Identification of neighborhood typology for potential transit-oriented development

Transportation Research Part D 78 (2020) 102186

Contents lists available at ScienceDirect

Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

Identification of neighborhood typology for potential transitoriented development

T

⁎

P. Phani Kumara, , Ch. Ravi Sekharb, Manoranjan Paridaa a b

Centre for Transportation Systems, Indian Institute of Technology Roorkee, Roorkee 247667, India Transportation Planning Division, CSIR – Central Road Research Institute, New Delhi 110025, India

A R T IC LE I N F O

ABS TRA CT

Keywords: Transit-oriented development Neighborhood typology Urban structure and travel behavior Trip distance Delhi TOD policy

While a good number of studies have defined neighborhood typologies based on transit-oriented development (TOD) factors, the literature offers limited clues for the identification of neighborhood types that are likely to be designated as TODs. The present study fills this gap through the use of a cluster-multilevel modeling approach for examining the underlying effects of urban structure on travel behavior among different neighborhood types. A two-step cluster analysis on spatial data from 47 neighborhoods of the Delhi metropolitan region has resulted in six neighborhood types. The study verifies the developed typology through the analysis of significant differences in terms of socio-demographic, and travel behavior variables, and found that the typology is valid. Besides, individual multilevel regression (MLR) models were developed using 5553 individual responses from all neighborhoods of typology. The MLR models were evident that the urban structure has an apparent effect and explains about one-third of the total variation in the distance traveled, assuming that other factors such as residential self-selection, land values, and environment may explain the remaining effects. This study has identified that the ‘transit’ type has shown consistent relationships between urban structure and vehicle kilometers traveled (VKT), thereby replicate TOD as a concept. The findings from the study are useful as a prescription tool for planners, policymakers, and government authorities to compare the performance of TOD characteristics within existing and planned neighborhoods. The modeling results are easy to be interpreted and are transferable to other Indian cities.

1. Introduction The majority of the urban land-use and transportation policies in the cities worldwide aim at achieving positive travel behavior such as the reduction in private transport (PrT) usage, vehicle kilometers traveled (VKT), traffic congestion and pollution, and enhancement in transit ridership, walking/cycling and quality of life. Transit-Oriented Development (TOD) is espoused as an urban strategy that combines multiple dimensions of urban structure to produce positive travel behavior (Kamruzzaman et al., 2016). However, the urban structure of neighborhoods is essential but not fully contribute to the success of TOD (Rodriguez et al., 2016). Some studies suggest that the land markets, travel demand management policies, availability of finance, parking provisions, and accessibility also make TODs successful (Cervero and Dai, 2014). All these powerful factors contribute to the effective integration of urban structure and travel behavior (Suzuki et al., 2013). Despite this, the analysis of market responses to TOD investments receives less attention and constrained to rigorous data. Because it often takes time for such factors to unfold and reveal themselves, the

⁎

Corresponding author at: Centre for Transportation Systems, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India. E-mail address: [email protected] (P. Phani Kumar).

https://doi.org/10.1016/j.trd.2019.11.015

1361-9209/ © 2019 Elsevier Ltd. All rights reserved.

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

present study majorly focuses on the urban structure dimensions and their relationships with travel behavior to represent the TOD concept. The analysis of urban structure-travel behavior relationships differentiates at distinct scales such as city or metropolitan, districts, corridors, and neighborhoods. Studies so far addressing the relationships in the TOD backdrop, were conducted at aggregate (city or metropolitan) level (Chen et al., 2017). In reality, the neighborhoods in a city possess spatial diversity by involving more than one type of urban structure combinations (Kamruzzaman et al., 2014). Recent studies addressed this spatial diversity among neighborhoods through a measurement tool, called TOD typology (Vale, 2015; Kumar et al., 2018). Neighborhoods with a similar set of urban structure elements can be grouped to form a specific TOD type (Huang et al., 2018). Every neighborhood type will have a definite density, transit system, physical elements, land use, street patterns, etc., that support the design of an ideal TOD neighborhood (Zemp et al., 2011). Besides, the urban structure of one neighborhood type may show consistent effects on travel behavior, and another type may not. Therefore, planners, policymakers, and government bodies need to identify the neighborhood type that has the true potential to become TODs based on existing relationships. Also, urban structure-travel behavior relationships rely on individuals living in a neighborhood. In such cases, the spatial dependency problem may occur among observations between neighborhoods within a neighborhood type (Hong et al., 2014; Cao et al., 2009). Therefore, the individual and neighborhood level effects on travel behavior could fully capture the nature of TOD within each neighborhood type. Limited studies have developed typologies in a quantitative manner (Kamruzzaman et al., 2014), yet no such studies are available in the Indian context. Also, researchers made limited efforts on examining the urban structure-travel behavior relationships across typology (Higgins and Kanaroglou, 2016). In light of the gaps found in the literature, the present study contributes to the existing literature on neighborhood typology and their urban structure-travel behavior relationships in the TOD perspective. The traditional typology development was extended to incorporate the underlying effects of urban structure on travel behavior that answer two major questions at the policy level. How can the typology be developed based on the existing urban structure? Which type of neighborhood has the true potential to become TODs? A cluster-multilevel modeling approach was employed to analyze data, which involves spatial data and 5553 individual responses from 47 selected neighborhoods of Delhi, India. We believe that such a typology in advance to TOD planning is essential for planners and policymakers that act as a measurement tool in analyzing the present and future growth of TODs. Secondly, examining the underlying effects of urban structure on travel behavior within each neighborhood type would identify the neighborhood type that replicates TOD as a concept. Such an analysis also exhibits the degree of TOD elements within and supports the planning of TOD for each neighborhood type. The organization of the study is as follows. The preceding section offers a brief background on the TOD and typology, existing typology approaches, and the linkage between urban structure and travel behavior. Section 3 presents the study area, data description, and methodology of the study. Section 4 demonstrates the development of neighborhood typology and its validation. Section 5 exhibits the underlying effects of urban structure on travel behavior within each neighborhood type. Section 6 suggests the policy parameters for TOD typology design and Section 7 concludes with the inferences of the study useful for policy implications. 2. Research background 2.1. Transit-oriented development (TOD) and typology Transit-oriented development (TOD) is often considered as a policy concept across the world for mitigating urban problems such as long commute distances and times, traffic congestion and air pollution (Bagley and Mokhtarian, 2002; Ewing and Cervero, 2010; Thomas et al., 2018). A TOD advocates high-density developments that support the viability of transit service and increase transit ridership (Chatman, 2003; Sung and Oh, 2011), diverse land uses that reduce the distance traveled and thereby improve air quality (Schlossberg and Brown, 2004; Cervero and Duncan, 2006), pedestrian-friendly streets which encourage walking/cycling and enhance public health (Zegras, 2010; Lin and Gau, 2006; Kamruzzaman et al., 2016), and proximity to highly accessible transit systems that reduce PrT usage (Schwanen and Mokhtarian, 2004; Cao et al., 2009). All the above consistent effects of urban structure on travel behavior could justify the true potential of any neighborhood to become a TOD (Renne, 2009; Singh et al., 2017). The characteristics of TOD seem comparable, but literature has shown huge diversity in its application for different contexts (Thomas et al., 2018; Laaly et al., 2017). For example, TODs in US cities focused mainly on centralizing density and sprawl around transit stops (Cervero, 2002; Nasri and Zhang, 2012; Ewing et al., 2017). In European cities, TOD seems to be a redevelopment of existing neighborhoods towards active transport (AT) (Knowles, 2012; Singh et al., 2017; Voulgaris et al., 2016). TODs in Asian cities is considered as a land value capture (LVC) for distributing metropolitan growth along transit corridors (Cervero and Murakami, 2009; Kong and Pojani, 2017; Kumar et al., 2018). Indian cities like Delhi are now planning for TODs as a sustainable strategy to free its urban areas from traffic congestion and pollution. Hence, it is typically understood that for planning and implementation of TOD, policies need to be context-sensitive (Thomas et al., 2018). To unfold this spatial diversity in TOD application and to capture the sensitivity in context, recent studies have identified typologies based on the existing urban structure characteristics (Higgins and Kanaroglou, 2016; Kumar et al., 2018). A TOD typology is the segmentation of neighborhoods that have similar urban structure. Such a typology helps to answer certain questions at the policy level, such as ‘which kind of transit system? What levels of density? What mixture of land uses? Which pattern of streets? Which type of neighborhood?’ that is necessary to augment the TOD success (Austin et al., 2010). Answers to these questions are very much essential to planners and policymakers while developing TOD policies (Kamruzzaman et al., 2014). Besides, a classification provides a careful benchmarking of urban structure elements which acts as a supplement for the TOD development. It enables government agencies and builders while investing funds on different neighborhood types to achieve comprehensive TOD benefits in 2

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

long term planning. Knowing the extensive benefits of developing a typology for the TOD planning and implementation, policymakers and government bodies worldwide have associated typologies in the policy documents of their jurisdictions (Austin et al., 2010; FDOT, 2012; WRI, 2018). 2.2. Existing TOD typology approaches Recent literature demonstrated two approaches for conceptualizing and developing TOD typologies (Higgins and Kanaroglou, 2016). The first one is the qualitative approach in which typologies were labeled based on the geographical and functional characteristics of neighborhoods (Calthorpe, 1993; Austin et al., 2010; FDOT, 2012). For example, Calthorpe (1993) have mentioned two types of TODs, namely urban and neighborhood TODs. Urban TODs are high-density areas within a half-mile distance from trunk-line transit. Neighborhood TODs possess lower to medium density residential areas within ten minutes of travel to local or feeder bus lines. Dittmar and Poticha (2004) demonstrated six TOD types namely suburban center, suburban, urban downtown, urban, neighborhood transit zone, and commuter town center, based on descriptive performance benchmarks between places and destinations. Florida department of transportation (FDOT) included a TOD typology in their TOD guidebook to provide general parameters and strategies for local government and transit agencies (FDOT, 2012). The Florida guidebook defines station typology in three different scales, namely regional, community, and neighborhood as a primary distinction. Each scale type includes specific levels of density, mixed-use, urban form, street network, and parking for different types of transit systems. In India, the Ministry of Urban Development (MoUD) has included a typology in their TOD guidance document for preparing TOD plans in high-density Indian cities (MoUD, 2016). Table 1 illustrates the typology which was defined based on station characteristics, availability of land, land use mix, and challenges for TOD implementation. The typology mentioned in the MoUD (2016) TOD guide considers developments (formal and informal) in a subjective manner. To serve as a practical handbook, the MoUD typology need to define specific levels of urban structure in a quantitative manner. The second approach is concerned with the quantitative segmentation of neighborhoods based on the existing TOD characteristics (Vale, 2015; Voulgaris et al., 2016). Researchers recognized that TOD could take different forms, and each neighborhood can serve different from a different urban structure (Atkinson-Palombo and Kuby, 2011). It was Bertolini (1999) who first introduced the ‘Node-Place’ model for segmenting neighborhoods into TOD typology. The Node – Place model is an integration of transportation (node) and urban structure (place) indices on the x-axis and y-axis, respectively. Fig. 1 demonstrates the model which specifies five types of TOD conditions, namely (i) balanced or accessibility, (ii) stress, (iii) dependency, (iv) unsustained places, and (v) unsustained nodes. The position of a neighborhood can be identified by the calculation of 16 node and place indicators as listed in Bertolini (1999). Although the Node-place model is a two-decade-old model, it serves as a basic analytical tool for developing typology. Some researchers enhanced this approach based on their purpose, contextual aspects, and data availability (Zemp et al., 2011; Vale, 2015; Lyu et al., 2016), and the model still has the following limitations:

