Transit-Oriented Development Typology in Middle East's Metropolitan Context: Iran as a Case Study

Transit-Oriented Development Typology in Middle East's Metropolitan Context: Iran as a Case Study | 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 Transit-Oriented Development Typology in Middle East's Metropolitan Context: Iran as a Case Study Shirin Sabaghi Abkooh, Mohammad Rahim Rahnama This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3968146/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 Recently, there has been a growing global interest in typology as an effective mechanism for streamlining contextual complexities and facilitating the implementation of Transit-Oriented Development (TOD), particularly in the vicinity of rail transportation systems. However, despite the precedence set by early adopters in the field of TOD, countries in the Middle East as newcomers lack comprehensive typological studies. Addressing this research gap, this paper endeavors to devise a TOD typology tailored to the geographical area encompassing a 400-meter radius around 33 active metro stations in Mashhad, Iran. Employing a systematic approach, the study constructs a spatial model integrating the 6D model (encompassing destination, distance, density, diversity, design, and demand management) alongside the k-means cluster analysis technique, thus contributing methodologically to the advancement of TOD typological methodologies. The findings delineate five discernible TOD archetypes, namely “urban neighborhoods,” “city commercial centers,” “specialized healthcare activity centers,” “recreational-educational activity centers,” and “transit centers.” Notably, the station areas categorized as “city commercial centers” exhibit the highest prevalence rate (78.78%). Nonetheless, the identification of the remaining four types bears significance, with the study notably introducing two novel typologies to the extant literature, namely the “specialized healthcare activity center” and the “recreational-educational activity center”, which hold applicability beyond the Iranian context. This research underscores the relevance of TOD typologies in informing urban development strategies and offers insights pertinent to transit-oriented planning endeavors. Transit-Oriented Development (TOD) TOD Typology 6D model Iranian context Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction As a popular urban planning strategy, TOD is considered one of the best examples of integrated transportation and land use planning (Shatu, Aston, Bhavesh Patel, & Kamruzzaman, 2022; Carpentieri, Papa, & Guida, 2021 ). This strategy pursues sustainable urban development within station areas through compact development, high density, mixed land use, and walkable zoning in collaboration with transportation planning (Liu, Zhang, & Xu, 2020 ; Kumar, Sekhar, & Parida, 2020 ). Contrary to the general concept of TOD, the complexities in the built environments around public transportation stations make the implementation of TODs customized for different contexts (Woo, 2021 ; Higgins & Kanaroglou, 2016 ). First of all, this requires simplifying the complexities of the field. One effective solution in this case is the development of TOD typology, which is a method for grouping areas with common characteristics. Similarities within a type enable policymakers and stakeholders to develop common strategies for planning or improving performance (Lyu, Bertolini, & Pfeffer, 2016 ). A realistic typology of TOD requires examining the current conditions of the context. Urban planning rarely starts from a blank space, and the existing built environment is an important factor in determining future development (Higgins & Kanaroglou, 2016 ). Therefore, it is easy to evaluate the performance of the existing conditions against TOD expectations and provide policies to promote TOD and achieve planning goals (Kamruzzaman, Baker, Washington, & Turrell, 2014 ). So far, several studies in the field of TOD typology have been conducted by developed countries such as America, Australia, Europe, and Canada, which are consistent with Western hegemony (Carpentieri, Papa, & Guida, 2021 ; Higgins & Kanaroglou, 2016 ; Kamruzzaman, Baker, Washington, & Turrell, 2014 ; Huang, Grigolon, Madureira, & Brussel, 2018 ; Cervero, Murphy, Ferrell, Goguts, & Tsai, 2004 ). In recent years, knowledge production in this field has involved the participation of several South and East Asian countries, such as India and China (Kumar, Sekhar, & Parida, 2020 ; Lyu, Bertolini, & Pfeffer, 2016 ; Gu, He, Chen, Zegras, & Jiang, 2019 ). However, the process of these studies in newcomer countries in the Middle East, such as Iran, is very slow. With a population of 86.3 million people, Iran is the 18th most populous country in the world. This country has not been excluded from the patterns of urbanization around the world. Iran's urban population has increased from 64.2% in 2000 to 75.9% in 2019 and is expected to reach 85.8% by 2050 (Bahrami, et al., 2023 ). The urbanization growth in Iran has significantly impacted most of its metropolises, leading to urban sprawl influenced by modernity, traditional urban planning patterns, and inappropriate population settlement (Maghsoodi Tilaki, Abdullah, Bahauddin, & Hedayati, 2014 ; Masoumi, 2012 ; Hosseini & Hajilou, 2018 ). One of the negative consequences of the sprawl development pattern is the lack of a dynamic link between the transportation system and land use planning, which leads to the dominance of car use, increasing the distance between work and life, low density, rising traffic congestion, raising gasoline consumption, and increasing environmental pollution (Rahnama & Sabaghi Abkooh, 2021 ). The growth of the urban population and its negative consequences caused the implementation of TOD in Iranian metropolises to be considered as a potential solution in recent decades. What stands out in this regard in Iran is the implementation of measures such as the development of mass transit infrastructure in the built environment of metropolises. Following that, most of the studies focus on the feasibility of TOD realization, identification of the key factors affecting TOD realization, provision of strategies, and evaluation of the role of TOD in environmental reconstruction (Abdi, 2021 ; Ghezeljehmeydan & Hakimi, 2023 ; Ahdnejad, Teymouri, & Doosti yekta, 2022 ; Abdi & Lamíquiz-Daudén, 2020 ; Mirmoghtadaee, 2016 ). However, there have not been many comprehensive studies that include systematic typology in the Iranian context, and the few cases are limited to Tehran, the capital of this country (Haqshenas, 2009 ; Japan International Cooperation Agency, 2018 ). To fill this gap in the TOD literature, this paper develops a typology for the urban context of Iran. In other words, we intend to develop the TOD typology methodology by combining the 6D model and k-mean cluster, to investigate the performance potential of TOD types in built environments located around rail transit stations. For this purpose, Mashhad, as one of the five major metropolises of Iran, with 3,062,242 residents and an annual reception of 30 million tourists in the area of 352 square kilometers of built-up urban areas (Statistical Centre of Iran, 2023 ), has been selected as a study sample. In this study, we focus on the Mashhad metro system as one of the most important backbones of public transportation. Currently, the metro has been operating with two active lines, 33 stations, and about 40 kilometers of track, which serves 4,784,6380 passengers in 13 urban areas of Mashhad (Deputy of Supervision and Engineering of Transport, 2022 ). While Mashhad is considered a case study, the methodology used is innovative in several aspects, has a more general value, and can be applied in other contexts, both in Iran and beyond. Based on the research purpose, the structure of the article is as follows: Section 2 discusses the TOD typology literature review. Section 3 presents the approach adopted for the classification of metro station areas in Mashhad, the details of the TOD indicators, the analysis method, and the introduction of the Mashhad metro. Section 4 shows the typology results and Section 5 discusses them. Finally, in Section 6 , we conclude, provide practical suggestions for improving the performance of different types of TODs in similar contexts of Iran, and suggest directions for future research efforts. 2. Literature review In recent decades, there has been a growing interest in typology development, especially in mass transit station areas, as a tool to find suitable solutions and facilitate future TODs (Huang, Grigolon, Madureira, & Brussel, 2018 ). Each type of station in a built-up area has a set of morphological and functional characteristics. Detailed analysis of these characteristics can help to answer the operational questions of TOD planning and design (Kamruzzaman, Baker, Washington, & Turrell, 2014 ; Zemp, Stauffacher, Lang, & Scholz, 2011 ). In addition, identifying different types of TODs helps to simplify infrastructure management by implementing standards in development operations and coordinating actions (Higgins & Kanaroglou, 2016 ). An overview of studies shows that two general categories, the node-place model and the Ds model, have been adopted to define inputs and identify different types of TODs. Concerning the node-location model, Bertolini ( 1999 ) presented a conceptual framework for the classification of TOD nodes and places based on rail stations in Amsterdam and Utrecht, Netherlands. In this study, he considered connections, frequency, and variety of transportation services as indicators of nodes, and variables such as the number of residents, the number of employees, and the degree of land use diversity as place indicators. The types identified on this basis include access, stress, dependence, and unstable sites. Reusser et al. ( 2008 ) used 11 node and location indicators to classify over 1,600 rail stations in Switzerland into five TOD types: smallest, small, medium in populated areas, medium but unstaffed, and large and very large stations in large urban centers. Chorus and Bertolini ( 2011 ) conducted another study using the mentioned model in the East Asian area for 99 station ranges in Tokyo and showed the same results as Reusser et al. Lyu et al. ( 2016 ) developed the node-place model proposed by Bertolini. They investigated 18 variables in three dimensions, including Transit, Oriented, and Development in the Beijing urban area, and identified six TOD types. By conducting a similar study in the Naples metropolitan area of Italy, six TOD types were developed, including “station areas in the periphery T unbalanced,” “station areas along the regional corridors TOD unbalanced,” “station areas TOD stressed,” “station areas with residential vocation on Vesuvian corridor,” “station areas semi-central TOD oriented,” and “station areas TOD unbalanced” (Carpentieri, Papa, & Guida, 2021 ). In Iran, there are only two TOD typology studies in Tehran using the node-place model. The first time, this discussion seriously started with the plan of station complexes at the same time as the first operated metro lines in 2010. In this project, three TOD types were “regional station,” “extra-regional station,” and “urban station.” Variables included station function, the intersection of the station, passenger numbers, and the existence of developable land uses. The final goal of realizing these residential complexes was to create huge multi-purpose complexes with land use diversity (Haqshenas, 2009 ). Japan International Cooperation Agency ( 2018 ) has also studied the TOD typology in the metropolitan area of Tehran on a wider scale. It defined three TOD types including “TOD regional level,” “TOD corridor level,” and “TOD station level” (i.e. terminal station in the city center, standard station in the city center, core station in the suburbs, and standard station in the suburbs). However, other studies have considered the Ds model as a more accurate way to identify the potential performance of TOD in station areas, which is also the basis of this study (Dittmar & Poticha, 2004 ; Ewing & Cervero, 2010 ). Currently, this model includes six main dimensions: (1) Destination. That plays an important role in TOD. The intelligent placement of destinations within station boundaries facilitates the provision of public transportation services and enables more efficient travel over shorter distances; (2) Density. It is based on the assumption that placing residential buildings near the main transportation nodes, amenities, and workplaces leads to increased convenience and attraction of sustainable modes of transportation. Parallel to the emphasis on higher density in this city development model, two scenarios of high density with 400 people per hectare, and low density with 180 people per hectare are also proposed for the population; (3) Diversity. TODs require a mix of land uses and their degree of balance; (4) Distance. One of the main elements of TOD is creating safe and suitable streets for pedestrians and cyclists. Therefore, it is necessary to create regular street networks and avoid dead-end alleys, winding paths, and constant change of direction; (5) Design. It is important to have good infrastructure for walking and cycling, as well as provide bus stations to increase the level of accessibility along with the variety of land uses; (6) Demand management. One of the most important actions of TOD communities is the management of car parking (i.e. the prohibition of creating parking spaces in the immediate area of stations, and the use of multi-level parking spaces), and creating bicycle parking spaces at origins and destinations (Daisa, 2004 ; Falconer & Richardson, 2010 ; Transportation, 2010 ; TransLink, 2012; Ogra & Ndebele, 2014 ; Ewing & Cervero, 2010 ; Rodríguez & Kangb, 2020 ). However, the previous studies have considered only some dimensions of this model for typology. Dittmar and Poticha ( 2004 ) identified six TOD types, taking into account factors such as density, housing diversity, and transportation services: “urban center,” “urban neighborhood,” “suburban center,” and “suburban neighborhood,” “Transport Station Suburb,” and “urban Center for employees.” Phoenix, Arizona, a study considered 12 indicators in three dimensions including transportation, socio-demographic, and land use. These indicators were reduced to five composite indicators using factor analysis, and finally, 27 studied stations were classified into five types: employment centers, middle-income mixed-use areas, transportation (park-and-ride) nodes, high population/rental areas, and urban poverty areas (Atkinson-Palombo & J. Kuby, 2011 ). Kamruzzaman et al. ( 2014 ) typified 1,734 precincts in Brisbane, and identified four TOD types using two-stage cluster analysis; including “existing neighborhood residential TOD,” “activity center TOD,” “potential TOD,” and “non-TOD.” They used the four dimensions of density, diversity, design, and distance as the basis of the