Smart and Connected Transport - A Case Study of Delhi
A typology of urban development around bus rapid transit stops in Indore and Ahmedabad India final version (1)
1. TRB 2015 Annual Meeting Paper revised from original submittal 1
A typology of urban development around Bus Rapid Transit (BRT) stops in Indore and
Ahmedabad, India
Paper submitted for presentation only at the 94th Annual TRB Meeting
ABE90 Transportation in the Developing Countries
Erik VERGEL-TOVAR, M.Sc.
PhD Candidate
Department of City and Regional Planning
The University of North Carolina at Chapel Hill
evergel@live.unc.edu
Word count: 6444
Tables: 6
2. TRB 2015 Annual Meeting Paper revised from original submittal 2
Abstract
Despite the rapid dissemination of Bus Rapid Transit BRT systems in Latin America and Asia,
little is known about the type of urban development around stops of this mass transportation
system. This paper expands the methodology applied in Latin America to India in order to
identify a BRT typology based on built environment attributes collected around BRT stops
within a buffer area of 250 meters. A typology of urban development around BRT stops from a
sample of 33 BRT stops in Indore and Ahmedabad is developed based on factor and cluster
analysis using built environment characteristics and geographic information systems data. Some
BRT types identified in this study reflect the high-density environment characteristic of larger
Indian cities, while other types show the presence of slums in conjunction with commercial land
uses within a non-motorized transport urban environment. Other types identified suggest the
emergence of high-rise developments in close proximity to BRT stops with a mixture of land
uses, including high levels of parking. This paper seeks to inform policy makers, planners and
communities living in close proximity to BRT corridors regarding data collection techniques in
data-poor areas as well as the orientation of urban development towards bus rapid transit in
India.
Keywords: Bus Rapid Transit BRT, urban development, typology, transit-oriented development
TOD, India.
3. TRB 2015 Annual Meeting Paper revised from original submittal 3
A typology of urban development around Bus Rapid Transit (BRT) stops in Indore and
Ahmedabad, India
1. Introduction
Bus Rapid Transit (BRT) is a cost-effective mass transportation system characterized by
exclusive bus lanes and reduction of travel times, high-passenger capacity and level boarding,
and a relatively short construction process. BRT systems have bus stops and terminals along
main transportation corridors where passengers can shift transportation modes or take feeder
routes that extend the service into surrounding neighborhoods (1). Currently, more than 180
cities in the world are implementing BRT systems mobilizing more than 31 million passengers
per day (2). The introduction of BRT systems has raised several questions regarding the BRT’s
city-shaping effects and its impacts on urban form (3).
As a mass transportation system built on the surface of cities, the conditions and factors that
explain the extent by which the introduction of BRT systems can generate land use changes and
urban development or redevelopment processes capture the attention of city and transportation
planners, developers and communities living in close proximity to BRT corridors. Within the
range of massive transportation investments, BRT systems have been seen by some local actors
as temporary investments without the capacity to generate transit-oriented development (TOD)
features along the BRT corridors and especially around BRT stops. Despite the remarkable
experience of Curitiba in the development of high density along BRT corridors, little is known
about the extent BRT systems can generate TOD (4).
Transit oriented development (TOD) is understood as an urban form with a mix of land uses with
various densities in close proximity to transit stops. TOD is also characterize by five goals:
location efficiency, rich mix of choices, value capture, place making and urban design (more
than a transit node, a public transportation stop can be a place) (5). The present study expands to
India the methodology developed in order to identify urban development typologies in seven
cities in Latin America with BRT systems under operation (6). The methodology was adjusted to
local characteristics by introducing new variables such as average block size, presence of
rickshaws and street vendors, presence of slums, parking on sidewalks, presence of sidewalks
and accessibility to the BRT stop within the buffer area.
The paper is structured in five sections. First, a literature review on the relationship between
BRT and urban development is followed by a description of transit typologies in relation to TOD
features. Second, the methodology developed is described including the list of variables and data
manipulation. Third, results are presented focusing on the BRT typology identified based on the
33 BRT stops studied. Fourth, a discussion regarding the BRT typologies identified in Indore and
Ahmedabad is developed looking at the level of transit orientation according to the built
environment factors and variables. Fifth, the conclusion includes a comparison of some TOD
features between the BRT typology identified in this study in relation to the typology identified
by previous research in Latin America. The main findings and suggestions for further research
also frame the conclusions.