• The segmentation of neighborhoods is limited to five clusters only. Also, the clusters need external validation before any conclusion (Vale, 2015). • The assumption of ‘Balance (Accessibility)’ in the long term is incoherent (Bertolini, 1999). This assumption is consistent only • • •

with balanced nodes, where ‘unbalanced’ (inconsistent travel behavior-urban structure relationships) situations are difficult to identify. The Node – Place indicators of Bertolini (1999) shall not be expected to represent multiple actors of TOD (Zemp et al., 2011). Many studies aimed at enhancing the set of indicators (Reusser et al., 2008; Vale, 2015), and yet it is a point of departure (Lyu et al., 2016). The correlation between node and place indicators will locate the majority of neighborhoods near the centerline (Vale, 2015). Alternatively, a concave curve would best represent a balanced situation rather than a straight line (Reusser et al., 2008). However, again, it is difficult to identify an unbalance situation. A ‘Balance’ node-place does not allow to evaluate whether it is a TOD or simply a Transit Adjacent Development (TAD), an evil brother of TOD (Vale, 2015).

Further studies have developed TOD typologies in a promising way using empirical methods (Austin et al., 2010). Researchers have employed different methods such as hierarchical (Song and Knaap, 2007), two-step (Kamruzzaman et al., 2014, 2016; Kumar et al., 2018), and latent class (Huang et al., 2018) clustering of TOD factors to arrive at typologies. Kamruzzaman et al. (2014) suggested that the classifications of likely neighborhoods should accord the existing urban structure. Higgins and Kanaroglou (2016) classified 372 rapid transit station areas of Toronto region into 10 TOD types based on latent class clustering of TOD factors such as population density, employment density, entropy, street connectivity, residential (%), commercial (%), mixed (%), and industrial (%) uses. This study validated the typology by examining statistical differences among socio-demographic and travel behavior variables. Huang et al. (2018) segmented 22 transit station areas of Arnhem-Nijmegen city region, Netherlands into 3 TOD types, namely urban mixed core, urban residential and suburban residential based on latent class clustering of factors such as population density, job density, business density, entropy, mixed-ness, intersection density, and bicycle and pedestrian network density. Atkinson-Palombo and Kuby (2011) have proposed five TOD types for 27 station areas of the upcoming light rail transit (LRT) system in Phoenix, the USA using a hierarchical clustering method. To validate the typology, this study has conducted three independent ANOVA tests on mean values of the value of advance TOD ($/feet), overlay zoning (%), and advance TOD subject to 3

4

Transport hubs with commercial and informal activities

Employment and community activities Unique destinations Residential districts with good accessibility to transit Economic, community and cultural activities Residential districts outside the city core with no proper accessibility to transit Residential districts outside the city core with good accessibility to transit

Intermodal Gateways

Employment Centers Destination Nodes Transit Neighborhoods Urban Core Infill Neighborhoods

New Residential Areas

Station Area Characteristics

Station Area Typologies

Table 1 TOD typology for Indian Cities (MoUD, 2016).

Integration of housing and employment uses Introducing housing into employment uses Accommodation of peak travel demand Affordable housing Accessibility to transit Accessibility to offers Expansion of retail opportunities

Moderate to high residential density and mixed uses

Planning and Development Challenges

Moderate to high residential density and mixeduse Moderate to high employment density Moderate to low residential density High residential density High density and mix-use Moderate to high residential density

Land Use Mix and Density

Moderate

Less Less Less Retrofitting and Infill Very less

Moderate

Land Availability

P. Phani Kumar, et al.

Transportation Research Part D 78 (2020) 102186

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

Fig. 1. Application of Node-Place Model to Amsterdam and Utrecht Agglomerations (Bertolini, 1999).

overlay zoning (%). Kamruzzaman et al. (2014) segmented 200 census collection districts (CCDs) of Brisbane into 4 TOD types, namely activity center TODs, potential TODs, residential TODs, and non-TODs using two-step cluster analysis. They validated the clusters using two multinomial logit (MNL) models on travel behavior data of 10,013 individuals living in the Brisbane area. Some important observations were emergent from the existing typology approaches. The subjective typologies are not timely and failed to quantify the urban structure and its spatial diversity. This subjective generalization of TOD conditions relies on little scientific support and may not be useful as a precise tool for TOD development (Kamruzzaman et al., 2014; Singh et al., 2017). Hence, planners and researchers mostly prefer quantitative typologies since they are testable, comparative, systematic, and supportive for TOD planning (Cervero and Murakami, 2009; Singh et al., 2017). Besides, policymakers paid very little attention to other subjective issues, such as land values, governance, finance, and demand management policies, that have a long-term influence on the TOD typologies. However, the influence of land markets on TOD typology, demand longitudinal data, and rigorous analysis (Suzuki et al., 2013). Secondly, existing studies have evidence that TOD typology is also not a “one-size-fits-all” approach (Kamruzzaman et al., 2014). In other words, the typology developed for one context may not apply to another context. However, the typologies available for different contexts would act as reference tools while framing a typology for any other context. Thirdly, the developed typology needs to be verified or validated using travel behavior indicators such as transit ridership, trip costs, trip time, vehicle kilometers traveled (VKT), vehicle ownership, and mode share (Austin et al., 2010).

2.3. Urban structure and travel behavior Although many policies and studies have mentioned TOD typologies, researchers made limited efforts on the causal effects of TOD inputs (urban structure) on TOD outputs (travel behavior) within neighborhood types (Higgins and Kanaroglou, 2016). Studies on this relationship are important because TOD planning in any city rarely starts at greenfield space (Thomas and Bertolini, 2015; Chen et al., 2017). Every neighborhood in a city possesses some degree of TOD-ness within (Laaly et al., 2017). In reality, the transit facilities across a city follow the urban structure in neighborhoods where there is enough travel demand, which leads to a transit adjacent development (TAD). The urban structure is further (re)oriented towards transit facilities, which creates a TOD. An unsuccessful (re)orientation remains the neighborhoods as TADs (Renne, 2009; Singh et al., 2017). Hence, TOD is not just an urban structure adjacent to a transit facility, but there is, importantly, a linkage between them. From the past three decades, studies on the linkage between urban structure and travel behavior have gained significant attention from numerous researchers (Ogra and Ndebele, 2014; Ewing et al., 2017). Ewing and Cervero (2010) have reported that 5 ‘D’ variables of urban structure namely ‘density, diversity, design, distance, and destination accessibility’ play a major role in achieving positive travel behavior. However, previous studies have identified two major methodological issues while investigating urban structure-travel behavior relationships among neighborhoods (Hong et al., 2014). Firstly, the contribution of residential self-selection in urban structure-travel behavior relationships is a major concern (Schwanen and Mokhtarian, 2004; Naess, 2012). Over two decades, the residential self-selection phenomenon has gained significant interest from numerous researchers. Some studies mention that the urban structure effects on travel behavior are underestimated due to this self-selection problem (Zhang et al., 2012). Recent studies also indicated that the urban structure has a dominant effect on travel behavior as compared to residential self-selection (Chen et al., 2017), and revealed that the effects of the urban structure itself matter (Handy et al., 2005; Mokhtarian and Cao, 2008). Brownstone and Golob (2009) have found that employing socio-demographic variables in the analysis itself can resolve the selfselection problem. Secondly, the measurement of urban structure variables at the neighborhood level would lead to a common phenomenon, called spatial dependency (Hong et al., 2014). For instance, Ding and Cao (2019) used spatial data of residential and workplace locations at the TAZ level to examine their effects on car ownership. Individuals in the same neighborhood type are likely to be similar in terms of their socio-demographic characteristics. They may also show similar characteristics between neighborhoods within the same 5

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

neighborhood type. Such observations that are analyzed in ordinary linear regression (OLR) models would underestimate the standard error of a coefficient or over-state the significance of the test (Kim and Wang, 2015). Recent studies have addressed this spatial dependency problem among observations through the application of multilevel regression models while investigating the urban structure-travel behavior relationships (Wu and Hong, 2017; Ding and Cao, 2019). However, limited studies have employed MLR models in examining the underlying effects of urban structure on the travel behavior of individuals living in different TOD types.

3. Research design 3.1. Study area Delhi stands second most populated metropolitan cities in the world, with a population of 26.7 million over 1484 sq.km of area. This city carries a well-established metro rail transit (MRT) system, which is currently operating with 343.36 km of network length that stretches across eight corridors and connects 250 station areas (DMRC, 2018). One of the long term strategic goals of the Master Plan for Delhi - 2021 (MPD-2021) is to build Delhi as a state that provides comfortable, affordable, and efficient mobility options to its citizens. To achieve the stated goals, the Delhi government has drafted a TOD policy guide in association with the MRT system (UTTIPEC, 2013; MoUD, 2016). The policy document implements TOD within 2000 m on both sides of the MRT corridors. More specifically, the TOD influence zone demarcation varies in three levels: (i) Intense Zone (300 m), (ii) Standard Zone (800 m), and (iii) Transition Zone (2000 m) from the centerline of corridors to the periphery. Given the historically high dense, mixed-use, and diverse income groups in its urban areas, the neighborhoods of Delhi stand as ideal locations for TOD development in the future years. Given this, the Delhi government has initiated many TOD projects at the neighborhood level (MoUD, 2016). However, the neighborhoods of Delhi carry significant complexity in its urban structure resulting in spatial diversity in the application of TOD. The present study addresses this spatial diversity problem by developing an advance TOD typology of likely neighborhoods. For this purpose, the present study has selected 47 neighborhoods based on visual inspection of existing features such as distance to transit, land use, and road patterns. Besides, the population density served as a threshold parameter in finalizing the neighborhoods, considering a threshold population density of 15,000 persons/sq.km for urban TODs (Dittmar and Poticha, 2004). Fig. 2 shows the selected neighborhoods which spatially cover the Delhi city in all directions.