research. A study in Seoul identified four TOD types based on the three dimensions of density, diversity, and design: high-density, moderate-density, compact business district setting, and compact housing (Woo, 2021 ). Taki & Maatouk ( 2018 ) developed three types of regional TOD, urban TOD, and suburban TOD based on the two dimensions of density and diversity in the Jakarta urban area. Huang et al. ( 2018 ) identified five TOD dimensions including density, diversity, destination, and design as the basis of TOD typology in the Arnhem-Nijmegen region, Netherlands. They classified three types: “suburban residential,” “urban residential,” and “urban mixed core.” These studies have paid attention to some or all of the dimensions of the urban structure, and “demand management” as an important and integral pillar of TOD implementation, has not been directly included among the variables. In this study, we have attempted to apply the 6D model to take an operational step in filling this crevice using the available data and considering the sixth dimension of TOD. 3. Data and Method 3.1. Data Description The present study uses spatial data to develop a potential quantitative typology of TOD within a radius of 400 meters of Mashhad railway stations. The data includes 20 TOD factors under the six dimensions (6Ds): destination, distance, design, density, diversity, and demand management. The data sources used are as follows: (1) Various destinations, including commercial centers, hotels, green spaces, educational centers, and healthcare centers, as well as information about parcels, and building density, were obtained from the GIS database of land uses and building floors which was taken from Mashhad Municipality in 2023. (2) The road networks and intersections were developed in QGIS using Google Street Map data in 2023. (3) Transportation demand management factors, including marginal parking lots, number of metro passengers, bicycle stations, public parking, bus stops, and bike lanes, were adopted from the annual report of Mashhad Transport and Traffic Deputy in 2023 and were developed in the QGIS. (4) The population data was also taken from the statistical blocks of the population and housing census in 2017, whose GIS layer was downloaded from the Iranian Statistical Center of Iran website. The study used metro station areas as favorable TOD sites for analysis. Following international standards (Galelo, Ribeiro, & Martinez, 2014 ; Kamruzzaman, Baker, Washington, & Turrell, 2014 ), a 400-meter buffer border was considered with minimum overlapping of the adjacent stations to calculate the TOD factors in Mashhad. Table 1 provides descriptions of the variables. Table 1 TOD variables and their description 6Ds Variable Description Destination Commercial land use Commercial land use area (m 2 ) Hotel Number of hotels Recreational land use Total commercial land use area (m 2 ) Green space Total public parks and green spaces area (m 2 ) Educational land use Total educational centers area (m 2 ) Healthcare land use Total healthcare centers area (m 2 ) Distance Road length Total road length (km) intersection Number of intersections Design Parcels Number of parcels Bike lane Total bike lane length (m) Bus station Number of bus stops Bike station Number of bike stations Density Horizontal density The ratio of the number of plots to the land area per hectare Floors Number of floors Population 2017 Population number of statistical blocks Population density The ratio of population to land area per hectare Diversity Entropy Entropy is calculated with formula 1. Formula 1: \(\text{H}\left(\text{X}\right)={\sum }_{i=1}^{N}\left({PDEN}_{i}*{log}\left(\frac{1}{{PDEN}_{i}}\right)\right)/\text{log}\left(N\right)\) Where PDEN is the ratio of a land use area to the total land use areas, and N is the number of land uses. The Entropy coefficient ranges from 0 to 1, with values close to 1 indicating a more equitable distribution of urban land in diverse activities and values close to 0 indicating the degree of more unbalanced distribution and mix. Demand Management Marginal parking (m) Length of marginal parking lots (m) Metro passengers Number of passengers per metro station in 2023 Public Parking capacity Number of car parking spaces in public parking lots Table 2 presents the mean, standard deviation, and variance of indicators for all stations. Table 2 6D characteristics within 400 meters of metro stations in Mashhad metropolis 6Ds Variable Mean Std. Deviation Variance Destination Commercial land use (m 2 ) 8695.04 10010.90 100218130.21 Hotel 0.09 0.38 0.15 Recreational land use (m 2 ) 332.33 1494.32 2232987.47 Green space 8222.95 20575.02 423331277.73 Educational land use (m 2 ) 23041.69 43082.76 1856124039.43 Healthcare land use (m 2 ) 8437.46 26067.67 679523451.34 Distance Road length 11.17 1.95 3.80 intersection 61.52 22.76 518.07 Design Parcels 612.00 391.15 152995.31 Bike lane 840.23 623.92 389280.20 Bus station 3.82 2.14 4.59 Bike station 0.91 0.72 0.52 Density Horizontal density 12.15 7.78 60.51 Floors 1.50 0.65 0.42 Population 2016 4617.48 3786.98 14341190.32 Population density 91.89 75.36 5679.80 Diversity Entropy 0.58 0.16 0.02 Demand Management Marginal parking (m) 1027.35 735.51 540979.89 Metro passengers 2064154.42 2579620.47 6654441779539.88 Public Parking capacity 201.27 448.67 201309.08 3.3. Methodology Two main steps were taken to classify TOD. First, the standard Z test was applied to equalize the variables. Then, the K-means cluster analysis model was used in SPSS software to cluster the examined stations. The final results of the typology were developed in GIS. Generally, most research on TOD typology has been done using hierarchical or two-step cluster methods. In only one case, the k-means analysis model was the basis of the study (Woo, 2021 ). On the other hand, few studies have paid attention to the standardization of variables (Rodríguez & Kangb, 2020 ). Therefore, this study has also taken a step towards developing methodology by combining these two methods with the 6D model. 3.3.1. Z standard test All data were standardized using the Z standard test (formula 2) to unify the indicators with various scales and reduce errors. Formula 2 $$Z=\frac{{x}_{i}-\stackrel{-}{x}}{\delta }$$ Where \({x}_{i}\) is the variable's size, \(\stackrel{-}{x}\) is the variable's mean, δ is the variable's standard deviation, and Z is the test of the normal distribution of the data. New data were collected from 33 Mashhad metro stations based on the primary data (20 variables) using this formula, and preparations were made for clustering. 3.3.1. K-mean cluster analysis Using an algorithm that can handle a large number of variables, K-means cluster analysis attempts to identify relatively homogeneous groups of cases based on selected characteristics. Accordingly, the number of primary center numbers of the clusters should be determined. Therefore, according to the common typology of TOD stations in the theoretical literature, we identified five clusters. Next, cluster membership, distance information, and final cluster centers were determined. ANOVA test was applied to verify the overall model, which provides information about the contribution of each variable in the separation of groups. Given a set of observations ( x 1 , x 2 , ..., x n ), where each observation is a d -dimensional real vector, k -means clustering aims to partition the n observations into ( k ≤ n ) sets S = { S 1 , S 2 , ..., S k } to minimize the sum of distances for every point toward their cluster centers. It is equivalent to minimizing the following formula. Formula 3 $$\begin{array}{c}argmin\\ s\end{array}\sum _{i=1}^{k}\sum _{x\in {S}_{i}}\Vert x-{\mu }_{i}{\Vert }^{2}=\begin{array}{c}\begin{array}{c}argmin\\ s\end{array}\sum _{i=1}^{k}\mid {S}_{i}\mid Var{S}_{i}\end{array}$$ Where µ i is the mean (also called centroid) of points in S i , i.e. $${\mu }_{i}=\frac{1}{\mid {S}_{i}\mid }\sum _{x\in {S}_{i}}x,$$ ∣S i ∣ is the size of S i , and ‖. ‖ is the usual L 2 norm. This is equivalent to minimizing the pairwise squared deviations of points in the same cluster: $$\begin{array}{c}argmin\\ s\end{array}\sum _{i=1}^{k}\frac{1}{\mid {S}_{i}\mid }\sum _{x,y\in {S}_{i}}\Vert x-y{\Vert }^{2}$$ The equivalence can be deduced from identity \(\mid {S}_{i}\mid \sum _{x\in {S}_{i}}\Vert x-{\mu }_{i}{\Vert }^{2}\) Since the total variance is constant, this is equivalent to maximizing the sum of squared deviations between points in different clusters (between cluster-sum of squares, BCSS). This deterministic relationship is also related to the law of total variance in probability theory. 3.4. Metro system in Mashhad metropolis The study area selected for the present research is that of the Mashhad metropolitan which is the second largest metropolis in Iran, located in the northeast of the country in the Khorasan Razavi province. With an area of ​​310 km 2 , this city has a population of 2,999,897 (Statistical Center of Iran, 2018). To manage the daily trip demands, reduce car usage, integrate transportation, and move toward sustainability, the Traffic and Transportation Organization of Mashhad Municipality drafted the Comprehensive Transportation and Traffic Plan (1994–1997). The most important goals of this plan were to improve transportation services, have access to opportunities, and raise the efficiency of current transportation systems. Therefore, one of the important missions defined for authorities was introducing the major options for transportation facilities. Following the first mission, the establishment of an LRT system with four routes and a total of 85 kilometers was proposed, which can meet a large share of demands with other public transit and non-motorized modes. In this regard, in 1997, as requested by the Transportation and Traffic Organization of Mashhad Municipality, four LRT routes were projected in the city. Currently, the routes 1 and 2 with 33 stations have been operated. Table 3 and Fig. 2 show the present and future lines of the metro system in the Mashhad metropolitan. Since passenger-absorbing hotspots in the urban built environment have been the criteria for determining the location of active stations, the area around them is a suitable case to develop a potential TOD typology. Table 3 Status of construction and development of LRT routes in Mashhad Average number of daily commutes project progress (%) Number of designed or built stations Number of operated stations Number of available wagons Number of required wagons Drilling rate (%) Number of stations Line length (km) Line 1 108500 100 0 22 60 100 100 22 24 Line 2 22000 100 0 11 30 100 100 11 14.9 Line 3 - 6.5 10 0 - 220 58.5 24 28.5 Line 4 - To equip the workshop 15 0 - 200 0 15 18.0 Sum 130500 - 26 36 90 620 - 76 85.0 Source (Bureau of Transportation Studies and Planning, 2021) 4. Results 4.1. Metro Station Typology After standardizing the variables, we discussed the TOD typology. For this purpose, K-means was performed by repeating the dialog table process. Therefore, the cluster centers were compared based on 6D variables, and the stability of the proposed K = 5 cluster(s) solution was confirmed (Table 4 ). To find the optimal center points, the value of the centers of each cluster changes in each iteration. In cluster 1, the values fluctuate and tend to decrease from 6.66 in iteration 1 to 8.743E-13 in iteration 10. The values decrease in cluster 2 from 3.079 in the first iteration to 1.174E-5 in the tenth iteration. But these values in clusters 3 and 4 are 0.000 in all iterations. In the fifth iteration, we see a decrease in values from 1.499 in the first iteration to 7.618E-5 in the tenth iteration. In general, the value for cluster 1 in the fourth iteration, cluster 2 in the fifth iteration, cluster 5 in iteration 9, and clusters 3, 4, and 5 in all iterations has reached zero, meaning that this value for k = 5 was fixed which determines the most significant location of the center. Table 4 Iteration History a Iteration Change in Cluster Centers 1 2 3 4 5 1 6.666 3.079 0.000 0.000 1.499 2 0.247 0.770 0.000 0.000 0.500 3 0.009 0.192 0.000 0.000 0.167 4 0.000 0.048 0.000 0.000 0.056 5 1.254E-5 0.012 0.000 0.000 0.019 6 4.646E-7 0.003 0.000 0.000 0.006 7 1.721E-8 0.001 0.000 0.000 0.002 8 6.373E-10 0.000 0.000 0.000 0.001 9 2.360E-11 4.698E-5 0.000 0.000 0.000 10 8.743E-13 1.174E-5 0.000 0.000 7.618E-5 a. Iterations stopped because the maximum number of iterations was performed. Iterations failed to converge. The maximum absolute coordinate change for any center is 5.527E-5. The current iteration is 10. The minimum distance between initial centers is 9.435. One of the k-means clustering results is the cluster membership assignment to determine the most relevant cluster for each station. Table 5 lists cluster membership and distance information for stations. Distances were calculated by Euclidean distance to measure their similarity, where the closer the distance, the more similar to the cluster center value. Distances are a suitable criterion to measure the similarity within a group and between cluster centers and stations. Since the grouping was accomplished based on these similarities, it is possible to assign cases among multiple groups. Therefore, five clusters were identified for stations. Among 33 metro stations, 26 stations are located in cluster 1 (78.78%), 3 stations in cluster 2 (9.09%), 1 station (Shariati) in cluster 3 (3.03%), 1 station (Melat Park) in cluster 4 (3.03%) and 1 station in cluster 5 (3.03%). Although clusters 2, 3, 4, and 5 have fewer representatives, their results can still be significant. Cluster 1 with the largest number of stations is the main case (Fig. 3 ). Table 5 Cluster Membership Name Cluster Distance Kashaf Rood 1 6.923 Fajr 1 3.886 Nabowat 1 4.284 Shahid Mofateh 1 2.899 Mashhad railway station 1 3.408 Shohada 2 3.153 Sadi 2 3.311 Alandasht 1 2.631 Koohsangi 1 4.664 Shahid Kaveh 1 3.621 Fakouri 1 3.335 West Terminal 5 2.249 Int. exposition 1 4.567 Eghbal-e-Lahouri 1 2.829 Sayyad-e-Shirazi 1 3.641 Sadaf 1 2.211 Haft-e-Tir 1 2.985 Danesh Amooz 1 3.058 Hashemieh 1 3.108 Kowsar 1 2.501 Azadi 1 2.393 Khayyam 1 3.105 Palestin 1 3.105 Taleghani 1 2.278 Qaem 1 3.202 Shariati 3 0.000 Emam Khomeini 1 6.232 Basij 2 4.105 Seventeeth Shahrivar 1 2.108 Parvin Etesami 1 3.050 Ghadir 1 3.519 Airport 5 2.249 Park-e-Mellat 4 0.000 Table 6 and the radar diagram (Fig. 4 ) show clustering results by comparing the standardized values in axes that start from the same central point, indicating the difference between the clusters. Five clusters are ranked based on 6D variables. As can be seen, the stations located in cluster 1 have the highest values related to the recreational land use variables (0.06), the number and density of the population (0.18), and the number of bike stations (0.12). In this cluster, dominance is with some dimensions of destination, density, and demand management. About 70% of the stations along the two metro lines are located in this type. These stations are the place of residence and the focus of local daily