4. TRB 2015 Annual Meeting Paper revised from original submittal 4
2. Literature Review
2.1. BRT and urban development
Coordination between urban transportation and land use is one of the challenges faced by cities
experiencing accelerated urban growth. Rapid urbanization and fast motorization trends further
complicate these challenges in developing countries (7). This conundrum is exemplified by the
marked growth that characterizes many cities across Asia. The rate of urbanization in India has
increased from 17% in 1950 to 30.9% in 2010 and is expected to increase up to 51.7% by 2050
(8). Motorization trends in India demonstrate fast gains, not only in total number of two-wheelers
(from 3 million in 1981 to 42 million in 2002), but also in car ownership: from 4 cars per 1000
people in 1991 to more than 7 cars per 1000 people in 2002 (9).
The experience of cities in Latin America and Asia by introducing BRT systems have shown the
improvement of transportation conditions for commuters and the reduction of air pollution and
traffic accidents with a relatively lower capital investment in comparison to rail-based
transportation systems (10). The introduction of Bus Rapid Transit (BRT) systems in India can
play a significant role in the improvement of not only the provision of adequate urban public
transport but also in the shift of transportation modes along high transportation demand corridors
in some cities in India. In 2008, New Delhi introduced a pilot BRT corridor. Since 2009,
Ahmedabad, Indore, Jaipur and Pune started the introduction of BRT systems (11). More
recently Bhopal and Rajkot started the introduction of BRT systems. the scope of the
intervention on the built environment by introducing BRT systems in Indian cities it is unclear
given the focus on transport infrastructure while it is expected that other BRT elements will be
provided by public-private partnerships (12).
Due to the rapid expansion of BRT systems worldwide; especially in the developing world, the
potential effects by this type of mass transport system on land development is considered the
“mode to watch” in the near future in terms of its capacity to guide urban development (13). The
relationship between BRT and urban development has been studied by looking at the
associations between BRT systems and property values with some findings suggesting property
price increments in close proximity to BRT corridors in Bogotá (Colombia) and others finding
with no impacts on property values as a result of the announcement of a new BRT corridor in
Ecatepec (Mexico) (14-18). Some studies have examined the relationship between BRT systems
and urban development with findings suggesting increments on urban density in Bogotá
(Colombia), the existence of physical barriers in order to access BRT stops in Jinan (China), land
use changes after the introduction of BRT corridors in Seoul (Korea), and concentration of land
development and redevelopment activities in close proximity to BRT corridors and stops in
Curitiba (Brazil), Bogotá (Colombia) and Quito (Ecuador) (19-22).
Empirical evidence on BRT’s effects on urban development, redevelopment, and land use
change is still limited. The capacity of BRT systems to promote TOD is still an open debate
characterized by some skepticism (23). Even though the emergence of these studies, the
literature is still limited about the city-shaping effects of BRT systems and the differences among
the results by previous studies suggests further research is needed (24).
5. TRB 2015 Annual Meeting Paper revised from original submittal 5
2.2. Transit-oriented development –TOD typologies
Identifying TOD typologies constitutes an important element in urban transportation planning,
especially for decision makers and planners, in terms of providing a set of urban environments
that can take place around transit stops in order to guide urban development and guide place
making dynamics. Some benefits of developing TOD typologies provides advantages such as
determining development potentials, mixture of land uses, density, infrastructure standards, and
TOD performance assessment which facilitates comparisons and benchmarking between groups
and across transit stop types (25).
Several approaches have been developed in order to identify TOD typologies. The envision
approach suggested by planners and architects aims to previously determine what type of urban
development is desirable around transit stops or to implement one type of transit stop (down
town, neighborhood, suburban) in order to promote certain developments according to the
location within urban areas (26). The node-place approach seeks to explore development and
redevelopment opportunities by looking at the level of intensification of certain urban activities
around transit station areas (27). The performance approach developed mainly for the North
American car-oriented context, generates TOD types by looking at the travel behavior variables
such as vehicles miles travel (VMT) by residents in transit zones, levels and interactions between
residential and employment activities in terms of percentages of workers in the transit zone (28).