Fig. 2. Spatial Distribution of Selected Neighborhoods in Delhi. 6

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

3.2. Data description The present study used two types of datasets: (a) spatial data of neighborhoods to represent 5 Ds of urban structure; (b) household survey data including socio-demographics and travel behavior of individuals living in selected neighborhoods. The spatial datasets were used to develop the quantitative typology for the neighborhoods of Delhi. The survey data was used to examine the underlying effects of urban structure on travel behavior among derived typology. 3.2.1. Spatial data The spatial data consists of eight TOD factors covering 5 Ds of urban structure namely population density, employment density, entropy, intersection density, network density, distance to transit, job access by PrT and job access by PuT. The following data sources were used to derive the TOD factors:

• Physical road network, bus and MRT network, and intersection points have been developed in ArcGIS using the google maps data of the year 2018 (https://www.google.co.in/maps); • Census block parcels as ‘neighborhoods’ with specified land use segmentation as given in the MPD – 2021 (WRI, 2018) Report (http://52.172.182.107/BPAMSClient/seConfigFiles/Downloads/MPD2021.pdf?t=0.8692890864331275); • Population and Employment data downloaded from the Census of India (2011) web source (http://censusindia.gov.in/) and •

extrapolated to the base year 2018. The geometric increase method was used for extrapolating the population and employment data of neighborhoods based on the available census data. The average population and employment growth are assumed to be constant over a decade; and Public transport data (bus and metro) downloaded from the Delhi Transport Corporation (DTC, 2015) and Delhi Metro Rail Corporation (DMRC, 2017) web sources (http://www.delhimetrorail.com/) (http://delhi.gov.in/wps/wcm/connect/DOIT_DTC/ dtc/home);

Unlike previous studies that focused on neighborhoods around transit stations (Lyu et al., 2016), the present study used census community blocks as the conducive neighborhoods for the analysis. In the absence of a universal boundary (Huang et al., 2018), the present study has considered a buffer boundary of 1200 m for calculating TOD factors in the Delhi context (Kumar et al., 2018). Fig. 3 demonstrates the calculation of TOD factors within each neighborhood buffer area using ArcGIS software. Table 2 presents the detailed statistics and descriptions of eight TOD factors. Table 3 explores the correlations among the calculated TOD factors. All the correlations appear to be in expected directions except with the ‘entropy’ indicator. However, the ‘entropy’ was retained since it is considered to be a significant factor for representing TOD-ness in a neighborhood (Singh et al., 2017). Due to the high correlations of

Fig. 3. TOD Factors within each Neighborhood Buffer Area. 7

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

Table 2 TOD Factors and their descriptions. TOD Factors

Description

Mean

St. Dev.

Population Density (Persons/ sq.km) Employment Density (Jobs/ sq.km) Entropy (0–1)

The number of persons residing within a unit neighborhood buffer area

24688.31

10656.10

The number of jobs available within a unit neighborhood buffer area

9203.35

6307.78

A combination of 9 different land-use types such as public and semi-public, residential, commercial, industrial, transportation, utility, recreational, government, and vacant land present within the neighborhood buffer. The entropy of a neighborhood can be estimated using the formula,

0.68

0.09

19.94 77.11

10.54 36.39

1246.57 16326.74

757.49 7382.01

15790.98

7176.28

n

entropy = ∑ j = 1 ∑i

Network Density (km/sq.km) Intersection density (Nos/ sq.km) Distance to Transit (m) Job Access by PrT (No of Jobs/ 30 min)

Job Access by PuT (No of Jobs/ 45 min)

(pij ln(pij )) ln (j )

, where pij is the fraction of jth land-use type in the ith population ward

within neighborhood buffer. The entropy values lie between 0 and 1 with a value nearer to 0 indicates single-use and of nearer to 1 indicates complete mix-use The sum of major and minor road lengths existing within a unit neighborhood buffer area The number of intersections (3 or 4-way) presents within a unit neighborhood buffer Euclidean distance from the center of a neighborhood buffer to the closest MRT station Travel time matrices were derived from the road network routes using the shortest travel time. The calculated travel time from a neighborhood indicates the time taken by private modes such as car and two-wheeler to reach any job location. Job access by PrT was calculated as the number of jobs reached within 30 min of travel from each neighborhood center Travel time matrices were derived from the public transit network routes using the shortest travel time. The calculated travel time from a neighborhood to any job location includes the time for access, in-vehicle, and egress trips, assuming that the access and egress modes as walking. The job access by PuT was calculated as the number of jobs reached within 45 min of travel from each neighborhood center

Table 3 Correlation Matrix between TOD Factors. TOD Factors

Distance to Transit

Network Density

Employment Density

Population Density

Entropy

Intersection density

Job Access by PrT

Job Access by PuT

Distance to Transit Network Density Employment Density Population Density Entropy Intersection density Job Access by PrT Job Access by PuT

1 −0.005 −0.348

−0.005 1 0.133

−0.348 0.133 1

−0.254 0.202 0.792

0.218 0.011 −0.1

−0.320 0.201 0.078

−0.064 0.035 0.239

−0.312 0.043 0.166

−0.254 0.218 −0.320 −0.064 −0.312

0.202 0.011 0.201 0.035 0.043

0.792 −0.100 0.078 0.239 0.166

1 −0.416 0.467 0.038 0.103

−0.416 1 −0.132 −0.103 0.125

0.467 −0.132 1 0.083 0.147

0.038 −0.103 0.083 1 0.994

0.103 0.125 0.147 0.994 1

The bold values indicate high correlation between TOD factors.

population density with employment density, and job access by PrT with job access by PuT, the present study has retained six TOD factors, eliminating ‘employment density’ and ‘job accessibility by PrT’ factors in the further analysis. 3.2.2. Travel survey data The developed typology was verified using the household travel survey data collected from August 2016 - April 2017 from the selected neighborhoods (CSIR-CRRI, 2017). The present study considered a cleared sample set of 5553 individual responses for the analysis. The survey data includes a rich set of socio-demographic variables mainly age (years), gender (1 = male, 0 = female), household size (Nos), household income (INR), workers in a household (Nos), vehicle ownership (Nos), driving license (1 = yes, 0 = no), and travel behavior variables such as trip purpose (business, education, work, and recreation), trip distance (km), trip time (mins), and a choice of bus, car, two-wheeler, metro, intermediate public transport (IPT), cycle and walk modes. The distances and time elapsed were calculated using the ‘recommended optimal path’ on the road network from google maps based on origin and a destination as well as mode reported for an individual trip. ArcGIS was used to analyze the geocoded information of each respondents’ origin and destination. Fig. 4 represents the trip distances covered by individuals using different modes of transport. The survey data has considered only single-purpose trips (e.g., work to home) to analyze the travel behavior of individuals. 3.3. Methodology The present study has two major objectives. Firstly, to develop a neighborhood typology using cluster analysis, and secondly, to examine the conceptual relationships between urban structure and trip distance among typology using multilevel regression models. This study uses SPSS software to conduct all statistical analyses. 8

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

Fig. 4. Trip Distance by Available Modes.

3.3.1. Two-step cluster analysis Cluster analysis facilitates to identify the natural segments of neighborhoods having common TOD profiles (Lyu et al., 2016). Among many clustering techniques, the present study employed the two-step clustering method due to two reasons. Firstly, it is a powerful clustering tool that often preferred in recent literature; secondly, it is more efficient for continuous input variables (Bacher et al., 2004; Kamruzzaman et al., 2014). Similar to the K-mean or ward method of clustering, this method estimates clusters in two stages, and also offers optimum clusters based on statistical inference. Therefore, this study conducted a two-step cluster analysis of six TOD factors namely population density, entropy, distance to transit, network density, intersection density, and job accessibility by PuT as the determinants of likely TOD profiles for the neighborhoods of Delhi. In the two-step clustering method, the similarity between any two clusters can be computed using log-likelihood measurement. All the input (continuous) variables are assumed to be normal and independent. The main advantage of this clustering method is that it estimates the optimum clusters based on the Akaike information criterion (AIC) and/or Bayesian information criterion (BIC), automatically. We can validate the cluster solution in three ways (Norusis, 2008). Firstly, the predictor importance of all input variables must be closer to 1, which implies that all input variables have equal contribution in forming the clusters. Secondly, the cluster quality can be evaluated using the Silhouette Coefficient, which is a measure of cohesion and separation of clusters. The Silhouette value must be greater than 0, suggesting a fair quality of the cluster model. Thirdly, Pearson’s chi-squared or independent sample t-tests have to be conducted on individual cluster data to confirm that the clusters are significantly different among the segmentation variables.

3.3.2. Multilevel regression modeling Recent literature has widely applied multilevel regression (MLR) models to spatial analysis in the urban structure and travel behavior background (Hong et al., 2014; Ding and Cao, 2019). Compared to OLR, MLR models have many advantages (Bhat, 2000; Ding et al., 2014). Firstly, it can overcome the spatial dependency problem among observations; secondly, it can handle multicollinearity issues among independent variables by estimating both individual and neighborhood variables at different levels. The present study employed multilevel models for two major reasons. Firstly, to account for the spatial dependency problem among neighborhood data within each neighborhood type. Secondly, to investigate the underlying effects of urban structure on trip distance as a test to identify the true potential in neighborhood types to become TODs. The specification of the multilevel model can be represented in Eqs. (1) and (2) as T yi N (αj [i] + βIL XiIL , σy2), for i = 1, 2, ⋯.,n

(1)

T αj N (γ + γNL XjNL , σα2), for j = 1, 2, ⋯, J

(2)

and

where yi represents the trip distance traveled by an individual. XiIL and XjNL refer to various individual-level (socio-demographic) and neighborhood-level (urban structure) variables, respectively. Varying intercept, αj is assumed to be normally distributed and inT dependent with the expected value γ + γNL XjNL . σy2 and σα2 represent the variations within trip distance and between neighborhoods, respectively. The intra-class correlation (ICC) explains the spatial dependency among individuals within a neighborhood, which can be determined using Eq. (3) as 9

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

Fig. 5. Overall quality of clustering based on Silhouette Measure.

ICC =

σα2

σα2 + σy2

(3)

ICC is the ratio of neighborhood-level variance to the overall error variance of the model. If the ICC is close to 0, the individuals within neighborhoods are independent, and an MLR reduces to an OLR model. For multilevel models to be appropriate, a rule of thumb is that ICC must be larger than 0.10 (Ding and Cao, 2019; Hox et al., 2017).