activities. Therefore, this cluster can be called the “urban neighborhood.” On average, 5,290 people live in each of the cluster 1 stations. The average population density is 105 people per hectare. The station areas occupy an average of 421 square meters of recreational spaces which include mostly leisure and gathering retail stores such as coffee shops and restaurants. Even though these stations have the highest standard values for bike stations, they have an average of one bicycle station that is less in a radius of 400 meters compared to the population density. In cluster 2, variables of commercial land use, hotels, road networks, plots, intersections, and horizontal building density have the highest standard values compared to other clusters. They include some dimensions of destination, distance, design, and density. The three stations, Shohada, Saadi, and Basij, are in this group. They are named the “city business center.” The average commercial land use is 32,429 square meters, the average number of plots is 1,340, and the horizontal building density is 26 plots per hectare. The average road network length is 11.97 km, and the average intersection number is 102, which is higher than the average of all stations (approximately 62 intersections) and indicates a relatively good level of accessibility. Shariati station is the only station located in cluster 3. In this cluster, the highest standard values are for healthcare centers, bike lanes and stations, floors, metro passengers, public parking lots, and bus stations. They are in the subgroups of destination, distance, density, and demand management, respectively. More than 26% of the 400-meter area of the station is dedicated to all kinds of healthcare centers, including doctors' offices, clinics, pharmacies, and laboratories. Two hospitals with international performance scales are near this station. The average height density is about two floors. Shariati station is the intersection of two metro lines 1 and 2 and near a bus terminal. Therefore, this station is the busiest one in Mashhad and has the traffic of 43,119 metro passengers per day. In addition, this area is the focus of the two public parking lots with a capacity of 1,349 car parking spaces to manage part of the transportation demand. There is a 1,165-meter-long bicycle infrastructure along with various transportation options. But in terms of cycling equipment, there is only one bike station with a capacity of 20 bicycles. Due to the presence of two large healthcare centers, Shariati station can be named a “specialized healthcare activity center”. There is also the only Mellat Park station in cluster 4. In this cluster, the variables of green space, educational land use, entropy coefficient, marginal parking, and bike station have the most standardized values, 4.57, 4.55, 1.28, 1.28, and 0.12, respectively. These variables form a part of the destination, diversity, and demand management dimensions. The two main land uses, Mellat Park and the Ferdowsi University of Mashhad are in the area of this station (i.e. about 64% of the total area). Although a bus terminal exists in the vicinity of Park Mellat station and is the origin of 24 lines in the Mashhad metropolitan area, marginal parking lots play the most prominent role among the variables of demand management; the length of the marginal parking lots in this area is about 1970 meters; higher than the average of all stations. Considering the wide function scale of Mellat Park and the Ferdowsi University of Mashhad, this station can be named a “recreational-educational activity center.” Two stations, “West terminal” and “Airport” are in cluster 5. Although the standardized values in this cluster are low and negative, there are fluctuations among them compared to other clusters. The values of cluster 5 for hotel and recreational use variables are equal to clusters 3 and 4; -0.23 and − 0.22, respectively. Commercial land use variables with − 0.87 and healthcare centers with − 0.32 have the lowest values in clusters 5 and 4. Regarding the green space, the standardized values of cluster 5 are equal to cluster 3 (-0.40). The two stations have the lowest standardized values regarding distance, design (variable of parcels), density (variables of horizontal density and floors), diversity, and Demand management. These two stations are at both ends of Line 1. Although they are in the same cluster, they have different functional scales. The airport station transports national and international passengers to Mashhad, and the western station connects the tourist suburbs to Mashhad at the district level. However, based on the significant role of these stations in transferring passengers between different vehicles, they can be named a “transit center.” Table 6 Final Cluster Centers ANOVA was performed to analyze the effect of variables in formatting clusters. F ratios in Table 7 show that the variables metro passengers (at 125.53) and healthcare land use (at 47.48) have the most significant effect on the clustering of stations, while the least effect is related to the recreational land use variable at 0.97. Table 7 ANOVA 6Ds Variable Cluster Error Mean Square df Mean Square df F Sig. Destination Commercial land use (m 2 ) 5.226 4 0.396 28 13.188 0.000 Hotel 1.863 4 0.879 28 2.120 0.105 Recreational land use (m 2 ) 0.110 4 1.127 28 0.097 0.982 Green space 5.492 4 0.358 28 15.328 0.000 Educational land use (m 2 ) 5.422 4 0.368 28 14.721 0.000 Health center land use (m 2 ) 6.972 4 0.147 28 47.483 0.000 Distance Road length 3.097 4 0.700 28 4.423 0.007 Intersection 3.343 4 0.665 28 5.026 0.004 Design Parcels 4.313 4 0.527 28 8.190 0.000 Bike lane 1.069 4 0.990 28 1.079 0.385 Bus station 2.085 4 0.845 28 2.468 0.068 Bike station 0.986 4 1.002 28 0.983 0.433 Density Horizontal density 4.284 4 0.531 28 8.071 0.000 Floors 2.874 4 0.732 28 3.927 0.012 Population 2016 1.079 4 0.989 28 1.092 0.380 Population density 1.079 4 0.989 28 1.092 0.380 Diversity Entropy 2.713 4 0.748 28 3.625 0.017 Demand Management Marginal parking (m) 1.180 4 0.974 28 1.211 0.328 Metro passengers 7.577 4 0.060 28 125.530 0.000 Public Parking capacity 1.999 4 0.857 28 2.332 0.080 5. Discussion In this part of the research, we discuss the TOD typology results for 33 metro stations in two active lines of 38.9 km in the Mashhad metropolis, Iran. According to the 6D model in the form of 20 variables and the k-means cluster method, five potential TOD types were identified, including “urban neighborhoods,” “city commercial centers,” “specialized healthcare activity centers,” “recreational-educational activity centers,” and “transit centers.” According to Table 5 , 78.78% of the stations are located in the “urban neighborhood” cluster spreading along two metro lines. In these stations, the variables of recreational land use, the number and density of the population, and the bike station have the most significant impact. The characteristics of the stations in this cluster are mainly consistent with the samples studied in America, South Korea, and China. They found that there is a positive relationship between cycling, population density, and public facilities in this type of TOD (Moudon, et al., 2005 ; Zhao, Nielsen, Olafsson, Carstensen, & Meng, 2017 ; Woo, 2021 ). Generally, these stations can be classified into two subgroups in terms of development and demographic characteristics. According to Fig. 3 , 13 stations in this cluster are in the western half of one of the main corridors in the city that connects the west to the east, and four stations are in the southern part. All of them affected by the master plan are following the principles of modern urban planning. That means the diversity dimension is lower than the average of all stations because the most mixed land uses are along the main roads, and the dominance is with residential land uses. The station areas need to be improved in transportation demand management. Although all kinds of public and non-motorized transportation systems exist along with the metro system, private vehicles still dominate, and the significant road length is to private car traffic. So that during peak hours we see traffic congestion and parking of cars around these stations. Nine other stations in this cluster are located in the northeast and east of Mashhad (against the direction of development predicted in the master plan). The area of these stations is among the worn-out and unplanned textures considered a part of the urban service area over time. They experience the highest population density (up to 402 people per hectare), even higher than the maximum recommended standard (Transportation, 2010 ). However, this level of population density has not been in terms of conscious planning. The main reason is the low value of mainly agricultural land uses and the influx of national and international immigrants over the past decades to settle in these areas. Most stations lack all kinds of urban services, parks and open spaces, and healthcare services on a local and regional scale. They also face problems in design due to unplanned growth. The northeastern and eastern corridors are also among the main corridors of the city that pass through the historical core. For this reason, mass transit (metro and BRT) exists in this area at the urban and suburban levels. However, these areas are in worse conditions than the stations in the western part on transportation demand management. On the one hand, the parallel operation of various mass transits has overshadowed their efficiency. On the other hand, the weakness in parking management and non-motorized transportation infrastructure is quite evident. The three stations, Shohada, Saadi, and Basij, are classified as the “city commercial center.” According to Table 6 , the influencing variables in this type are commercial and residential land uses, parcels, horizontal building density, road network, and intersections. The stations are among the closest ones to the historical-religious core and are the center of attraction for tourists. There are many shopping centers, specialized lines selling healthcare and audio-visual goods, hotels, and historical places in their area. Basij station is the intersection of BRT and rail lines that provides access to the intercity passenger terminal and the airport. In general, this type of TOD is very similar to the regional TOD type in the Jakarta Metropolitan Region and the Urban commercial core type in Delhi; thematic mixed land uses and the predominance of the built environment are evident. Hence, they are highly accessible for job opportunities (Kumara, Sekhar, & Parida, 2020 ; Taki & Maatouk, 2018 ). Despite the positive characteristics of the development of TOD in this cluster, the boundaries of these stations need strengthening on non-motorized transportation infrastructure, especially bike lanes and stations, and building density. The third cluster, “specialized healthcare activity center”, includes only one station. For two reasons, the station is considered one of the crucial travel destinations and has more metro passengers than others. First, it is the intersection of two metro lines. Secondly, two hospitals and other healthcare centers are in the station area. Distance and demand management dimensions have better conditions in this cluster than others. So, we see multi-story public parking lots, the limitation of marginal parking time on the main roads, and other public and non-motorized transportation options. But what is evident during peak hours in the area is the high traffic congestion. Although the height density factor is significant, it is still far from the standards of 4 to 10 floors in a radius of 400 meters (Transportation, 2010 ). The fourth cluster, the “recreational-educational activity center”, also includes only one station. This station is one of the destinations for leisure and higher education. On the one hand, a park with an urban function scale and an approximately 70-hectare area have turned this station into one of the recreational destinations for citizens and tourists in Mashhad. On the other hand, Ferdowsi University of Mashhad and Mashhad University of Medical Sciences, as Iran's first-level universities, are the destinations of about 33,300 domestic and international students and 1,711 faculty members in most of the year (nine months of the year). The diversity of land uses is also higher in this cluster compared to 47 neighborhoods in Delhi (in the range of 0.56–0.75) and 24 Chinese cities (0.58) (Kumara, Sekhar, & Parida, 2020 ; Gu, He, Chen, Zegras, & Jiang, 2019 ). The main reason is the development of mixed commercial, office, and residential land uses in the last two decades. What should be noted about clusters 3 and 4 is that some studies have generally identified one type of TOD as a “specialized activity center” (Department of Infrastructure and Planning, 2010 ). However, a category does not exist exclusively. Therefore, this study filled this gap by developing a typology for similar examples in other cities and countries. The “Transit Center” cluster stations include West Terminal and Airport at both ends of Mashhad Metro Line 1. They have lower standard values than other clusters in all 6D dimensions. The findings of this part of the study are similar to the characteristics of the C2 type in Beijing, China, the Airport type in the Toronto area, Canada, and the terminal station type in Espadanal de Azambuja, Portugal, which are on the edge of the urban area, and all TOD characteristics are low (Lyu, Bertolini, & Pfeffer, 2016 ; Higgins & Kanaroglou, 2016 ; Galelo, Ribeiro, & Martinez, 2014 ). Although these two stations are in the same cluster, they are distinct in the function scale. The west terminal station is the first station of line 1 of the Mashhad metro at the end of Vakil Abad Boulevard, the intersection of Mashhad and two suburban cities with the function of tourism and leisure. This station is near the Vakil Abad bus terminal with the 14 Mashhad and suburb bus lines. The Airport station is the second busiest airport in the country. The annual capacity of the airport's national and international passengers is about 8 million people. Within the vicinity of this station, high-speed buses, city taxis, and subways allow passengers to access different parts of the city, which is a positive step in reducing the use of private vehicles and energy consumption. According to their functional nature, the station areas only provide urban services, including goods and passenger transportation. 