Emerging studies identifying TOD typologies have been developing empirically based
approaches looking at different attributes around metrorail and bused-based transit stops. Based
on land use and development data, 5 types of transit stops were identified in Hong Kong (29) by
looking at building area, development scale area, density and mixture of attributes within a
buffer area from 200m to 500m. 5 types of station areas based on built environment and
socioeconomic data were identified along the light rail corridor in Phoenix (Arizona) within one
mile buffer area (30). In Latin America, 10 typologies of urban development around BRT stops
based on built environment, population density and spatial location data collected in seven cities
(6) within a buffer area of 250m for single stops and 500m for BRT terminals. In Brisbane
(Australia), 4 neighborhood typologies were identified based on the density of built environment
indicators (employment, residential, land use, road type and intersections) and survey data of
residents living within a buffer area of 800m from transit corridors and within census collection
districts (CCD) overlapped with transit corridors (25).
TOD typologies have been defined based on the future vision of planners and architects or based
on empirical studies looking at built environment attributes around transit stops. Both approaches
have built environment variables in common such as land development, land uses, residential
and employment activity, density, population density, building area, mixture of attributes and
location (downtown, urban, neighborhood, suburban). Socioeconomic characteristics have been
introduced in emerging studies looking at travel behavior and TOD across typologies. The
identification of typologies has been developed mainly based on built environment attributes and
the introduction of socioeconomic variables constitutes an important step in the identification of
typologies and travel behavior studies. The identification of BRT typologies has been conducted
in Latin America(6). Little is known about typologies of BRT stops in other regions of the world.
The identification of BRT typologies and identification of TOD features based on local
characteristics contributes to identify opportunities to promote TOD around BRT stops.
6. TRB 2015 Annual Meeting Paper revised from original submittal 6
3. Methodology
The present study relies on primary and secondary data collected in Indore and Ahmedabad
during fieldwork visits from June to August of 2013. Both cities are implementing BRT systems
but at different stages, while Ahmedabad started its first BRT corridor in 2009 and is
constructing a second corridor, Indore started operations of a pilot BRT corridor in the first half
of 2013. These two cities were selected according to the following criteria: the BRT system
should be in operation; the cities are medium or large cities; it would be feasible to undertake
primary data collection due to the presence of local contacts; secondary data is available; the
systems provide most of the features of a full BRT system such as exclusive bus lanes, reduction
of travel times, high-passenger capacity and level boarding. The BRT stops were selected in
consultation with local transportation planners based on the criteria to identify a sample of stops
that is heterogeneous in terms of the characteristics of the built environment around them.
3.1. Study areas
Indore, Madhya Pradesh
Indore is located in the state of Madhya Pradesh in the center of India. It is the largest city in this
state with an urban population of 2,367,447 inhabitants and a density of 3,727 hab/ km2
. Indore
is the core of a metropolitan region with 3,272,335 inhabitants. Indore introduced a BRT system
known as “iBus” in the first semester of 2013. The BRT corridor denominated “pilot” has an
extension of 11 km and mobilizes 22,200 passengers per day (2). The BRT system in Indore has
in total 21 BRT stops and 12 BRT stops were selected for this study.
Ahmedabad, Gujarat
Ahmedabad is located in the state of Gujarat at the north west of India and it. Ahmedabad is the
fifth largest city in India with an urban population of 5,570,585 inhabitants and a density of
12,005 hab/ km2
. It is the core of a metropolitan area of 6,240,201 inhabitants. Ahmedabad
introduced a BRT system known as “Janmarg” in 2009. The first BRT corridor currently under
operation has a total length of 39km and mobilizes 130,000 passengers per day (2). The BRT
system in Ahmedabad has in total 109 BRT stops. A total of 21 BRT stops were selected for the
present study.