4. Neighborhood typology 4.1. Development of neighborhood typology The Two-step cluster analysis has segmented the 47 neighborhoods of Delhi into six clusters namely low-density, urban residential, affluent, urban commercial core, urban mixed core, and transit neighborhoods. Fig. 5 depicts that the cluster solution has obtained a Silhouette value nearer to 0.5, suggesting a ‘fair’ cluster quality. The predictor performance of all input variables was found closer to 1, implying an equal contribution of all variables in predicting the clusters. The neighborhood types were labeled based on their relativeness to the MoUD (2016) TOD guide and through literature support. Fig. 6 illustrates the spatial distribution of typology for the development of TOD in Delhi. Table 4 represents the levels of TOD factors for the developed typology. Based on the differences in urban structure, we can interpret the six neighborhood types as:

Fig. 6. Typology for the TOD development in Delhi. 10

11 Dilshad Garden, Gagan Vihar, Kotla, Malviya Nagar, Nand Nagri, Pushp Vihar

Chanakyapuri, Jamia Nagar, Madanpur Khadar, Mehrauli, Noor Nagar, Okhla, R K Puram, Tughlakabad, Vasant Kunj

6

Badarpur, Dwarka, East of Kailash, Govindpuri, Kailash Colony, Lajpat Nagar, Mundka, Peeragarhi, Rohini, Swaran Park Andrews Ganj, Gautam Nagar, Green Park, Gulmohar Park, Hauz Khas, Jungpura, Moti Bagh, Netaji Nagar, Sarojini Nagar, Shanti Nikethan, South Moti Bagh 11 Janakpuri, Nangloi, Nangloi Ext., Paharganj, Rajdhani Park, Uttam Nagar

Chittaranjan Park, Greater Kailash, Pitampura, Preet Vihar, South Extension II

7163–9120 (−52)

7193–10825 (−50)

9

24524–28901 (67)

11716–14562 (−24)

20249–24926 (39)

12750–14206 (−17)

835–1527 (−6)

5

630–1410 (−1)

6

477–982 (−50)

5–12 (−65)

390–787 (−52)

785–1660 (−20)

10

8–29 (1)

20–22 (10)

0.63–0.76 (1) 35–85 (−1)

2350–2550 (100)

28–45 (110)

0.72–0.78 (10) 61–89 (−5)

14–29 (−10)

0.64–0.68 (−3) 114–142 (68)

16559–25791 (−17)

5–17 (−65)

0.58–0.68 (−1) 114–127 (55)

16939–23075 (−22)

0.48–0.60 (−16) 48–96 (−7)

36104–46884 (68)

Kailash Colony

0.69–0.73 (6) 27–54 (−47)

24689–33936 (8)

Hauz Khas

26349–45003 (70)

Uttam Nagar

11665–14941 (−46)

Preet Vihar

Moderate to high-density neighborhoods with good access to regional and subregional centers

Significant centers of economic, community and cultural importance with regional-scale retail destinations

High-density neighborhoods of commercial activity that serve as main public/semipublic amenities of the city

Upscale residential neighborhoods dominated with car-oriented streets

Predominantly large housing neighborhoods just outside the city core area

Dilshad Garden

Transit

Urban Mixed Core

Urban Commercial Core

Affluent

Urban Residential

*the values of indicators are in the form a − b(x), where a is the 25th quartile value, b is the 75th quartile value, and x in the parenthesis is the percentage difference from the overall mean value of the indicator.

Number of Neighborhoods

Population Density (Persons/sq.km) Entropy (0–1) Intersection Density (Nos/sq.km) Network Density (km/ sq.km) Distance to Transit (m) Job Access to Public Transport (within 45 min) Neighborhoods

R K Puram

Low-density residential neighborhoods of dedicated communities like administrative/religious/ industrial/political

Description

TOD Profiles

Low-Density

TOD Typology

Table 4 Quantitative Levels of TOD Factors for Neighborhood Typology in Delhi.

P. Phani Kumar, et al.

Transportation Research Part D 78 (2020) 102186

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

4.1.1. Low-density The ancient neighborhoods of Delhi were labeled as low-density clusters due to their dispersed density patterns as shown in Table 4. These neighborhoods lie outside the city core (as shown in Fig. 6) and are home to many architectural monuments such as Redfort, Qutab Minar, Jain temple, Muslim ‘Ghetto’, and diplomatic avenues to museums, embassies, official residences for politicians and civil servants of India, parks, and open spaces. Some neighborhoods like Okhla, Tughlakabad, Madanpur Khadar, etc., possess an unplanned and self-organized urban structure with the congested and narrow road network. On the other hand, neighborhoods like Chanakyapuri and Rk Puram are exceptional for having a well-planned street network lying inside the city core but are typically low-density and regulated developments. Neighborhoods in this cluster type are more than 2 km away from MRT, resulting in very low access to employment opportunities. Interestingly, the low-density neighborhoods have shown an entropy value of 0.723, implicating that the ancient neighborhoods of Delhi are traditionally mixed in nature (Jain et al., 2014). 4.1.2. Urban residential Urban residential neighborhoods as illustrated in Fig. 6, are large housing colonies lying just outside the periphery of the city core and are within the TOD transition zone (2000 m) from the MRT system. These neighborhoods are characterized by lower job accessibility (52% smaller than the mean) and a lower mix of land uses (0.571). Residents in these neighborhoods are mostly PrTdependent due to poor accessibility of PuT to employment opportunities. However, the population density (70% higher than the mean) that specifies the neighborhoods of this type have suitable demand to consider for TOD planning in the future years. 4.1.3. Affluent neighborhoods The wealthier neighborhoods of Delhi belong to this cluster type. These neighborhoods lie between the inner and outer periphery of the city core (see Fig. 6), possess moderately dense (26,781 persons/sq.km) and mix-use (0.68), good network density (42.01 km/ sq.km), and are within the TOD transition zone from the MRT system. Due to the dominance of high-income group residents and larger road network (110% higher than mean), most of the trips in affluent types are PrT-dependent. 4.1.4. Urban commercial core The neighborhoods that include commercial, historical, cultural, political, and core places of the city were classified as the urban commercial core. Many commercial activities such as crafts bazaar, timber trading, and marketing, ancient hotels, theatres, minars, Mughal settlements, etc., are prominent in these neighborhoods. The street network is narrow and very much congested by pedestrian movements due to higher intersection density (68% higher than the mean). This cluster group (630 m) locates within the TOD standard zone to the MRT system (UTTIPEC, 2013), hence, highly accessible to employment opportunities. 4.1.5. Urban mixed core Eleven neighborhoods were classified as the urban mixed core due to heavy mixed-ness (0.75) in their urban structure. Similar to the urban commercial core, these neighborhoods represent the core part of the city and possess high access to jobs using PuT (44% higher than the mean) and good network density (20–22 km/sq.km). In contrast to the existing studies (Huang et al., 2018), the mixed core type has a lower population density (22% less than the mean) and intersection density (5% less than the mean). However, this cluster group lies within the threshold distance of 2000 m from MRT that supports the transition zone for TOD planning (UTTIPEC, 2013). 4.1.6. Transit neighborhoods Neighborhoods that are located within the intense zone (300 m) from MRT stations belong to this cluster type. Transit neighborhoods are characterized by moderate density (20,593 persons/sq.km), diverse land use (0.69), well-established street network (20 km/sq.km), proximity to transit (566.92 m), and possess high accessibility to employment opportunities (26,423 jobs within 45 mins). MoUD (2016) TOD guide mentioned transit neighborhoods as affordable housing districts. It is true for neighborhoods like East of Kailash, Lajpat Nagar, and Badarpur which provide affordable housing options for most of the working population in Delhi. These neighborhoods replicate the urban structure of full-fledged TODs in the literature. For instance, the population density, entropy, road network density, and proximity to the transit of transit neighborhoods are just above the reported values for TOD neighborhoods in Shanghai, China (Chen et al., 2017). 4.2. Validation of neighborhood typology The present study validates the developed typology using independent t-tests on socio-demographic variables and ANOVA tests on travel behavior variables. Table 5 presents the descriptive statistics of socio-demographic variables within each neighborhood type. Independent statistical t-tests were conducted to observe the deviation in demographic parameters within each neighborhood type from the overall sample mean (Higgins and Kanaroglou, 2016). The demographic parameters considered are household income, household size, workers per household, vehicle ownership, male (%), 20–55 yrs old (%), availability of driving license, trip purpose, and mode share (%) in terms of PuT (MRT and bus), PrT (car and two-wheeler), and AT (walk, cycle and auto-rickshaw) modes. As observed in Table 5, there exist significant differences in socio-demographic variables among developed clusters. The average household income of residents in affluent neighborhoods is 44.4% higher, and of Transit neighborhoods are 37.1% lower than the sample mean. As expected, 66.5% of residents in affluent neighborhoods use PrT modes and only 5.4% of them use AT modes. Residents in low-density, urban residential and affluent types own more than one vehicle. It reflects the evidence that the 12

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

Table 5 Socio-demographic Characteristics of Neighborhood Typology. Typology

Low-Density

Urban Residential

Affluent

Urban Commercial Core

Urban Mixed Core

Transit

Overall Mean (Std.Dev)

Male (%)

0.628* (6) 29916.32* (−24) 3.438* (4) 1.253* (−7) 1.177* (9.1) 0.764* (15) 0.828 (3)

0.662* (12) 35358.592* (−10) 3.388 (−2) 1.359 (−0.4) 1.053 (−2) 0.663 (0) 0.762* (−4)

0.645* (9) 65291.800* (67) 3.206 (−3) 1.269* (−6) 1.623* (50) 0.774* (17) 0.845* (5)

0.656* (11) 35364.807* (−10) 3.348* (1) 1.468* (9) 0.981* (−9) 0.622* (−6) 0.832* (4)

0.488* (−18) 30587.121* (–22) 3.521* (6) 1.398 (3) 0.813* (−25) 0.524* (−21) 0.708* (−12)

0.664* (12) 24587.264* (−37) 3.318 (0.22) 1.333* (−2) 0.809* (−25) 0.632* (−5) 0.894* (12)

0.593 (0.351) 39380.52 (25293.020) 3.311 (0.881) 1.360 (0.546) 1.08 (0.799) 0.670 (0.471) 0.801 (0.109)

0.300 (1) 0.130* (−10) 0.110* (1) 0.46 (1)

0.250* (−14) 0.166* (16) 0.121* (23) 0.463* (0)

0.312* (7) 0.174* (22) 0.078* (−20) 0.436 (−6)

0.322* (10) 0.118* (−18) 0.043* (−9) 0.516* (11)

0.263* (−10) 0.125* (−13) 0.191* (16) 0.488* (5)

0.34* (17) 0.070* (−51) 0.054* (−8) 0.536* (15)

0.292 (0.298) 0.143 (0.350) 0.098 (0.229) 0.466 (0.22)

0.693* (24) 0.105* (−10) 956

0.562* (0) 0.166* (42) 990

0.665* (19) 0.054* (−54) 634

0.511* (−9) 0.085* (−27) 513

0.435* (−23) 0.204* (74) 1231

0.386* (−31) 0.151* (29) 1229

0.592 (0.154) 0.119 (0.187) 5553

Household Income (INR.) Household Size (Nos.) Workers per Household (Nos.) Vehicle Ownership (Nos.) Driving License (%) 20–55 Years Old (%) Trip Purpose (%) Business Education Recreation Work Mode Share (%) Private transport (Car + 2-wheeler) Active transport (Walk + Cycle + IPT) Number of Samples

Note: Value in the parenthesis is the % difference from the overall mean value, *indicates statistical significance at 0.05% level.