6. Conclusion The current study determined that based on the 6D model, metro station areas in Mashhad metropolis have the potential to develop five TOD clusters: cluster 1, “urban neighborhood;” cluster 2, “city business center;” cluster 3, “specialized healthcare activity center;” cluster 4, “recreational-educational activity center;” and cluster 5, “Transit center.” In some cases, this typology has similarities with the types identified in American, Indian, Chinese, Canadian, and South Korean cities. Based on the findings, the various dimensions need to be developed to become successful TODs. Therefore, we have provided suggestions for the TOD implementation based on the function of station areas, which can be used in other newcomer countries with similar contexts. Adopting practical strategies to expand educational centers, medical services such as equipped para clinics, green space on a regional scale, and locating residential centers, especially in the station areas adjacent to clusters 3 and 4, will help in both increasing the number of destinations and adjustment in their distribution. Hence, this will reduce the travel distance and increase the level of access of residents and health tourists to the station areas. Two other dimensions are diversity and density. Considering the population density in the type of “urban neighborhood,” it is necessary to adopt incentive policies to increase the building density as much as possible to diversify non-residential activities. This is especially important in the station areas that include worn-out and underprivileged urban tissues and have a significant population by adopting the urban improvement and renovation approach based on TOD principles, granting low-interest loans, and joint construction projects as public-private partnerships. Implementing such policies will significantly respond to the daily needs of the residents in the station areas or adjacent to them. It helps to improve the design and increase the access distances. In the type of specialized healthcare activity centers, due to the relatively favorable conditions of building density, it is suggested to compensate for the diversity by applying incentive policies such as the right to transfer the development and expansion of complementary activities in the health and medical field. Applying such policies involves the type of recreational-educational activity center to increase building density in both residential and commercial sectors . Other measures suggested to implement successful TOD in station areas are improving various aspects of transportation demand management. These areas witness a range of marginal parking lots used in some places with a limited time during the day, especially in clusters 2, 3, and 4, and all the time in the other clusters. They encourage users to use cars and reduce the efficiency of the public transportation system (rail and bus). In this regard, it is suggested to reduce and eliminate these types of parking lots. Another practical policy for parking management is to create and increase the capacity of multi-story public parking lots at the 400-meter edge of stations in all clusters to minimize their functional interference with the public transportation system while serving car users. Developing the bicycle infrastructure and facilities is suggested in all clusters to promote non-motorized vehicles as much as possible in integration with public transportation. Stations in the “transit center” type need to be prompted in all dimensions of TOD. These stations act as entrance and exit gates of the city and must play their role well in the national and international arenas. So that everyone can get to know the capabilities and the valuable points by entering the station areas. Therefore, this requires the development of large and luxury shopping centers, leisure centers, and accommodation centers such as hotels in the form of optimal designs with acceptable access distances. In parallel with these changes, it is necessary to take measures in line with the integrated development of various sustainable transportation options (e.g., increase of bus stations, development of bicycle infrastructure and facilities, development of road network, replacement of multi-story parking lots instead of surface and marginal parking lots). Based on the results of this study, further studies can focus on strategic planning of various TOD types. It is also suggested that spatial analysis and optimal location of travel destinations within a radius of 400 to 800 meters of rail corridors would be the basis of future studies to improve accessibility. Eventually, considering the nonlinear effects of 6D variables on residents' travel behavior among TOD types can improve the study findings. Declarations Disclosure and declaration statement The authors report there are no competing interests to declare. Data availability statement All data used in this study are fully available and can be reviewed for verification by others. Acknowledgments The authors acknowledge the Mashhad Municipality for providing the GIS database to collect part of this article's data. Authors’ Contributions Shirin Sabaghi Abkooh was responsible for conceptualization, methodology, data, formal analysis, interpretation, and original draft preparation. 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Policy. 64 , 1–11 (2017). https://doi 10.1016/j.tranpol.2018.01.018 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3968146","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273681077,"identity":"2459595d-fd34-4c04-abc4-cf96ffde49ec","order_by":0,"name":"Shirin Sabaghi Abkooh","email":"","orcid":"","institution":"Ferdowsi University of Mashhad","correspondingAuthor":false,"prefix":"","firstName":"Shirin","middleName":"Sabaghi","lastName":"Abkooh","suffix":""},{"id":273681078,"identity":"a1873ccf-af0e-4071-82d7-d4e0106b070f","order_by":1,"name":"Mohammad Rahim Rahnama","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYFACHjApB+VJEK/FmHQtiQ1EO8u8/eyxBz9z7NI3XDvA+OEHg0U+QS0yZ/LSDXu3JeduuJ3ALNnDIGFJ0DoJhhwzCd5tzCAtDNJAvgFBWyT435hJ/t1Wn24AtOU3cVokcsykebcdTgBqYSPSFok3ZtKy244bzryd2GbZY0CUw3LMJN9uq5bnu518+MaPijrCWpAAYwMDA0kaRsEoGAWjYBTgBAC7PTJIAj1XawAAAABJRU5ErkJggg==","orcid":"","institution":"Ferdowsi University of Mashhad","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Rahim","lastName":"Rahnama","suffix":""}],"badges":[],"createdAt":"2024-02-18 22:14:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3968146/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3968146/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51443810,"identity":"0b4b44b4-3c0a-459e-b63f-b6a022dcff0c","added_by":"auto","created_at":"2024-02-21 18:01:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60329,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3968146/v1/0de61cb4c2e27f4d93814d0e.jpg"},{"id":51442744,"identity":"acec6d3f-58ee-47a5-baab-efa8fb82e525","added_by":"auto","created_at":"2024-02-21 17:53:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153373,"visible":true,"origin":"","legend":"\u003cp\u003ePresent and future lines of the metro system in the Mashhad metropolis, Iran\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3968146/v1/29e0cd0659358b32602e1ab0.jpg"},{"id":51442742,"identity":"4c82cd3d-811b-4fd2-908b-4ed12fbd6e7f","added_by":"auto","created_at":"2024-02-21 17:53:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128419,"visible":true,"origin":"","legend":"\u003cp\u003eMembership status of stations in five clusters\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3968146/v1/906a6d4a2487a653ce49b005.jpg"},{"id":51443812,"identity":"f34ae430-bfd9-42d7-9333-1889a7f59fe6","added_by":"auto","created_at":"2024-02-21 18:01:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":63105,"visible":true,"origin":"","legend":"\u003cp\u003eRadar diagram of the standardized values for cluster types\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3968146/v1/8b272b9f60b68560c7cb6d9b.jpg"},{"id":53523996,"identity":"a4ff7eed-bf0d-4763-af19-3253175fe73f","added_by":"auto","created_at":"2024-03-27 04:32:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":847152,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3968146/v1/3eda27e6-805f-4e05-bfe5-8e144d70b00b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transit-Oriented Development Typology in Middle East's Metropolitan Context: Iran as a Case Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs a popular urban planning strategy, TOD is considered one of the best examples of integrated transportation and land use planning (Shatu, Aston, Bhavesh Patel, \u0026amp; Kamruzzaman, 2022; Carpentieri, Papa, \u0026amp; Guida, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This strategy pursues sustainable urban development within station areas through compact development, high density, mixed land use, and walkable zoning in collaboration with transportation planning (Liu, Zhang, \u0026amp; Xu, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kumar, Sekhar, \u0026amp; Parida, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Contrary to the general concept of TOD, the complexities in the built environments around public transportation stations make the implementation of TODs customized for different contexts (Woo, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Higgins \u0026amp; Kanaroglou, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). First of all, this requires simplifying the complexities of the field. One effective solution in this case is the development of TOD typology, which is a method for grouping areas with common characteristics. Similarities within a type enable policymakers and stakeholders to develop common strategies for planning or improving performance (Lyu, Bertolini, \u0026amp; Pfeffer, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A realistic typology of TOD requires examining the current conditions of the context. Urban planning rarely starts from a blank space, and the existing built environment is an important factor in determining future development (Higgins \u0026amp; Kanaroglou, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, it is easy to evaluate the performance of the existing conditions against TOD expectations and provide policies to promote TOD and achieve planning goals (Kamruzzaman, Baker, Washington, \u0026amp; Turrell, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSo far, several studies in the field of TOD typology have been conducted by developed countries such as America, Australia, Europe, and Canada, which are consistent with Western hegemony (Carpentieri, Papa, \u0026amp; Guida, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Higgins \u0026amp; Kanaroglou, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kamruzzaman, Baker, Washington, \u0026amp; Turrell, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Huang, Grigolon, Madureira, \u0026amp; Brussel, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cervero, Murphy, Ferrell, Goguts, \u0026amp; Tsai, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In recent years, knowledge production in this field has involved the participation of several South and East Asian countries, such as India and China (Kumar, Sekhar, \u0026amp; Parida, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lyu, Bertolini, \u0026amp; Pfeffer, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gu, He, Chen, Zegras, \u0026amp; Jiang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the process of these studies in newcomer countries in the Middle East, such as Iran, is very slow.\u003c/p\u003e \u003cp\u003eWith a population of 86.3\u0026nbsp;million people, Iran is the 18th most populous country in the world. This country has not been excluded from the patterns of urbanization around the world. Iran's urban population has increased from 64.2% in 2000 to 75.9% in 2019 and is expected to reach 85.8% by 2050 (Bahrami, et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The urbanization growth in Iran has significantly impacted most of its metropolises, leading to urban sprawl influenced by modernity, traditional urban planning patterns, and inappropriate population settlement (Maghsoodi Tilaki, Abdullah, Bahauddin, \u0026amp; Hedayati, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Masoumi, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hosseini \u0026amp; Hajilou, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). One of the negative consequences of the sprawl development pattern is the lack of a dynamic link between the transportation system and land use planning, which leads to the dominance of car use, increasing the distance between work and life, low density, rising traffic congestion, raising gasoline consumption, and increasing environmental pollution (Rahnama \u0026amp; Sabaghi Abkooh, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The growth of the urban population and its negative consequences caused the implementation of TOD in Iranian metropolises to be considered as a potential solution in recent decades. What stands out in this regard in Iran is the implementation of measures such as the development of mass transit infrastructure in the built environment of metropolises. Following that, most of the studies focus on the feasibility of TOD realization, identification of the key factors affecting TOD realization, provision of strategies, and evaluation of the role of TOD in environmental reconstruction (Abdi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ghezeljehmeydan \u0026amp; Hakimi, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ahdnejad, Teymouri, \u0026amp; Doosti yekta, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Abdi \u0026amp; Lam\u0026iacute;quiz-Daud\u0026eacute;n, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mirmoghtadaee, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, there have not been many comprehensive studies that include systematic typology in the Iranian context, and the few cases are limited to Tehran, the capital of this country (Haqshenas, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Japan International Cooperation Agency, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo fill this gap in the TOD literature, this paper develops a typology for the urban context of Iran. In other words, we intend to develop the TOD typology methodology by combining the 6D model and k-mean cluster, to investigate the performance potential of TOD types in built environments located around rail transit stations. For this purpose, Mashhad, as one of the five major metropolises of Iran, with 3,062,242 residents and an annual reception of 30\u0026nbsp;million tourists in the area of 352 square kilometers of built-up urban areas (Statistical Centre of Iran, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), has been selected as a study sample. In this study, we focus on the Mashhad metro system as one of the most important backbones of public transportation. Currently, the metro has been operating with two active lines, 33 stations, and about 40 kilometers of track, which serves 4,784,6380 passengers in 13 urban areas of Mashhad (Deputy of Supervision and Engineering of Transport, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While Mashhad is considered a case study, the methodology used is innovative in several aspects, has a more general value, and can be applied in other contexts, both in Iran and beyond.\u003c/p\u003e \u003cp\u003eBased on the research purpose, the structure of the article is as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e discusses the TOD typology literature review. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the approach adopted for the classification of metro station areas in Mashhad, the details of the TOD indicators, the analysis method, and the introduction of the Mashhad metro. Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the typology results and Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e5\u003c/span\u003e discusses them. Finally, in Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e6\u003c/span\u003e, we conclude, provide practical suggestions for improving the performance of different types of TODs in similar contexts of Iran, and suggest directions for future research efforts.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eIn recent decades, there has been a growing interest in typology development, especially in mass transit station areas, as a tool to find suitable solutions and facilitate future TODs (Huang, Grigolon, Madureira, \u0026amp; Brussel, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Each type of station in a built-up area has a set of morphological and functional characteristics. Detailed analysis of these characteristics can help to answer the operational questions of TOD planning and design (Kamruzzaman, Baker, Washington, \u0026amp; Turrell, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zemp, Stauffacher, Lang, \u0026amp; Scholz, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition, identifying different types of TODs helps to simplify infrastructure management by implementing standards in development operations and coordinating actions (Higgins \u0026amp; Kanaroglou, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn overview of studies shows that two general categories, the node-place model and the Ds model, have been adopted to define inputs and identify different types of TODs.\u003c/p\u003e \u003cp\u003eConcerning the node-location model, Bertolini (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) presented a conceptual framework for the classification of TOD nodes and places based on rail stations in Amsterdam and Utrecht, Netherlands. In this study, he considered connections, frequency, and variety of transportation services as indicators of nodes, and variables such as the number of residents, the number of employees, and the degree of land use diversity as place indicators. The types identified on this basis include access, stress, dependence, and unstable sites. Reusser et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) used 11 node and location indicators to classify over 1,600 rail stations in Switzerland into five TOD types: smallest, small, medium in populated areas, medium but unstaffed, and large and very large stations in large urban centers. Chorus and Bertolini (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) conducted another study using the mentioned model in the East Asian area for 99 station ranges in Tokyo and showed the same results as Reusser et al. Lyu et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) developed the node-place model proposed by Bertolini. They investigated 18 variables in three dimensions, including Transit, Oriented, and Development in the Beijing urban area, and identified six TOD types. By conducting a similar study in the Naples metropolitan area of Italy, six TOD types were developed, including \u0026ldquo;station areas in the periphery T unbalanced,\u0026rdquo; \u0026ldquo;station areas along the regional corridors TOD unbalanced,\u0026rdquo; \u0026ldquo;station areas TOD stressed,\u0026rdquo; \u0026ldquo;station areas with residential vocation on Vesuvian corridor,\u0026rdquo; \u0026ldquo;station areas semi-central TOD oriented,\u0026rdquo; and \u0026ldquo;station areas TOD unbalanced\u0026rdquo; (Carpentieri, Papa, \u0026amp; Guida, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Iran, there are only two TOD typology studies in Tehran using the node-place model. The first time, this discussion seriously started with the plan of station complexes at the same time as the first operated metro lines in 2010. In this project, three TOD types were \u0026ldquo;regional station,\u0026rdquo; \u0026ldquo;extra-regional station,\u0026rdquo; and \u0026ldquo;urban station.\u0026rdquo; Variables included station function, the intersection of the station, passenger numbers, and the existence of developable land uses. The final goal of realizing these residential complexes was to create huge multi-purpose complexes with land use diversity (Haqshenas, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Japan International Cooperation Agency (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) has also studied the TOD typology in the metropolitan area of Tehran on a wider scale. It defined three TOD types including \u0026ldquo;TOD regional level,\u0026rdquo; \u0026ldquo;TOD corridor level,\u0026rdquo; and \u0026ldquo;TOD station level\u0026rdquo; (i.e. terminal station in the city center, standard station in the city center, core station in the suburbs, and standard station in the suburbs).\u003c/p\u003e \u003cp\u003eHowever, other studies have considered the Ds model as a more accurate way to identify the potential performance of TOD in station areas, which is also the basis of this study (Dittmar \u0026amp; Poticha, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ewing \u0026amp; Cervero, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, this model includes six main dimensions: (1) \u003cem\u003eDestination.\u003c/em\u003e That plays an important role in TOD. The intelligent placement of destinations within station boundaries facilitates the provision of public transportation services and enables more efficient travel over shorter distances; (2) \u003cem\u003eDensity.\u003c/em\u003e It is based on the assumption that placing residential buildings near the main transportation nodes, amenities, and workplaces leads to increased convenience and attraction of sustainable modes of transportation. Parallel to the emphasis on higher density in this city development model, two scenarios of high density with 400 people per hectare, and low density with 180 people per hectare are also proposed for the population; (3) \u003cem\u003eDiversity.\u003c/em\u003e TODs require a mix of land uses and their degree of balance; (4) \u003cem\u003eDistance.\u003c/em\u003e One of the main elements of TOD is creating safe and suitable streets for pedestrians and cyclists. Therefore, it is necessary to create regular street networks and avoid dead-end alleys, winding paths, and constant change of direction; (5) \u003cem\u003eDesign.\u003c/em\u003e It is important to have good infrastructure for walking and cycling, as well as provide bus stations to increase the level of accessibility along with the variety of land uses; (6) \u003cem\u003eDemand management.\u003c/em\u003e One of the most important actions of TOD communities is the management of car parking (i.e. the prohibition of creating parking spaces in the immediate area of stations, and the use of multi-level parking spaces), and creating bicycle parking spaces at origins and destinations (Daisa, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Falconer \u0026amp; Richardson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Transportation, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; TransLink, 2012; Ogra \u0026amp; Ndebele, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ewing \u0026amp; Cervero, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rodr\u0026iacute;guez \u0026amp; Kangb, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the previous studies have considered only some dimensions of this model for typology. Dittmar and Poticha (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) identified six TOD types, taking into account factors such as density, housing diversity, and transportation services: \u0026ldquo;urban center,\u0026rdquo; \u0026ldquo;urban neighborhood,\u0026rdquo; \u0026ldquo;suburban center,\u0026rdquo; and \u0026ldquo;suburban neighborhood,\u0026rdquo; \u0026ldquo;Transport Station Suburb,\u0026rdquo; and \u0026ldquo;urban Center for employees.\u0026rdquo; Phoenix, Arizona, a study considered 12 indicators in three dimensions including transportation, socio-demographic, and land use. These indicators were reduced to five composite indicators using factor analysis, and finally, 27 studied stations were classified into five types: employment centers, middle-income mixed-use areas, transportation (park-and-ride) nodes, high population/rental areas, and urban poverty areas (Atkinson-Palombo \u0026amp; J. Kuby, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Kamruzzaman et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) typified 1,734 precincts in Brisbane, and identified four TOD types using two-stage cluster analysis; including \u0026ldquo;existing neighborhood residential TOD,\u0026rdquo; \u0026ldquo;activity center TOD,\u0026rdquo; \u0026ldquo;potential TOD,\u0026rdquo; and \u0026ldquo;non-TOD.\u0026rdquo; They used the four dimensions of density, diversity, design, and distance as the basis of the research.\u003c/p\u003e \u003cp\u003eA study in Seoul identified four TOD types based on the three dimensions of density, diversity, and design: high-density, moderate-density, compact business district setting, and compact housing (Woo, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Taki \u0026amp; Maatouk (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) developed three types of regional TOD, urban TOD, and suburban TOD based on the two dimensions of density and diversity in the Jakarta urban area. Huang et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) identified five TOD dimensions including density, diversity, destination, and design as the basis of TOD typology in the Arnhem-Nijmegen region, Netherlands. They classified three types: \u0026ldquo;suburban residential,\u0026rdquo; \u0026ldquo;urban residential,\u0026rdquo; and \u0026ldquo;urban mixed core.\u0026rdquo;\u003c/p\u003e \u003cp\u003eThese studies have paid attention to some or all of the dimensions of the urban structure, and \u0026ldquo;demand management\u0026rdquo; as an important and integral pillar of TOD implementation, has not been directly included among the variables. In this study, we have attempted to apply the 6D model to take an operational step in filling this crevice using the available data and considering the sixth dimension of TOD.\u003c/p\u003e"},{"header":"3. Data and Method","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data Description\u003c/h2\u003e \u003cp\u003eThe present study uses spatial data to develop a potential quantitative typology of TOD within a radius of 400 meters of Mashhad railway stations. The data includes 20 TOD factors under the six dimensions (6Ds): destination, distance, design, density, diversity, and demand management. The data sources used are as follows:\u003c/p\u003e \u003cp\u003e(1) Various destinations, including commercial centers, hotels, green spaces, educational centers, and healthcare centers, as well as information about parcels, and building density, were obtained from the GIS database of land uses and building floors which was taken from Mashhad Municipality in 2023.\u003c/p\u003e \u003cp\u003e(2) The road networks and intersections were developed in QGIS using Google Street Map data in 2023.\u003c/p\u003e \u003cp\u003e(3) Transportation demand management factors, including marginal parking lots, number of metro passengers, bicycle stations, public parking, bus stops, and bike lanes, were adopted from the annual report of Mashhad Transport and Traffic Deputy in 2023 and were developed in the QGIS.\u003c/p\u003e \u003cp\u003e(4) The population data was also taken from the statistical blocks of the population and housing census in 2017, whose GIS layer was downloaded from the Iranian Statistical Center of Iran website.\u003c/p\u003e \u003cp\u003eThe study used metro station areas as favorable TOD sites for analysis. Following international standards (Galelo, Ribeiro, \u0026amp; Martinez, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kamruzzaman, Baker, Washington, \u0026amp; Turrell, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), a 400-meter buffer border was considered with minimum overlapping of the adjacent stations to calculate the TOD factors in Mashhad. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides descriptions of the variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTOD variables and their description\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6Ds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eDestination\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommercial land use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial land use area (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHotel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of hotels\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecreational land use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal commercial land use area (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal public parks and green spaces area (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducational land use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal educational centers area (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthcare land use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal healthcare centers area (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDistance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoad length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal road length (km)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eintersection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of intersections\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eDesign\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParcels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of parcels\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBike lane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal bike lane length (m)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBus station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of bus stops\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBike station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of bike stations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eDensity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHorizontal density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe ratio of the number of plots to the land area per hectare\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFloors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of floors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation number of statistical blocks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe ratio of population to land area per hectare\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiversity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEntropy is calculated with formula 1.