. In line with the findings of Sahu (2018), this research prioritizes density and diversity, which have a significant influence on urban form and functionality (Riggs \u0026amp; Chamberlain, 2018; Papa \u0026amp; Bertolini, 2015; Ewing et al., 2017). Overall, Residential TOD emerged as the most prevalent development type in the study area, while the University of Tabriz station was identified as the most prominent example of an Active TOD. Nonetheless, this study has certain limitations that future research should address. First, the use of a 600-meter service radius, while consistent with previous research, may not fully capture TOD functionality compared to larger radii (e.g., 1000 meters), possibly resulting in limited findings. Second, TOD typologies were determined using mean values and cluster centroids, which may not comprehensively reflect the complex spatial realities of Tabriz. Third, demographic characteristics of users were not incorporated into the typology, as the study focused on urban structural indicators with accessible quantitative data. Future studies should consider integrating demographic variables with physical indicators for a more holistic evaluation. Finally, because the current analysis is limited to the east-west corridor of Tabriz, its conclusions do not extend to the entire urban network. However, the TOD typology is expected to evolve substantially with the planned implementation of a north-south BRT line and the full operation of the Tabriz metro system.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Term\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTransit-Oriented Development\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBus Rapid Transit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCentral Business District\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeographic Information System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlobal Positioning System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVKT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVehicle Kilometers Traveled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eANOVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnalysis of Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDetailed Transportation Plan (implied in \u0026ldquo;comprehensive and detailed\u0026rdquo;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArcGIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA GIS software for working with maps and geographic information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRT Lane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpecific route or corridor designated for Bus Rapid Transit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDeclaration\u003c/b\u003e: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: Akbar Hamidi and Zahra Rasoulzadeh contributed to the conceptual design, data collection, and analysis. Akbar Hamidi , Zahra Rasoulzadeh Abolfazl Ghanbari, Hossein Tahmasebi Moghaddam, Golzar Einali, and Khalil Gholamnia were responsible for GIS modeling, statistical analysis, and visualization. Khalil Gholamnia, Omid Ghorbanzadeh, and Sarbast Moslem supervised the research, reviewed the manuscript, and contributed to the interpretation of the results. All authors contributed to writing, reviewing, and approving the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to express their gratitude to the Tabriz Urban Planning and Transportation Organization and the Tabriz and Suburbs Bus Company for providing access to valuable spatial and transit-related data. We also appreciate the support of colleagues and academic mentors who contributed insightful feedback during the development of this research. Special thanks are extended to Charles University, Prague, and the University of Natural Resources and Life Sciences, Vienna (BOKU), for their academic support and institutional collaboration that enriched the quality of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdullah, R., Xavier, B. D., Namgung, H., Varghese, V., \u0026amp; Fujiwara, A. (2024). Managing transit-oriented development: A comparative analysis of expert groups and multi-criteria decision making methods. Sustainable Cities and Society, 115, 105871.\u003c/li\u003e\n \u003cli\u003eAkbari, N., \u0026amp; Zahedi, K. (2008). Application of ranking methods and multi-criteria decision-making. Tahran: Publications of the Organization of Municipalities and Villages of the country.\u003c/li\u003e\n \u003cli\u003eAl-Harami, A., \u0026amp; Furlan, R. (2020). Qatar National Museum-Transit oriented development: The masterplan for the urban regeneration of a green TOD. 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Urban planning in Qatar: Strategies and vision for the development of transit villages in Doha. Australian Planner, Volume 53, 2016 - Issue 4, 286-301. doi.org/10.1080/07293682.2016.1259245\u003c/li\u003e\n \u003cli\u003eZhao, Y., Hu, S., \u0026amp; Zhang, M. (2024). Evaluating equitable transit-oriented development (TOD) via the node-place-people model. Transportation Research Part A: Policy and Practice, 185, 104116.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003cp\u003e\u003cspan\u003e1. The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster means are equal.\u003c/span\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Transit-Oriented Development (TOD), TOD Typology, Sustainable Urban Structure, Bus Rapid Transit (BRT), Urban Planning, Spatial Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6941040/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6941040/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTransit-Oriented Development (TOD) has emerged as a transformative strategy in sustainable urban planning, aiming to integrate land use and public transportation to foster compact, walkable, and mixed-use communities centered around high-capacity transit systems. This study explores the spatial characteristics of TOD along the Bus Rapid Transit (BRT) corridor in Tabriz, Iran\u0026mdash;a rapidly growing metropolitan area facing challenges such as urban sprawl, traffic congestion, and inefficient land use. The analysis focuses on four fundamental dimensions of TOD: density, diversity, design, and distance to transit stations. A 600-meter buffer zone was delineated around each BRT station, following international TOD planning standards, to assess the spatial structure and performance of the corridor. Spatial analyses were conducted using ArcGIS, while K-means cluster analysis was performed in SPSS to classify and interpret TOD typologies. The findings indicate significant spatial heterogeneity in TOD indicators across the BRT network. Four distinct TOD typologies were identified, ranging from active TOD zones with high density and land-use mix to areas lacking TOD features entirely. The study highlights the uneven integration of transit infrastructure with surrounding land uses and identifies critical zones with potential for TOD-based redevelopment. By contextualizing TOD principles in a Middle Eastern urban setting, this research contributes to the global discourse on sustainable transport and urban structure. It offers practical insights for urban planners, local authorities, and policymakers seeking to improve land use efficiency, enhance transit accessibility, and promote balanced urban growth in developing cities.\u003c/p\u003e","manuscriptTitle":"Typology of Transit-Oriented Development to Promote Sustainable Urban Structure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 09:12:26","doi":"10.21203/rs.3.rs-6941040/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf9d776e-1cc8-4b61-98c5-df4193becc46","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T02:08:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 09:12:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6941040","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6941040","identity":"rs-6941040","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}