proximity to transit plays a major role in vehicle ownership (Cervero, 2002). The proportion of working and business trips are 53.6% and 34% (15% and 17% higher than the overall mean, respectively) in transit neighborhoods, followed by the 51.6% and 32.2% in urban commercial core type, respectively. Also, in transit type, a significant proportion (13.5%) of middle-age individuals use AT modes, preponderance that children and elders prefer other safer modes. Residents in mixed core neighborhoods have a higher share (74.2 times higher than the overall mean) of AT mode choice compared to other neighborhood types. Due to the presence of high land use mix, 19.1% (15.5% than the sample mean) of trips in the urban mixed core are recreational. Further, the developed typology was verified using travel behavior outcomes such as VKT, vehicle time traveled (VTT), and mode choice (Austin et al., 2010). Three separate ANOVA tests were conducted on VKT, VTT, and public mode share (%) as dependent variables. ANOVA test facilitates whether there exist significant differences in the mean values of dependent variables among clusters by a nominal independent variable (Devore and Peck, 2001). Table 6 presents the results of ANOVA tests with developed clusters as independent variables. All the three ANOVA tests were found significant at a 95% confidence interval. It indicates that the neighborhood types are statistically different in terms of travel behavior outcomes and the typology found valid. The average VKT and VTT were found highest in low-density (9.26 km in 63.32 mins) and lowest in transit neighborhoods (6.10 km in 41.67 mins). Next, to transit neighborhoods, residents in urban commercial core and urban mixed core types are completing their trips at an average of 6.25 km in 48 mins and 7.12 km in 47.76 mins, respectively. Besides, the mean values of PuT share (%) vary from low-density to transit neighborhoods. Only 20.2% and 27.2% of residents in low-density and urban residential types use PuT, respectively. It attributes to the fact that residents in low-density neighborhoods mostly rely on PrT modes to complete their daily trips. Transit neighborhoods are fascinating with 46.3% of residents use PuT modes.

Table 6 Results of Two-way ANOVA tests. Dependent variable

VKT (km) VTT (min) Public mode (%)

Neighborhood Typology

Overall Mean

Low Density

Urban Residential

Affluent

Urban Commercial Core

Urban Mixed Core

Transit

9.26 63.32 0.202

7.80 50.52 0.272

9.02 53.75 0.281

6.25 48.00 0.403

7.12 47.76 0.360

6.10 41.67 0.463

13

7.626 50.06 0.290

Test Results F

sig.

38.47 25.61 20.37

0.000 0.005 0.007

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

These findings are interesting and emerging in two important inferences. Firstly, the neighborhoods which lie within the standard zone (800 m) to transit are performing well in terms of VKT, VTT, and public mode share, i.e., producing positive travel behavior. Especially, residents living in such neighborhoods are found to travel shorter distances and spending less time to complete their vehicular trips. These findings are consistent and add evidence to the existing studies that living near transit facilities reduce VKT (Zegras 2010; Kamruzzaman et al., 2014). Secondly, the affluent neighborhoods that are already transit-rich (within 1200 m) have shown weak values due to the high-income PrT-oriented travel behavior of residents. It adds evidence to the previous findings that high-income group residents are reluctant to PuT usage (Churchman, 1999; Cervero, 2002; Pongprasert and Kubota, 2019). 5. Effects of urban structure on trip distance Based on statistical differences among urban structure, socio-demographic, and travel behavior variables between clusters, the developed typology was verified and validated. The neighborhood types were further examined their potential to become TODs based on conceptual relationships between urban structure and travel behavior. The present study developed individual MLR models for examining the relationships among urban structure characteristics (group-level variables), socio-demographic characteristics (individual-level variables), and trip distance (dependent variable) between neighborhoods (group-level indicator) of each neighborhood type. The present study employed MLR models due to its ability in handling spatial dependency and multi-collinearity issues among observations. While predicting the conceptual relationships into the six models (one for each type), the influencing variables were mean-centered and estimated based on restricted maximum likelihood (Albright and Marinova, 2010). The interclass correlation (ICC) was calculated using Eq. (3) for both intercept only and full models, separately. The ICC values for each model were found larger than 0.1, indicating that MLR models were appropriate for the data. The present study reported the 95% confidence interval (CI) values instead of mean estimates of parameters and p values. The 95% CI values infer that the parameters which do not include ‘zero’ between the lower bound and upper bound estimates are said to be statistically significant. Literature suggests that the trip distance not only varies between neighborhoods but also varies upon the mode choice (Cao et al., 2009). That said, mode choice may mediate the effects of urban structure on the distance traveled. Hence, the mode choice as an exogenous variable in the model may lead to endogeneity bias. The present study eliminated this endogeneity bias by developing two separate MLR models for each neighborhood type, one for PrT (car and two-wheeler) and another for PuT (bus and MRT) trips. The separate MLR models reflect the travel patterns and socio-demographics of individuals while using PrT and PuT modes. Thus the existence of indirect effects of urban structure on travel behavior can be presumed when two MLR models of a neighborhood type compared to each other. Tables 7 and 8 demonstrates the results of MLR models on distance traveled by PrT and PuT modes within each neighborhood type, respectively. The results indicated that the mode available in a neighborhood show a noticeable variation on trip distance. 5.1. Socio-demographic characteristics With regard to socio-demographic factors, high-income residents tend to travel longer distances using PrT modes, whereas lowincome residents choose PuT for longer distances. It is plausible that for high-income groups, travel distance, and cost associated with it hardly matters. Age is another significant parameter that influences trip distance. Elders and male individuals are most likely to Table 7 MLR Results for Trip Distance by Private Transport (Car + Two-Wheeler). Parameter

Intercept Age Female Income Travel Cost Driving License Population Density Entropy Network Density Intersection Density Distance to transit Job Accessibility

Low Density

Urban Residential

Affluent

Urban Commercial

Urban Mixed

Transit

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

0.270 0.017 −0.188 0.030 0.681 −0.226 0.094 −0.357 0.004 0.045 −0.488 −0.361

1.477 0.060 −0.027 0.071 0.776 0.037 0.519 0.019 0.308 0.194 −0.046 −0.048

0.208 0.024 −0.221 0.021 0.736 0.043 0.028 −0.053 0.049 −0.086 −0.218 −0.888

0.898 0.040 −0.108 0.067 0.820 0.298 0.129 0.065 0.057 0.040 0.027 −0.325

−0.315 0.016 −0.206 0.066 0.714 0.023 −0.077

0.408 0.041 −0.072 0.149 0.782 0.275 0.030

0.040

0.049

−0.322 0.050 −0.384 0.011 0.686 −0.110 −0.379 −1.134 0.209

0.535 0.059 −0.152 0.104 0.809 0.276 −0.034 −0.051 0.323

−0.070

0.034

−0.069 0.676

0.691 −0.516

−0.216 0.028 −0.227 0.010 0.746 −0.119 −0.224 −0.276 −0.361 −0.025 0.008 −0.302

0.262 0.036 −0.118 0.080 0.810 0.078 −0.023 −0.134 0.134 0.123 0.126 −0.113

−0.417 0.019 −0.237 0.007 0.709 −0.010 −0.431 −0.231 −0.146 −0.179 0.253 −0.774

1.263 0.036 −0.120 0.058 0.774 0.196 −0.076 −0.076 0.058 −0.097 0.304 −0.090

Akaike Information Criteria (AIC) Intercept Only 830.186 Full Model 182.883

1300.364 229.285

1167.391 131.681

812.164 205.447

1815.728 264.790

1634.257 175.949

Interclass Correlation (ICC) Intercept Only 0.228 Full Model 0.496

0.289 0.370

0.374 0.448

0.109 0.420

0.155 0.548

0.273 0.595

Note: Lower and Upper bound values are at a 95% confidence interval. 14

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

Table 8 MLR Results for Trip Distance by Public Transport (Bus + MRT). Parameters

Intercept Age Female Income Travel Cost Driving License Population Density Entropy Network Density Intersection Density Distance to transit Job Accessibility

Low Density

Urban Residential

Affluent

Urban Commercial

Urban Mixed

Transit

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

−1.295 −0.104 0.254 −0.063 0.411 −0.172 −0.269 −0.266 −0.206 −0.092 −0.261 −0.285

0.390 −0.032 0.428 −0.059 0.578 −0.004 0.350 0.124 0.220 0.097 0.291 0.206

−0.867 −0.069 0.060 −0.106 0.406 −0.192 0.013 −0.020 −0.207 −0.157 −0.409 0.535

1.114 0.037 0.303 −0.099 0.612 0.051 0.271 0.226 0.049 0.189 0.334 1.357

−1.148 −0.091 0.127 −0.167 0.277 −0.381 −0.578

0.237 0.060 0.593 −0.077 0.496 0.135 −0.110

0.032

0.408

−2.046 −0.169 0.130 −0.308 −0.023 −0.354 −0.901 −0.860 −1.238

0.406 0.198 0.630 −0.186 0.411 0.238 0.194 2.766 0.536

0.227

0.602

−2.002 0.918

−0.841 3.606

−0.763 −0.018 0.267 −0.089 0.446 −0.232 0.108 0.096 −1.089 0.178 −0.139 0.098

0.488 0.054 0.458 −0.023 0.551 −0.081 0.555 0.156 −0.173 0.277 −0.081 0.282

−3.760 −0.033 0.026 −0.090 0.196 −0.063 0.503 0.227 −0.496 0.411 −1.201 0.876

1.453 0.079 0.191 −0.079 0.355 −0.322 0.596 0.277 −0.088 0.460 −0.506 1.795

Akaike Information Criteria (AIC) Intercept Only 719.302 Full Model 515.899

777.267 406.223

291.827 191.152

320.102 204.788

930.268 483.229

1044.663 608.992

Interclass Correlation (ICC) Intercept Only 0.427 Full Model 0.585

0.371 0.498

0.385 0.557

0.228 0.508

0.321 0.548

0.277 0.562

Note: Lower and Upper bound values are at a 95% confidence interval.

travel long distances using PrT modes, whereas young, and female individuals tend to travel shorter distances and choose PuT modes. It is an interesting finding and indicates the sustainable mobility patterns of female and young individuals. Vehicle ownership has a positive and negative influence on trip distances using PrT and PuT modes, respectively (see Tables 7 and 8), which implicates that owning vehicles encourage people to drive more. All the above findings are consistent and accord with previous findings on gender and mode choice (Vance and Lovanna, 2007; Patterson et al., 2005; Hong et al., 2014). Notably, holding a driving license has shown insignificant influence on trip distance, except in the case of urban residential and affluent types, where it generally increases VKT.

5.2. Urban structure characteristics The six TOD factors are found to be highly related to the trip distance for both PrT and PuT models. Neighborhood types with higher population densities have proved to lower VKT and higher transit distances. This trend is not plausible in neighborhood types where higher population densities have shown a positive influence on VKT. It is as expected and attributed due to the presence of high-income residents and PrT-dominated streets in affluent and urban residential types. The effect of entropy index is varying across neighborhood types, but it generally decreases VKT and increase PuT usage for longer distances. On the other hand, VKT increases with greater network density; reduce the transit distances. Additionally, an increase in intersection densities will lower VKT and increase PuT distances. However, for low-density type, these relationships are converse. Distance to transit has a significant and positive effect on VKT and also tends to generate shorter distances using PuT. Notably, neighborhood types that lie outside the standard zone from MRT have shown atypical relationships of TOD factors on trip distance. Additionally, neighborhood types with good job accessibility have lower VKT and greater transit distances. The findings indicate that the urban structure has a dominating and apparent effect on trip distance as compared to socio-demographic factors and is more pronounced in neighborhood types that lie within the standard zone (800 m) from MRT.