\u003c/p\u003e \u003cp\u003eFormula 1:\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{H}\\left(\\text{X}\\right)={\\sum }_{i=1}^{N}\\left({PDEN}_{i}*{log}\\left(\\frac{1}{{PDEN}_{i}}\\right)\\right)/\\text{log}\\left(N\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere PDEN is the ratio of a land use area to the total land use areas, and N is the number of land uses. The Entropy coefficient ranges from 0 to 1, with values close to 1 indicating a more equitable distribution of urban land in diverse activities and values close to 0 indicating the degree of more unbalanced distribution and mix.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eDemand Management\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarginal parking (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLength of marginal parking lots (m)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetro passengers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of passengers per metro station in 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic Parking capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of car parking spaces in public parking lots\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the mean, standard deviation, and variance of indicators for all stations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e6D characteristics within 400 meters of metro stations in Mashhad metropolis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6Ds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eDestination\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommercial land use (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8695.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10010.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100218130.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHotel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecreational land use (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e332.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1494.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2232987.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8222.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20575.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e423331277.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducational land use (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23041.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43082.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1856124039.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthcare land use (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8437.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26067.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e679523451.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDistance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoad length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eintersection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e518.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eDesign\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParcels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e612.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e391.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e152995.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBike lane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e840.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e623.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e389280.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBus station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBike station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eDensity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHorizontal density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFloors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4617.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3786.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14341190.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5679.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiversity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eDemand Management\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarginal parking (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1027.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e735.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e540979.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetro passengers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2064154.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2579620.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6654441779539.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic Parking capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e448.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e201309.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Methodology\u003c/h2\u003e \u003cp\u003eTwo main steps were taken to classify TOD. First, the standard Z test was applied to equalize the variables. Then, the K-means cluster analysis model was used in SPSS software to cluster the examined stations. The final results of the typology were developed in GIS. Generally, most research on TOD typology has been done using hierarchical or two-step cluster methods. In only one case, the k-means analysis model was the basis of the study (Woo, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, few studies have paid attention to the standardization of variables (Rodr\u0026iacute;guez \u0026amp; Kangb, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, this study has also taken a step towards developing methodology by combining these two methods with the 6D model.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Z standard test\u003c/h2\u003e \u003cp\u003eAll data were standardized using the Z standard test (formula 2) to unify the indicators with various scales and reduce errors.\u003c/p\u003e \u003cp\u003eFormula 2\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$Z=\\frac{{x}_{i}-\\stackrel{-}{x}}{\\delta }$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the variable's size, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e is the variable's mean, δ is the variable's standard deviation, and Z is the test of the normal distribution of the data.\u003c/p\u003e \u003cp\u003eNew data were collected from 33 Mashhad metro stations based on the primary data (20 variables) using this formula, and preparations were made for clustering.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. K-mean cluster analysis\u003c/h2\u003e \u003cp\u003eUsing an algorithm that can handle a large number of variables, K-means cluster analysis attempts to identify relatively homogeneous groups of cases based on selected characteristics. Accordingly, the number of primary center numbers of the clusters should be determined. Therefore, according to the common typology of TOD stations in the theoretical literature, we identified five clusters. Next, cluster membership, distance information, and final cluster centers were determined. ANOVA test was applied to verify the overall model, which provides information about the contribution of each variable in the separation of groups.\u003c/p\u003e \u003cp\u003eGiven a set of observations (\u003cb\u003ex\u003c/b\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cb\u003ex\u003c/b\u003e\u003csub\u003e2\u003c/sub\u003e, ..., \u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e), where each observation is a \u003cem\u003ed\u003c/em\u003e-dimensional real vector, \u003cem\u003ek\u003c/em\u003e-means clustering aims to partition the \u003cem\u003en\u003c/em\u003e observations into (\u003cem\u003ek\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003en\u003c/em\u003e) sets \u003cb\u003eS\u003c/b\u003e = {\u003cem\u003eS\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003eS\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e, ..., \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e} to minimize the sum of distances for every point toward their cluster centers. It is equivalent to minimizing the following formula.\u003c/p\u003e \u003cp\u003eFormula 3\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}argmin\\\\ s\\end{array}\\sum _{i=1}^{k}\\sum _{x\\in {S}_{i}}\\Vert x-{\\mu }_{i}{\\Vert }^{2}=\\begin{array}{c}\\begin{array}{c}argmin\\\\ s\\end{array}\\sum _{i=1}^{k}\\mid {S}_{i}\\mid Var{S}_{i}\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u0026micro;\u003csub\u003ei\u003c/sub\u003e is the mean (also called centroid) of points in S\u003csub\u003ei\u003c/sub\u003e, i.e.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${\\mu }_{i}=\\frac{1}{\\mid {S}_{i}\\mid }\\sum _{x\\in {S}_{i}}x,$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e∣S\u003csub\u003ei\u003c/sub\u003e∣ is the size of S\u003csub\u003ei\u003c/sub\u003e, and ‖. ‖ is the usual L\u003csup\u003e2\u003c/sup\u003e norm. This is equivalent to minimizing the pairwise squared deviations of points in the same cluster:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}argmin\\\\ s\\end{array}\\sum _{i=1}^{k}\\frac{1}{\\mid {S}_{i}\\mid }\\sum _{x,y\\in {S}_{i}}\\Vert x-y{\\Vert }^{2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe equivalence can be deduced from identity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mid {S}_{i}\\mid \\sum _{x\\in {S}_{i}}\\Vert x-{\\mu }_{i}{\\Vert }^{2}\\)\u003c/span\u003e\u003c/span\u003e Since the total variance is constant, this is equivalent to maximizing the sum of squared deviations between points in different clusters (between cluster-sum of squares, BCSS).\u003c/p\u003e \u003cp\u003eThis deterministic relationship is also related to the law of total variance in probability theory.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Metro system in Mashhad metropolis\u003c/h2\u003e \u003cp\u003eThe study area selected for the present research is that of the Mashhad metropolitan which is the second largest metropolis in Iran, located in the northeast of the country in the Khorasan Razavi province. With an area of ​​310 km\u003csup\u003e2\u003c/sup\u003e, this city has a population of 2,999,897 (Statistical Center of Iran, 2018). To manage the daily trip demands, reduce car usage, integrate transportation, and move toward sustainability, the Traffic and Transportation Organization of Mashhad Municipality drafted the Comprehensive Transportation and Traffic Plan (1994\u0026ndash;1997). The most important goals of this plan were to improve transportation services, have access to opportunities, and raise the efficiency of current transportation systems. Therefore, one of the important missions defined for authorities was introducing the major options for transportation facilities. Following the first mission, the establishment of an LRT system with four routes and a total of 85 kilometers was proposed, which can meet a large share of demands with other public transit and non-motorized modes. In this regard, in 1997, as requested by the Transportation and Traffic Organization of Mashhad Municipality, four LRT routes were projected in the city. Currently, the routes 1 and 2 with 33 stations have been operated. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the present and future lines of the metro system in the Mashhad metropolitan.\u003c/p\u003e \u003cp\u003eSince passenger-absorbing hotspots in the urban built environment have been the criteria for determining the location of active stations, the area around them is a suitable case to develop a potential TOD typology.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatus of construction and development of LRT routes in Mashhad\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage number of daily commutes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproject progress (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of designed or built stations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of operated stations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of available wagons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumber of required wagons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eDrilling rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNumber of stations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLine length\u003c/p\u003e \u003cp\u003e(km)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo equip the workshop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e130500\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e620\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003e76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e85.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003e(Bureau of Transportation Studies and Planning, 2021)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1. Metro Station Typology\u003c/h2\u003e\n \u003cp\u003eAfter standardizing the variables, we discussed the TOD typology. For this purpose, K-means was performed by repeating the dialog table process. Therefore, the cluster centers were compared based on 6D variables, and the stability of the proposed K\u0026thinsp;=\u0026thinsp;5 cluster(s) solution was confirmed (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTo find the optimal center points, the value of the centers of each cluster changes in each iteration. In cluster 1, the values fluctuate and tend to decrease from 6.66 in iteration 1 to 8.743E-13 in iteration 10. The values decrease in cluster 2 from 3.079 in the first iteration to 1.174E-5 in the tenth iteration. But these values in clusters 3 and 4 are 0.000 in all iterations. In the fifth iteration, we see a decrease in values from 1.499 in the first iteration to 7.618E-5 in the tenth iteration. In general, the value for cluster 1 in the fourth iteration, cluster 2 in the fifth iteration, cluster 5 in iteration 9, and clusters 3, 4, and 5 in all iterations has reached zero, meaning that this value for k\u0026thinsp;=\u0026thinsp;5 was fixed which determines the most significant location of the center.\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIteration History \u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIteration\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eChange in Cluster Centers\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.254E-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.646E-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.721E-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.373E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.360E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.698E-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.743E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.174E-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.618E-5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003ea. Iterations stopped because the maximum number of iterations was performed. Iterations failed to converge. The maximum absolute coordinate change for any center is 5.527E-5. The current iteration is 10. The minimum distance between initial centers is 9.435.