Table 9 Summary of Average Elasticities of TOD Factors on VKT. TOD Factors

Population Density Entropy Intersection Density Network Density Distance to Transit Job Accessibility

Proven Effects

Expected Effects

Low Density

Urban Residential

Affluent

Urban Commercial

Urban Mixed

Transit

0.11* – 0.02* 0.03* −0.27* −0.17*

0.40* 0.49 0.06* −0.15 – −0.10*

– 0.02 – – −0.14 0.11

−0.65* −0.07* – 0.10* – –

−0.20* −0.17* 0.07 – 0.14* –

−0.25* −0.23* −0.05* 0.07 0.20* −0.07*

* Indicates statistical significance at 95% confidence interval. 15

−0.04 −0.09 −0.12 0.12 0.05 −0.05

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

5.3. Conceptual relationships Table 9 illustrates the expected directions of six TOD factors explained by 5Ds of urban structure on VKT at six neighborhood types. The average elasticities represent effect sizes, which is the ratio of mean values of dependent and independent variables weighted by regression coefficients (Ewing and Cervero, 2010). The effect sizes for each neighborhood type were calculated from linear regression coefficients and mean values of six TOD factors and corresponding VKT values. It is observed from Table 9 that the estimated elasticities are two to three times higher than the average expected elasticities. However, the magnitude of the average elasticities as reported in Ewing and Cervero (2010) do not have any statistical confidence, yet the directionality ( ± ) would augment the conceptual relationships between urban structure and VKT. For low-density types, the distance to transit is strongly and negatively correlated with VKT. This factor act as a proxy for all other factors, indicating that living far from transit means far from the city core, lower densities with low job accessibility. The most strongly related factor with VKT in urban residential type is population density (0.40) since entropy (0.49) is found insignificant. This is surprising, given the expected effects in the literature on density dimension is converse. Further, the directionality of weighted elasticities of two design factors is not identical to expected effects. The elasticities of VKT related to all TOD factors are insignificant at the affluent type. Again, population density has a strong and negative relation with VKT of urban commercial, urban mixed and transit types. This appears to be population density as the primary determinant of VKT, except for low-density type. Next most strongly related to VKT are entropy and distance to transit factors. Also, surprising are the two factors of the design dimension that have smaller elasticities. However, literature mentions that shorter networks with many intersections apparently shorten travel distances (Ewing and Cervero, 2010). Among the proposed typology, transit type has pronounced consistent relationships between urban structure and VKT, after controlling for socio-demographic variables. The population density has a negative correlation with distance traveled and is consistent with the previous findings (Cao et al., 2009; Sardari et al., 2018). This finding evidence that door-to-door access to opportunities encourages long-distance trips using public transit (Djurhuus et al., 2014). The intersection density has a significant and negative effect on VKT and is consistent with the Hong et al. (2014) and Sardari et al. (2018) findings. Furthermore, the network density showed a negative impact on the VKT, as supported by Chen et al. (2017). As expected, the availability of transit within proximity tends the individuals to travel long distances. All these consistent relationships are evidence that the neighborhoods in transit type have the potential to replicate TODs as a concept. 6. Suggested parameters for TOD typology design 6.1. Macro-level planning parameters Examining the six TOD types across 47 neighborhoods of Delhi, it is clear that the proposed typology is conducive to TOD planning. Most of the neighborhood types feature higher population densities, an explicit mix of land uses, a well-established street network, proximity to MRT, and good access to jobs using PuT modes. For instance, the average population densities of the typology are higher than the threshold density of 8100 persons/sq.km as reported for ‘neighborhood’ TODs (Calthorpe, 1993). The entropy values are within the range of 0.56–0.75, which is higher than 24 other Chinese cities (0.58) associated with the urban rail transit system (Gu et al., 2019). Neighborhoods in urban commercial, urban mixed and transit types have entropy values closer to the TOD neighborhoods (0.74) in Shanghai, China (Chen et al., 2017). The intersection densities in Affluent (119) and Urban commercial (129) types are higher than TAD (110) types, but lower than TOD (386) types of eight US metropolitan areas (Park et al., 2018). Fig. 7 demonstrates the comparison of the average population densities and public mode shares of developed typology with global transit-oriented cities. The urban commercial and mixed core types feature some elements of TOD like higher densities and reasonable PuT usage, as compared to TODs in developed cities like Washington DC, New York, and London. But showing very weak PuT shares as compared to TODs in Asian cities. Low-density neighborhoods exhibit very low densities and poor PuT use. The situation is similar to urban residential but possesses very high population densities. Besides, an emphasis on job accessibility through walking may support affluent types in increasing PuT usage, where the pedestrian environment is lacking due to PrT-oriented street design. The findings suggest that twofold improvements in urban structure may reorient such neighborhoods towards MRT. Firstly, to provide MRT facilities within the standard zone (800 m) of neighborhoods which will further increase access to employment opportunities. Secondly, to provide better street connectivity through AT with the available road network which may predominantly increase PuT usage. Further, it is complex to identify which neighborhood type i.e., urban commercial or urban mixed core has the future potential for TOD planning in Delhi. The average population density in the urban mixed core is almost half of the urban commercial core, which makes the mixed core neighborhoods need retrofitting of urban structure to turn into full-fledged TODs. In particular, the mixed core types require “articulated densities” i.e., strategic distribution of densities around MRT for urban structure integration. It should again be clarified that the neighborhood types like low-density that presently have bus service, may improve when MRT services become available, but there nevertheless still appears to be lacking in terms of job accessibility, pedestrian environment, and population densities. Following the TOD concept, the typology suggests that transit neighborhoods have a large room for improvements. Transit type exhibits respectable rates of PuT and AT usage, mixed-ness, job accessibility, and lower VKT/VTT, as well as an apparent attractiveness to younger and working individuals. These neighborhoods stand as potential sites for TOD implementation with policy interventions that prioritize AT accessibility, urban mix, and better balance them. Such interventions in urban structure will alleviate 16

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

Fig. 7. Population Densities and Public Mode Share in Global TOD Cities and Typology. (Source: Data of global cities was compiled from Cox, 2017 and Suzuki et al., 2015).

MRT as a major attractor and generator of transit trips. It could move the transit type to the top of the pyramid of neighborhood types and begin to replicate ideal TOD characteristics.

6.2. Micro-level planning parameters The typology in a quantitative and aggregate sense using 5Ds may also influence the nature of TODs in terms of micro-scale planning aspects such as building heights, floor-area-ratio (FARs), parking, etc. Table 10 illustrates the suggested policy interventions for typology in global TOD cities through the FAR mix to maximize the potential and ensure that the development is truly TOD and not TAD. The changes in land use policies by allocating higher FARs and converting single-use to mixed can increase densities and market revenues (Rodriguez et al., 2016). The proposed maximum FAR for Delhi is much lower than those of global TOD cities (for instance, 4.0 in Delhi versus 20.0 in Tokyo) (Suzuki et al., 2015). It is attributed to the fact that the policymakers and planners in most developing cities like Delhi are reluctant to increasing FARs since they are perceived to accumulate overcrowding, traffic congestion, and pollution (Suzuki et al., 2013). However, a new land use policy that differentiates higher FARs and mixed-use based on job accessibility to MRT would allow Delhi to develop a stronger and integrated TOD planning approach. Any policy interventions such as the increase in FARs must be sensitive to existing conditions and it may be deemed to be appropriate or acceptable that neighborhood types, for example, transit type have the greater potential for TOD with those interventions. The other neighborhood types may still benefit from tuning TOD indicators and offer a relative case for policy interventions based on transit neighborhoods. For example, it would be productive in making use of mixed and compact nature of urban structure in low-density types towards more walkable environments. The high-density colonies in urban residential neighborhoods shall reflect the TOD characteristics when establishes mixed and AT-friendly infrastructure. For affluent types, transportation and land use policies must focus on restricting parking and attracting high-income group residents to transit facilities. New plans to alter the Table 10 Suggested Floor-Area-Ratios (FARs) for Typology in Global TOD Cities. Typology

Proposed FAR Mix for Delhi*

Tokyo

Hong Kong

Singapore

New York

Seoul

All Residential Low-Density Medium-Density High-Density Commercial Industrial Mixed Use Maximum

Residential – – – Residential Residential Residential 4

– 0.5–2.0 1.0–5.0 1.0–5.0 2–13.0 1.0–4.0 1.0–4.0 20

– 0.2–3.0 0.67–5.0 6.5–10.0 3.5–12.0 1.0–12.0 – 12

1.4–11.2 – 1.4–11.2 1.4–11.2 1.4–12.6 1.0–3.5 1.4–25.0 25

– 0.5–16.5 0.78–7.2 0.99–12.0 0.78–15 1.0–10.0 0.78–10.0 15.00

1.0–7.0 1.0–7.0 1.0–7.0 1.0–7.0 1.0–15.0 1.0–4.0 1.0–7.0 10

55%, Commercial 5% Community 10%, Retail 30%

30%, Commercial 50%, Community 10%, Retail 10% 30%, Commercial 5%, Community 10%, Industrial 55% 30%, Commercial 5%, Community 10%, Retail 55%

* FAR Mix proposed by WRI (2018). Data on global TOD cities was compiled fromSalat and Ollivier (2017). 17

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

established FARs in mixed core types will generate articulated densities, thereby promote transit usage. Although commercial core has the potential for high-density developments, attaining AT-friendly streets will reduce traffic congestion and increase sustainability. Thus, planners, policymakers, and government bodies need to account for these policy parameters that offer TOD design in the present and future. 7. Conclusions and policy implications The present study identified that the neighborhood types that are within the standard zone (800 m) from MRT have strong potential for TOD planning, even though most of the neighborhood types already have densities and mixed-land uses comparable to other TOD cities in Asia and Latin America. It suggests that urban structure can play a significant role in inducing neighborhoods towards TOD when applied as rail-based redevelopment policy plans. In other words, TOD planning could be successful when applied to the MRT standard zone just as evident from the study findings. Considering the high-density and mixed-use conditions of neighborhoods in Delhi, TOD planning and policies in these neighborhoods need to learn from those of Asian cities such as Hong Kong, Shanghai, Seoul etc. Application strategies need to emphasize not density and diversity itself, but the other macro and micro level planning factors such as design, accessibility, FARs, parking, etc. which can augment TOD success in the neighborhoods of Delhi. From the study findings, three major policy implications were found to be suitable: enhancing a transit-proximity and accessibility function, emphasizing the density and mixed-use bonus, and guiding AT-friendly urban design. Firstly, the quality and quantity for transit (bus and MRT) connectivity within transit type should be focused to create more TOD neighborhoods. In western cities, the TOD concept was to achieve 3Ds i.e., density, diversity, and design around transit stations. However, in Asian cities like Delhi, where MRT has already been well established, the quantity and quality of transit service, in addition to enhancing priority dimensions such as design and job accessibility can be expected to produce positive travel behavior such as lowering VKT and traffic congestion, increase in PuT and AT usage, and market values. The study results demonstrated that not only do transit type but also mixed and commercial core types have closely related to positive travel behavior. For example, high population density lower VKT, entropy not only affect and decrease VKT but also has a significant and positive correlation with PuT usage. In this regard, better job accessibility and street design around transit facilities could have more synergistic effects in decreasing VKT and attracting PuT users. It implies that designing an AT-accessible street design increase PuT usage in neighborhoods with high densities, mixed-use, and transit proximity. Secondly, the density and mixed-use bonus as a strategic approach would be a powerful policy measure in promoting PuT usage. In Asian cities like Delhi, where high densities and mixed-use settlements are prominent, a high-density development near a transit station may not play a significant role because the maximum influence level of the strategy has already available. Although even higher FARs strategy could possibly promote PuT usage in low-density type, there is also the likelihood that it can exacerbate the already congested traffic conditions as observed in urban residential type. Thus, cautionary policy measures are essential in the application of high-density and mixed-use strategy when applying TOD parameters to such neighborhood types. Despite the fact that the high-density and mixed-use strategy is not applicable to all neighborhood types of Delhi, strategic approaches are necessary to offer incentives to guarantee land and market values. For example, providing a density and mixed-use bonus as an incentive to developers would be more powerful in attracting more transit users if it were applied to low-density types. Such an approach to effectively manage high-density and mixed-use bonuses can automatically go together with land and market values while encouraging TOD-based urban development. Third and finally, the AT-friendly street design is still an important policy parameter in promoting TOD conditions. The study identified that mixed core type has almost similar relationships as transit type with higher AT shares. It implies that AT-friendly street design needs to be emphasized in achieving TOD conditions more than high-density and mixed-use in such neighborhoods. Literature has shown that a pedestrian-friendly street design such as a grid-type road pattern with higher intersections and lower PrT network density has a positive influence on lowering VKT in neighborhoods. As the design parameters were examined for the neighborhood types of Delhi, results showed that they were less effective in decreasing VKT. For example, the average elasticity (−0.05) of intersection density in transit type is lower than those of expected (−0.12) and overall effects. Yet other neighborhood types have shown to have different elasticities in terms of directionality and magnitude at statistically significant levels. For all neighborhood types (except for transit type), the network density showed distinct results when compared to the findings from existing studies on urban design-travel behavior relationships. They have reported that VKT will increase with an increase in PrT-network density but decrease with an increase in AT-network density. Conversely, in regards to AT-friendly street design, the use of walk modes is found better to promote PuT usage. Despite the fact that the walk-friendly design strategy is applicable in creating a TOD neighborhood, it may not be applicable to the AT users in neighborhood types, for example, mixed core and urban residential, where AT modes such as e-rickshaw are prominent. Thus, a strategic approach is necessary to offer street design to all AT modes such as walking, cycling, and e-rickshaw around the transit facilities. Such a peculiar urban street design in the neighborhoods of Delhi seems to differ from that of western cities and are comparable to the South-East Asian cities. With the findings of the study as a foundation, upcoming research can employ the modeling approach in establishing TOD typology for other Indian cities. This method can be used as a structure to ensure government agencies while investing funds, based on existing TOD conditions and interpreting relationships to safeguard future capitals. Besides, future research need to focus on establishing the FAR standards for TOD development among neighborhood typology. This research has certain limitations related to the modeling approach. The outputs of the two-step cluster analysis will undoubtedly return different cluster types using different neighborhood data. As suggested in Higgins and Kanaroglou (2016), we can achieve a more generalized typology by incorporating large datasets from multiple regions. Such a standardized typology would enable straight comparisons with other study areas. The 18

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

study findings can be improved by considering the non-linear effects of urban structure on travel behavior among neighborhood typology. CRediT authorship contribution statement P. Phani Kumar: Conceptualization, Methodology, Software, Validation, Formal analysis, Writing - original draft. Ch. Ravi Sekhar: Investigation, Resources, Data curation, Writing - review & editing. Manoranjan Parida: Writing - review & editing, Visualization, Supervision. Acknowledgment The research work is part of a Doctoral research work, which is supported by the fellowship from the Ministry of Human Resource and Development (MHRD), India. Appendix A. Supplementary material Supplementary data to this article can be found online at https://doi.org/10.1016/j.trd.2019.11.015. References Albright, J.J., Marinova, D.M., 2010. Estimating Multilevel Models using SPSS. Information Technology Services, Indiana University. http://www.indiana.edu/ ~statmath/stat/all/hlm/. Atkinson-Palombo, C., Kuby, M.J., 2011. The geography of advance transit-oriented development in metropolitan Phoenix, Arizona, 2000–2007. J. Transp. Geogr. 19 (2), 189–199. https://doi.org/10.1016/j.jtrangeo.2010.03.014. Austin, M., Belzer, D., Benedict, A., Esling, P., Haas, P., Miknaitis, G., et al., 2010. Performance-Based Transit-Oriented Development Typology Guidebook. Center For TOD, pp. 91 (December). Bacher, J., Wenzig, K., Vogler, M., 2004. SPSS Two-Step Cluster — A First Evaluation. Universität Erlangen-Nürnberg, Nürnberg, Germany. Bagley, M.N., Mokhtarian, P.L., 2002. The impact of residential neighborhood type on travel behavior: a structural equation modeling approach. Ann. Reg. Sci. 36 (2), 279–297. https://doi.org/10.1007/s001680200083. Bhat, C.R., 2000. A multi-level cross-classified model for discrete response variables. Transp. Res. Part B: Methodol. https://doi.org/10.1016/S0191-2615(99)00038-7. Bertolini, L., 1999. Spatial development patterns and public transport: the application of an analytical model in the Netherlands. Plan. Pract. Res. 14 (2), 199–210. https://doi.org/10.1080/02697459915724. Calthorpe, P., 1993. The Next American Metropolis. Princeton Architectural Press, New York. Cao (Jason), X., Mokhtarian, P.L., Handy, S.L., 2009. The relationship between the built environment and nonwork travel: a case study of Northern California. Transp. Res. Part A: Policy Pract. 43 (5), 548–559. https://doi.org/10.1016/j.tra.2009.02.001. Census, 2011. Census of India 2011. http://www.censusindia.gov.in/2011-provresults/prov_data_products_ani.html. Cervero, R., 2002. Built environments and mode choice: toward a normative framework. Transp. Res. Part D: Transp. Environ. 7 (4), 265–284. https://doi.org/10. 1016/S1361-9209(01)00024-4. Cervero, R., Dai, D., 2014. BRT TOD: Leveraging transit oriented development with bus rapid transit investments. Transp. Policy. https://doi.org/10.1016/j.tranpol. 2014.08.001. Cervero, R., Duncan, M., 2006. Which reduces vehicle travel more: jobs-housing balance of retail-housing mixing? J. Am. Plan. Assoc. 72 (4), 475–490. https://doi. org/10.1080/01944360608976767. Cervero, R., Murakami, J., 2009. Rail and property development in Hong Kong: experiences and extensions. Urban Stud. https://doi.org/10.1177/ 0042098009339431. Chatman, D., 2003. How density and mixed uses at the workplace affect personal commercial travel and commute mode choice. Transp. Res. Rec. 1831 (03), 193–201. https://doi.org/10.3141/1831-22. Chen, F., Wu, J., Chen, X., Zegras, P.C., Wang, J., 2017. Vehicle kilometers traveled reduction impacts of Transit-Oriented Development: evidence from Shanghai City. Transp. Res. Part D: Transp. Environ. 55, 227–245. https://doi.org/10.1016/j.trd.2017.07.006. Churchman, A., 1999. Disentangling the concept of density. J. Plan. Literat. 13 (4), 389–411. https://doi.org/10.1177/08854129922092478. Cox, W., 2017. The 37 Megacities and Largest Cities: Demographia World Urban Areas: 2017. Newgeography.com. CSIR - Central Road Research Institute (CRRI), 2017. Development and Application of Technologies for Sustainable Transportation (SUSTRANS). 12th Five Year Plan Network Project (ESC-0106). Devore, J., Peck, R., 2001. The Exploration and Analysis of Data, fourth ed. Duxbury, Pacific Grove, CA. Ding, C., Lin, Y., Liu, C., 2014. Exploring the influence of the built environment on tour-based commuter mode choice: a cross-classified multilevel modeling approach. Transp. Res. Part D: Transp. Environ. https://doi.org/10.1016/j.trd.2014.08.001. Ding, C., Cao, X., 2019. How does the built environment at residential and work locations affect car ownership? An application of the cross-classified multilevel model. J. Transp. Geogr. https://doi.org/10.1016/j.jtrangeo.2019.01.012. Dittmar, H., Poticha, S., 2004. The new transit town: best practices in transit-oriented development. Transportation 71 (i), 253. https://doi.org/10.5860/CHOICE.420424. Djurhuus, S., Hansen, H.S., Aadahl, M., Glümer, C., 2014. Individual public transportation accessibility is positively associated with self-reported active commuting. Front. Public Health. https://doi.org/10.3389/fpubh.2014.00240. DMRC, 2017. Delhi Metro Rail Corporation (DMRC). http://www.delhimetrorail.com/press_reldetails.aspx?id=bhc0DzbE6tklld. DTC, 2015. Delhi Transport Corporation (DTC). http://delhi.gov.in/wps/wcm/connect/DOIT_DTC/dtc/home. Ewing, R., Cervero, R., 2010. Travel and the built environment. J. Am. Plan. Assoc. 76 (3), 265–294. https://doi.org/10.1080/01944361003766766. Ewing, R., Tian, G., Lyons, T., Terzano, K., 2017. Trip and parking generation at transit-oriented developments: five US case studies. Landsc. Urban Plann. 160, 69–78. https://doi.org/10.1016/j.landurbplan.2016.12.002. Florida Department of Transportation (FDOT), 2012. Florida TOD Guidebook. http://www.fltod.com/Florida%20TOD%20Guidebook-sm.pdf. Gu, P., He, D., Chen, Y., Zegras, C., Jiang, Y., 2019. Transit-oriented development and air quality in Chinese cities: a city-level examination. Transp. Res. Part D: Transport Environ. https://doi.org/10.1016/j.trd.2018.03.009. Handy, S., Cao, X., Mokhtarian, P., 2005. Correlation or causality between the built environment and travel behavior? Evidence from Northern California. Transp. Res. Part D: Transport Environ. 10 (6), 427–444. https://doi.org/10.1016/j.trd.2005.05.002. Higgins, C.D., Kanaroglou, P.S., 2016. A latent class method for classifying and evaluating the performance of station area transit-oriented development in the Toronto region. J. Transp. Geogr. 52, 61–72. https://doi.org/10.1016/j.jtrangeo.2016.02.012. Hong, J., Shen, Q., Zhang, L., 2014. How do built-environment factors affect travel behavior? Spatial analysis at different geographic scales. Transportation. https:// doi.org/10.1007/s11116-013-9462-9. Hox, J.J., Moerbeek, M., Van de Schoot, R., 2017. Multilevel Analysis: Techniques and Applications. Routledge.