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOne of the k-means clustering results is the cluster membership assignment to determine the most relevant cluster for each station. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e lists cluster membership and distance information for stations. Distances were calculated by Euclidean distance to measure their similarity, where the closer the distance, the more similar to the cluster center value. Distances are a suitable criterion to measure the similarity within a group and between cluster centers and stations. Since the grouping was accomplished based on these similarities, it is possible to assign cases among multiple groups. Therefore, five clusters were identified for stations. Among 33 metro stations, 26 stations are located in cluster 1 (78.78%), 3 stations in cluster 2 (9.09%), 1 station (Shariati) in cluster 3 (3.03%), 1 station (Melat Park) in cluster 4 (3.03%) and 1 station in cluster 5 (3.03%). Although clusters 2, 3, 4, and 5 have fewer representatives, their results can still be significant. Cluster 1 with the largest number of stations is the main case (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCluster Membership\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistance\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKashaf Rood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFajr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNabowat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShahid Mofateh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMashhad railway station\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShohada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSadi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlandasht\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.631\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKoohsangi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShahid Kaveh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFakouri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Terminal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInt. exposition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEghbal-e-Lahouri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSayyad-e-Shirazi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSadaf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHaft-e-Tir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDanesh Amooz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHashemieh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKowsar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAzadi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKhayyam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePalestin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaleghani\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQaem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShariati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmam Khomeini\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasij\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeventeeth Shahrivar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParvin Etesami\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGhadir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAirport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePark-e-Mellat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\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\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and the radar diagram (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) show clustering results by comparing the standardized values in axes that start from the same central point, indicating the difference between the clusters. Five clusters are ranked based on 6D variables.\u003c/p\u003e\n\u003cp\u003eAs can be seen, the stations located in cluster 1 have the highest values related to the recreational land use variables (0.06), the number and density of the population (0.18), and the number of bike stations (0.12). In this cluster, dominance is with some dimensions of destination, density, and demand management. About 70% of the stations along the two metro lines are located in this type. These stations are the place of residence and the focus of local daily activities. Therefore, this cluster can be called the \u0026ldquo;urban neighborhood.\u0026rdquo; On average, 5,290 people live in each of the cluster 1 stations. The average population density is 105 people per hectare. The station areas occupy an average of 421 square meters of recreational spaces which include mostly leisure and gathering retail stores such as coffee shops and restaurants. Even though these stations have the highest standard values for bike stations, they have an average of one bicycle station that is less in a radius of 400 meters compared to the population density.\u003c/p\u003e\n\u003cp\u003eIn cluster 2, variables of commercial land use, hotels, road networks, plots, intersections, and horizontal building density have the highest standard values compared to other clusters. They include some dimensions of destination, distance, design, and density. The three stations, Shohada, Saadi, and Basij, are in this group. They are named the \u0026ldquo;city business center.\u0026rdquo; The average commercial land use is 32,429 square meters, the average number of plots is 1,340, and the horizontal building density is 26 plots per hectare. The average road network length is 11.97 km, and the average intersection number is 102, which is higher than the average of all stations (approximately 62 intersections) and indicates a relatively good level of accessibility.\u003c/p\u003e\n\u003cp\u003eShariati station is the only station located in cluster 3. In this cluster, the highest standard values are for healthcare centers, bike lanes and stations, floors, metro passengers, public parking lots, and bus stations. They are in the subgroups of destination, distance, density, and demand management, respectively. More than 26% of the 400-meter area of the station is dedicated to all kinds of healthcare centers, including doctors\u0026apos; offices, clinics, pharmacies, and laboratories. Two hospitals with international performance scales are near this station. The average height density is about two floors. Shariati station is the intersection of two metro lines 1 and 2 and near a bus terminal. Therefore, this station is the busiest one in Mashhad and has the traffic of 43,119 metro passengers per day. In addition, this area is the focus of the two public parking lots with a capacity of 1,349 car parking spaces to manage part of the transportation demand. There is a 1,165-meter-long bicycle infrastructure along with various transportation options. But in terms of cycling equipment, there is only one bike station with a capacity of 20 bicycles. Due to the presence of two large healthcare centers, Shariati station can be named a \u0026ldquo;specialized healthcare activity center\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eThere is also the only Mellat Park station in cluster 4. In this cluster, the variables of green space, educational land use, entropy coefficient, marginal parking, and bike station have the most standardized values, 4.57, 4.55, 1.28, 1.28, and 0.12, respectively. These variables form a part of the destination, diversity, and demand management dimensions. The two main land uses, Mellat Park and the Ferdowsi University of Mashhad are in the area of this station (i.e. about 64% of the total area). Although a bus terminal exists in the vicinity of Park Mellat station and is the origin of 24 lines in the Mashhad metropolitan area, marginal parking lots play the most prominent role among the variables of demand management; the length of the marginal parking lots in this area is about 1970 meters; higher than the average of all stations. Considering the wide function scale of Mellat Park and the Ferdowsi University of Mashhad, this station can be named a \u0026ldquo;recreational-educational activity center.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eTwo stations, \u0026ldquo;West terminal\u0026rdquo; and \u0026ldquo;Airport\u0026rdquo; are in cluster 5. Although the standardized values in this cluster are low and negative, there are fluctuations among them compared to other clusters. The values of cluster 5 for hotel and recreational use variables are equal to clusters 3 and 4; -0.23 and \u0026minus;\u0026thinsp;0.22, respectively. Commercial land use variables with \u0026minus;\u0026thinsp;0.87 and healthcare centers with \u0026minus;\u0026thinsp;0.32 have the lowest values in clusters 5 and 4. Regarding the green space, the standardized values of cluster 5 are equal to cluster 3 (-0.40). The two stations have the lowest standardized values regarding distance, design (variable of parcels), density (variables of horizontal density and floors), diversity, and Demand management. These two stations are at both ends of Line 1. Although they are in the same cluster, they have different functional scales. The airport station transports national and international passengers to Mashhad, and the western station connects the tourist suburbs to Mashhad at the district level. However, based on the significant role of these stations in transferring passengers between different vehicles, they can be named a \u0026ldquo;transit center.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u0026nbsp;\u003c/strong\u003eFinal Cluster Centers\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/132203_cef980177e9a226b/132203_custom_files/img1708495677.png\" style=\"width: 650px; height: 586.927px;\" width=\"650\" height=\"586.927\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eANOVA was performed to analyze the effect of variables in formatting clusters. F ratios in Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eshow that the variables metro passengers (at 125.53) and healthcare land use (at 47.48) have the most significant effect on the clustering of stations, while the least effect is related to the recreational land use variable at 0.97.\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eANOVA\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e6Ds\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eDestination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommercial land use (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHotel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecreational land use (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational land use (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth center land use (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoad length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntersection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParcels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBike lane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBus station\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBike station\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDensity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHorizontal density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFloors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiversity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemand Management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarginal parking (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetro passengers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic Parking capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eIn this part of the research, we discuss the TOD typology results for 33 metro stations in two active lines of 38.9 km in the Mashhad metropolis, Iran. According to the 6D model in the form of 20 variables and the k-means cluster method, five potential TOD types were identified, including \u0026ldquo;urban neighborhoods,\u0026rdquo; \u0026ldquo;city commercial centers,\u0026rdquo; \u0026ldquo;specialized healthcare activity centers,\u0026rdquo; \u0026ldquo;recreational-educational activity centers,\u0026rdquo; and \u0026ldquo;transit centers.\u0026rdquo;\u003c/p\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, 78.78% of the stations are located in the \u0026ldquo;urban neighborhood\u0026rdquo; cluster spreading along two metro lines. In these stations, the variables of recreational land use, the number and density of the population, and the bike station have the most significant impact. The characteristics of the stations in this cluster are mainly consistent with the samples studied in America, South Korea, and China. They found that there is a positive relationship between cycling, population density, and public facilities in this type of TOD (Moudon, et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Zhao, Nielsen, Olafsson, Carstensen, \u0026amp; Meng, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Woo, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenerally, these stations can be classified into two subgroups in terms of development and demographic characteristics. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, 13 stations in this cluster are in the western half of one of the main corridors in the city that connects the west to the east, and four stations are in the southern part. All of them affected by the master plan are following the principles of modern urban planning. That means the diversity dimension is lower than the average of all stations because the most mixed land uses are along the main roads, and the dominance is with residential land uses. The station areas need to be improved in transportation demand management. Although all kinds of public and non-motorized transportation systems exist along with the metro system, private vehicles still dominate, and the significant road length is to private car traffic. So that during peak hours we see traffic congestion and parking of cars around these stations. Nine other stations in this cluster are located in the northeast and east of Mashhad (against the direction of development predicted in the master plan). The area of these stations is among the worn-out and unplanned textures considered a part of the urban service area over time. They experience the highest population density (up to 402 people per hectare), even higher than the maximum recommended standard (Transportation, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, this level of population density has not been in terms of conscious planning. The main reason is the low value of mainly agricultural land uses and the influx of national and international immigrants over the past decades to settle in these areas. Most stations lack all kinds of urban services, parks and open spaces, and healthcare services on a local and regional scale. They also face problems in design due to unplanned growth. The northeastern and eastern corridors are also among the main corridors of the city that pass through the historical core. For this reason, mass transit (metro and BRT) exists in this area at the urban and suburban levels. However, these areas are in worse conditions than the stations in the western part on transportation demand management. On the one hand, the parallel operation of various mass transits has overshadowed their efficiency. On the other hand, the weakness in parking management and non-motorized transportation infrastructure is quite evident.