19

Transportation Research Part D 78 (2020) 102186

P. Phani Kumar, et al.

Huang, R., Grigolon, A., Madureira, M., Brussel, M., 2018. Measuring transit-oriented development (TOD) network complementarity based on TOD node typology. J. Transp. Land Use 11 (1). https://doi.org/10.5198/jtlu.2018.1110. Jain, S., Aggarwal, P., Kumar, P., Singhal, S., Sharma, P., 2014. Identifying public preferences using multi-criteria decision making for assessing the shift of urban commuters from private to public transport: a case study of Delhi. Transport. Res. F: Traffic Psychol. Behav. 24, 60–70. https://doi.org/10.1016/j.trf.2014.03.007. Kamruzzaman, M., Baker, D., Washington, S., Turrell, G., 2014. Advance transit oriented development typology: case study in Brisbane, Australia. J. Transp. Geogr. 34, 54–70. https://doi.org/10.1016/j.jtrangeo.2013.11.002. Kamruzzaman, M., Baker, D., Washington, S., Turrell, G., 2016. Determinants of residential dissonance: implications for transit-oriented development in Brisbane. Int. J. Sustain. Transp. 10 (10), 960–974. https://doi.org/10.1080/15568318.2016.1191094. Kim, C., Wang, S., 2015. Empirical examination of neighborhood context of individual travel behaviors. Appl. Geogr. https://doi.org/10.1016/j.apgeog.2014.10.017. Knowles, R.D., 2012. Transit-oriented development in Copenhagen, Denmark: from the finger plan to Ørestad. J. Transp. Geogr. 22, 251–261. https://doi.org/10. 1016/j.jtrangeo.2012.01.009. Kong, W., Pojani, D., 2017. Transit-oriented street design in Beijing. J. Urban Des. 22 (3), 388–410. https://doi.org/10.1080/13574809.2016.1271700. Kumar, P.P., Ravi Sekhar, C., Parida, M., 2018. Residential dissonance in TOD neighborhoods. J. Transp. Geogr. 72. https://doi.org/10.1016/j.jtrangeo.2018.09.005. Laaly, S., Jeihani, M., Lee, Y.-J., 2017. A multiscale, transit-oriented development definition based on context-sensitive paradigm. Transp. Res. Rec. 2671, 31–39. https://doi.org/10.3141/2671-04. Lin, J.J., Gau, C.C., 2006. A TOD planning model to review the regulation of allowable development densities around subway stations. Land Use Policy 23 (3), 353–360. https://doi.org/10.1016/j.landusepol.2004.11.003. Lyu, G., Bertolini, L., Pfeffer, K., 2016. Developing a TOD typology for Beijing metro station areas. J. Transp. Geogr. 55, 40–50. https://doi.org/10.1016/j.jtrangeo. 2016.07.002. Ministry of Urban Development (MoUD), 2016. Transit-oriented development guidance document. http://www.sutpindia.com/skin/pdf/TOD%20Guidance %20Document%20compressed.pdf. Mokhtarian, P.L., Cao, X., 2008. Examining the impacts of residential self-selection on travel behavior: a focus on methodologies. Trans. Res. Part B: Methodol. 42 (3), 204–228. Naess, P., 2012. Urban form and travel behavior: experience from a Nordic context. J. Transp. Land Use 5 (2), 21–45. https://doi.org/10.5198/jtlu.v5i2.314. Nasri, A., Zhang, L., 2012. Impact of metropolitan-level built environment on travel behavior. Transp. Res. Record: Journal of the Transp. Res. Board 2323, 75–79. https://doi.org/10.3141/2323-09. Norusis, M.J., 2008. SPSS 16.0 Guide to Data Analysis, second ed. Prentice-Hall, Upper Saddle River. Ogra, A., Ndebele, R., 2014. The role of 6Ds: density, diversity, design, destination, distance, and demand management in transit oriented development (TOD). In: NeoInternational Conference on Habitable Environments, (May 2013), pp. 539–546. Park, K., Ewing, R., Scheer, B.C., Ara Khan, S.S., 2018. Travel behavior in TODs vs. non-TODs: using cluster analysis and propensity score matching. Transp. Res. Rec. https://doi.org/10.1177/0361198118774159. Patterson, Z., Ewing, G., Haider, M., 2005. Gender-based analysis of work trip mode choice of commuters in suburban Montreal, Canada, with stated preference data. Transp. Res. Rec. https://doi.org/10.3141/1924-11. Pongprasert, P., Kubota, H., 2019. TOD residents’ attitudes toward walking to transit station: a case study of transit-oriented developments (TODs) in Bangkok, Thailand. J. Mod. Transp. https://doi.org/10.1007/s40534-018-0170-1. Renne, J.L., 2009. Evaluating Transit-Oriented Development Using a Sustainability Framework: Lessons from Perth’s Network City. Planning Sustainable Communities: Diversity of Approaches and Implementation Challenges, pp. 115–148. Retrieved from http://www.vtpi.org/renne_tod.pdf. Reusser, D.E., Loukopoulos, P., Stauffacher, M., Scholz, R.W., 2008. Classifying railway stations for sustainable transitions - balancing node and place functions. J. Transp. Geogr. 16 (3), 191–202. https://doi.org/10.1016/j.jtrangeo.2007.05.004. Rodriguez, D.A., Vergel-Tovar, E., Camargo, W.F., 2016. Land development impacts of BRT in a sample of stops in Quito and Bogotá. Transp. Policy. https://doi.org/ 10.1016/j.tranpol.2015.10.002. Salat, S., Ollivier, G., 2017. Transforming the urban space through transit-oriented development. In: Transforming the Urban Space through Transit-Oriented Development, https://doi.org/10.1596/26405. Sardari, R., Hamidi, S., Pouladi, R., 2018. Effects of traffic congestion on vehicle miles traveled. Transp. Res. Rec. https://doi.org/10.1177/0361198118791865. Schlossberg, M., Brown, N., 2004. Comparing transit-oriented development sites by walkability indicators. Transp. Res. Record: J. Transp. Res. Board 1887 (1887), 34–42. https://doi.org/10.3141/1887-05. Schwanen, T., Mokhtarian, P.L., 2004. The extent and determinants of dissonance between actual and preferred residential neighborhood type. Environ. Plan. B: Plan. Des. 31 (5), 759–784. https://doi.org/10.1068/b3039. Singh, Y.J., Lukman, A., Flacke, J., Zuidgeest, M., Van Maarseveen, M.F.A.M., 2017. Measuring TOD around transit nodes - towards TOD policy. Transp. Policy 56 (February), 96–111. https://doi.org/10.1016/j.tranpol.2017.03.013. Song, Y., Knaap, G.-J., 2007. Quantitative classification of neighborhoods: the neighborhoods of new single-family homes in the Portland metropolitan area. J. Urban Des. 12 (1), 1–24. https://doi.org/10.1080/13574800601072640. Sung, H., Oh, J.T., 2011. Transit-oriented development in a high-density city: identifying its association with transit ridership in Seoul, Korea. Cities 28 (1), 70–82. https://doi.org/10.1016/j.cities.2010.09.004. Suzuki, H., Cervero, R., Iuchi, K., 2013. Transforming Cities with Transit. Transit and Land-Use integration for sustainable urban development. https://doi.org/10. 1596/978-0-8213-9745-9. Suzuki, H., Murakami, J., Hong, Y.-H., Tamayose, B., 2015. Financing transit-oriented development with land values: adapting land value capture in developing countries. In: Financing Transit-Oriented Development with Land Values: Adapting Land Value Capture in Developing Countries, https://doi.org/10.1596/978-14648-0149-5. Thomas, R., Bertolini, L., 2015. Policy transfer among planners in transit-oriented development. Town Plan. Rev. 86 (5), 537–560. https://doi.org/10.3828/tpr. 2015.32. Thomas, R., Pojani, D., Lenferink, S., Bertolini, L., Stead, D., van der Krabben, E., 2018. Is transit-oriented development (TOD) an internationally transferable policy concept? Reg. Stud. 1–13. https://doi.org/10.1080/00343404.2018.1428740. UTTIPEC, 2013. Transit-Oriented Development. Policy. Norms. Guidelines. United Traffic and Transportation Infrastructure (Planning and Engineering) Centre (UTTIPEC). Delhi Development Authority (DDA). http://uttipec.nic.in/writereaddata/linkimages/5320760375.pdf. Vale, D.S., 2015. Transit-oriented development, integration of land use and transport, and pedestrian accessibility: Combining node-place model with pedestrian shed ratio to evaluate and classify station areas in Lisbon. J. Transp. Geogr. 45 (November), 70–80. https://doi.org/10.1016/j.jtrangeo.2015.04.009. Vance, C., Lovanna, R., 2007. Gender and the automobile: analysis of nonwork service trips. Transp. Res. Rec. https://doi.org/10.3141/2013-08. Voulgaris, C.T., Taylor, B.D., Blumenberg, E., Brown, A., Ralph, K., 2016. Synergistic neighborhood relationships with travel behavior: an analysis of travel in 30,000 US neighborhoods. J. Transp. Land Use 10 (1), 437–461. https://doi.org/10.5198/jtlu.2016.840. World Resources Institute (WRI), 2018. Transit-oriented development manual. Delhi TOD policy and regulations interpretation. WRI India. https://www.wricitieshub. org/sites/default/files/Delhi%20TOD%20Policy%20Manual_0.pdf. Wu, W., Hong, J., 2017. Does public transit improvement affect commuting behavior in Beijing, China? A spatial multilevel approach. Transp. Res. Part D: Transp. Environ. https://doi.org/10.1016/j.trd.2016.08.032. Zegras, C., 2010. The built environment and motor vehicle ownership and use: evidence from Santiago de Chile. Urban Stud. 47 (8), 1793–1817. https://doi.org/10. 1177/0042098009356125. Zemp, S., Stauffacher, M., Lang, D.J., Scholz, R.W., 2011. Classifying railway stations for strategic transport and land use planning: context matters!. J. Transp. Geogr. 19 (4), 670–679. https://doi.org/10.1016/j.jtrangeo.2010.08.008. Zhang, L., Nasri, A., Hong, J.H., Shen, Q., 2012. How built environment affects travel behavior: a comparative analysis of the connections between land use and vehicle miles traveled in US cities. J. Transp. Land Use. https://doi.org/10.5198/jtlu.v5i3.266.

20