\u003c/p\u003e \u003cp\u003eThe three stations, Shohada, Saadi, and Basij, are classified as the \u0026ldquo;city commercial center.\u0026rdquo; According to Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the influencing variables in this type are commercial and residential land uses, parcels, horizontal building density, road network, and intersections. The stations are among the closest ones to the historical-religious core and are the center of attraction for tourists. There are many shopping centers, specialized lines selling healthcare and audio-visual goods, hotels, and historical places in their area. Basij station is the intersection of BRT and rail lines that provides access to the intercity passenger terminal and the airport. In general, this type of TOD is very similar to the regional TOD type in the Jakarta Metropolitan Region and the Urban commercial core type in Delhi; thematic mixed land uses and the predominance of the built environment are evident. Hence, they are highly accessible for job opportunities (Kumara, Sekhar, \u0026amp; Parida, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Taki \u0026amp; Maatouk, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite the positive characteristics of the development of TOD in this cluster, the boundaries of these stations need strengthening on non-motorized transportation infrastructure, especially bike lanes and stations, and building density.\u003c/p\u003e \u003cp\u003eThe third cluster, \u0026ldquo;specialized healthcare activity center\u0026rdquo;, includes only one station. For two reasons, the station is considered one of the crucial travel destinations and has more metro passengers than others. First, it is the intersection of two metro lines. Secondly, two hospitals and other healthcare centers are in the station area. Distance and demand management dimensions have better conditions in this cluster than others. So, we see multi-story public parking lots, the limitation of marginal parking time on the main roads, and other public and non-motorized transportation options. But what is evident during peak hours in the area is the high traffic congestion. Although the height density factor is significant, it is still far from the standards of 4 to 10 floors in a radius of 400 meters (Transportation, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe fourth cluster, the \u0026ldquo;recreational-educational activity center\u0026rdquo;, also includes only one station. This station is one of the destinations for leisure and higher education. On the one hand, a park with an urban function scale and an approximately 70-hectare area have turned this station into one of the recreational destinations for citizens and tourists in Mashhad. On the other hand, Ferdowsi University of Mashhad and Mashhad University of Medical Sciences, as Iran's first-level universities, are the destinations of about 33,300 domestic and international students and 1,711 faculty members in most of the year (nine months of the year). The diversity of land uses is also higher in this cluster compared to 47 neighborhoods in Delhi (in the range of 0.56\u0026ndash;0.75) and 24 Chinese cities (0.58) (Kumara, Sekhar, \u0026amp; Parida, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gu, He, Chen, Zegras, \u0026amp; Jiang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The main reason is the development of mixed commercial, office, and residential land uses in the last two decades.\u003c/p\u003e \u003cp\u003eWhat should be noted about clusters 3 and 4 is that some studies have generally identified one type of TOD as a \u0026ldquo;specialized activity center\u0026rdquo; (Department of Infrastructure and Planning, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, a category does not exist exclusively. Therefore, this study filled this gap by developing a typology for similar examples in other cities and countries.\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;Transit Center\u0026rdquo; cluster stations include West Terminal and Airport at both ends of Mashhad Metro Line 1. They have lower standard values than other clusters in all 6D dimensions. The findings of this part of the study are similar to the characteristics of the C2 type in Beijing, China, the Airport type in the Toronto area, Canada, and the terminal station type in Espadanal de Azambuja, Portugal, which are on the edge of the urban area, and all TOD characteristics are low (Lyu, Bertolini, \u0026amp; Pfeffer, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Higgins \u0026amp; Kanaroglou, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Galelo, Ribeiro, \u0026amp; Martinez, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although these two stations are in the same cluster, they are distinct in the function scale. The west terminal station is the first station of line 1 of the Mashhad metro at the end of Vakil Abad Boulevard, the intersection of Mashhad and two suburban cities with the function of tourism and leisure. This station is near the Vakil Abad bus terminal with the 14 Mashhad and suburb bus lines. The Airport station is the second busiest airport in the country. The annual capacity of the airport's national and international passengers is about 8\u0026nbsp;million people. Within the vicinity of this station, high-speed buses, city taxis, and subways allow passengers to access different parts of the city, which is a positive step in reducing the use of private vehicles and energy consumption. According to their functional nature, the station areas only provide urban services, including goods and passenger transportation.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe current study determined that based on the 6D model, metro station areas in Mashhad metropolis have the potential to develop five TOD clusters: cluster 1, \u0026ldquo;urban neighborhood;\u0026rdquo; cluster 2, \u0026ldquo;city business center;\u0026rdquo; cluster 3, \u0026ldquo;specialized healthcare activity center;\u0026rdquo; cluster 4, \u0026ldquo;recreational-educational activity center;\u0026rdquo; and cluster 5, \u0026ldquo;Transit center.\u0026rdquo; In some cases, this typology has similarities with the types identified in American, Indian, Chinese, Canadian, and South Korean cities. Based on the findings, the various dimensions need to be developed to become successful TODs. Therefore, we have provided suggestions for the TOD implementation based on the function of station areas, which can be used in other newcomer countries with similar contexts.\u003c/p\u003e\n\u003cp\u003eAdopting practical strategies to expand educational centers, medical services such as equipped para clinics, green space on a regional scale, and locating residential centers, especially in the station areas adjacent to clusters 3 and 4, will help in both increasing the number of destinations and adjustment in their distribution. Hence, this will reduce the travel distance and increase the level of access of residents and health tourists to the station areas.\u003c/p\u003e\n\u003cp\u003eTwo other dimensions are diversity and density. Considering the population density in the type of \u0026ldquo;urban neighborhood,\u0026rdquo; it is necessary to adopt incentive policies to increase the building density as much as possible to diversify non-residential activities. This is especially important in the station areas that include worn-out and underprivileged urban tissues and have a significant population by adopting the urban improvement and renovation approach based on TOD principles, granting low-interest loans, and joint construction projects as public-private partnerships. Implementing such policies will significantly respond to the daily needs of the residents in the station areas or adjacent to them. It helps to improve the design and increase the access distances. In the type of specialized healthcare activity centers, due to the relatively favorable conditions of building density, it is suggested to compensate for the diversity by applying incentive policies such as the right to transfer the development and expansion of complementary activities in the health and medical field. Applying such policies involves the type of recreational-educational activity center to increase building density in both residential and commercial sectors\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eOther measures suggested to implement successful TOD in station areas are improving various aspects of transportation demand management. These areas witness a range of marginal parking lots used in some places with a limited time during the day, especially in clusters 2, 3, and 4, and all the time in the other clusters. They encourage users to use cars and reduce the efficiency of the public transportation system (rail and bus). In this regard, it is suggested to reduce and eliminate these types of parking lots. Another practical policy for parking management is to create and increase the capacity of multi-story public parking lots at the 400-meter edge of stations in all clusters to minimize their functional interference with the public transportation system while serving car users. Developing the bicycle infrastructure and facilities is suggested in all clusters to promote non-motorized vehicles as much as possible in integration with public transportation.\u003c/p\u003e\n\u003cp\u003eStations in the \u0026ldquo;transit center\u0026rdquo; type need to be prompted in all dimensions of TOD. These stations act as entrance and exit gates of the city and must play their role well in the national and international arenas. So that everyone can get to know the capabilities and the valuable points by entering the station areas. Therefore, this requires the development of large and luxury shopping centers, leisure centers, and accommodation centers such as hotels in the form of optimal designs with acceptable access distances. In parallel with these changes, it is necessary to take measures in line with the integrated development of various sustainable transportation options (e.g., increase of bus stations, development of bicycle infrastructure and facilities, development of road network, replacement of multi-story parking lots instead of surface and marginal parking lots).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the results of this study, further studies can focus on strategic planning of various TOD types. It is also suggested that spatial analysis and optimal location of travel destinations within a radius of 400 to 800 meters of rail corridors would be the basis of future studies to improve accessibility. Eventually, considering the nonlinear effects of 6D variables on residents\u0026apos; travel behavior among TOD types can improve the study findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure and declaration statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are fully available and can be reviewed for verification by others.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the Mashhad Municipality for providing the GIS database to collect part of this article\u0026apos;s data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShirin Sabaghi Abkooh was responsible for conceptualization, methodology, data, formal analysis, interpretation, and original draft preparation. Mohammad Rahim Rahnama was responsible for methodology, data, formal analysis, interpretation, review, and editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdi, M.: What the newcomers to transit-oriented development are confronted with? Evidence from Iranian policy and planning. J. Transp. Geogr. \u003cb\u003e92\u003c/b\u003e, 103005 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/j.jtrangeo.2021.103005\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/j.jtrangeo.2021.103005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdi, M., Lam\u0026iacute;quiz-Daud\u0026eacute;n, P.: Transit-oriented development in developing countries: A qualitative meta-synthesis of its policy, planning and implementation challenges. Int. J. 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However, despite the precedence set by early adopters in the field of TOD, countries in the Middle East as newcomers lack comprehensive typological studies. Addressing this research gap, this paper endeavors to devise a TOD typology tailored to the geographical area encompassing a 400-meter radius around 33 active metro stations in Mashhad, Iran. Employing a systematic approach, the study constructs a spatial model integrating the 6D model (encompassing destination, distance, density, diversity, design, and demand management) alongside the k-means cluster analysis technique, thus contributing methodologically to the advancement of TOD typological methodologies. The findings delineate five discernible TOD archetypes, namely \u0026ldquo;urban neighborhoods,\u0026rdquo; \u0026ldquo;city commercial centers,\u0026rdquo; \u0026ldquo;specialized healthcare activity centers,\u0026rdquo; \u0026ldquo;recreational-educational activity centers,\u0026rdquo; and \u0026ldquo;transit centers.\u0026rdquo; Notably, the station areas categorized as \u0026ldquo;city commercial centers\u0026rdquo; exhibit the highest prevalence rate (78.78%). Nonetheless, the identification of the remaining four types bears significance, with the study notably introducing two novel typologies to the extant literature, namely the \u0026ldquo;specialized healthcare activity center\u0026rdquo; and the \u0026ldquo;recreational-educational activity center\u0026rdquo;, which hold applicability beyond the Iranian context. This research underscores the relevance of TOD typologies in informing urban development strategies and offers insights pertinent to transit-oriented planning endeavors.\u003c/p\u003e","manuscriptTitle":"Transit-Oriented Development Typology in Middle East's Metropolitan Context: Iran as a Case Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 17:53:38","doi":"10.21203/rs.3.rs-3968146/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":"2b13eca4-c86d-4f2c-b91f-c465c8da3e6b","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-27T04:15:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-21 17:53:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3968146","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3968146","identity":"rs-3968146","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}