3.2. Data collection
The data collection was developed in two stages. First, BRT stops were georeferenced by using
Google Earth and geographic information system (GIS). A buffer of 250 meters was determined
for all BRT stops on GIS. BRT stop maps were developed for each BRT stop identifying blocks
and segments within the buffer area. A block consists on a polygon with several attached land
parcels or public spaces and it is framed by segments. A segment is the side of one block that
goes from one intersection or corner to another as part of continuum of constructions, vacant
land or public spaces. Second, the author conducted fieldwork visits to all BRT stops selected
for this study. The fieldwork visits allowed the author to update the shape of blocks not capture
by Google Earth. Built environment data was collected at the segment and block level by using
an audit tool adapted from the Pedestrian Environment Data Scan (PEDS)1
. Table 1 shows the
total number of segments and blocks examined per city. The total number of segments studied in
both cities is 4,744 which are part of 1,020 blocks.
1
The Pedestrian Environment Data Scan (PEDS) Tool http://planningandactivity.unc.edu/RP1.htm
7. TRB 2015 Annual Meeting Paper revised from original submittal 7
3.3. Data Manipulation
The unit of analysis for this study is the BRT stop buffer area. Thus, all variables were
aggregated at the BRT stop level. At the stop level, distance in kilometers to the closest activity
node or central business district (CBD) was calculated for each BRT stop. In Indore, the CBD
was determined as the area around the Railway Station by measuring the distance along the
Mahatma Gandhi Road. In Ahmedabad, the CBD was identified by selecting the BRT Stop
“Lokmanya Tilak Bag”, a stop located in the Historical Center of the city. Segment density was
calculated based on the total number of segments in the BRT stop buffer gross area (total
segments/area 0.19635 sq-km). Population density was calculated based on the percentage share
of each block within the ward area (census tracts in India), then all population data was
aggregated at the BRT stop level and population density was calculated based on the BRT stop
buffer gross area in hectares (19.635 Ha).
At the block level, nine variables were calculated by counting the presence of facilities and
public spaces and dividing them by the BRT stop gross area (0.19635 sq-km). Public facility
index measures the presence of facilities from zero to seven, excluding the presence of big-box
developments (Malls). Public facility density measures the sum of facilities present in the BRT
stop per gross area. BRT-oriented facility index refers to the presence of supportive facilities for
the BRT system such as hospitals, libraries, markets/squares, temples, schools and others (i.e.
universities and transport hubs). BRT-oriented facility density was calculated by the sum of BRT
supportive facilities per gross area. Green area’s density, park density were calculated by the
presence of parks, squares, pocket squares, green areas and boulevards per gross area. Non-
motorized transport (NMT) friendliness measures the presence of parks, squares, pocket squares,
boulevards, pedestrian segments, pedestrian bridges, bike-paths per gross area. Rickshaws/street
vendor’s density was calculated by counting their presence per gross area. Average block size
was calculated in square meters on GIS based on the block size within the buffer area.
At the segment level, thirty seven variables were calculated. Land use index measures the
presence the ten types of land uses (institutional, industrial, commercial, mixed-commercial,
single residential, multifamily residential, mixed industrial commercial, mixed commercial
residential, vacant and open green area). The intensity of land uses was calculated by the
percentage of segments with each land use type in relation to the total number of segments. The
density of BRT supportive land uses (mixed commercial, single and multifamily residential and
institutional) was calculated by summing these types of land uses and dividing their presence per
gross area. BRT unsupportive land uses (industrial, industrial commercial and vacant) was
calculated by summing these types of land uses and dividing their presence per gross area. The
variables measuring building heights, vertical urban density, the development level, the condition
and maintenance of constructions were calculated by the percentage segment including each
variable in the relation to the total number of segments.
The presence of slums was calculated by the percentage of segments with this housing typology.
The intensity of parking was calculated by the percentage of segments with on-street parking,
off-street parking and parking on sidewalks. Commercial land uses and parking (on-street
parking, off-street parking, parking on sidewalks) was calculated in relation to the total number
of segments. Entropy was calculated by using the formula developed by Cervero and Kockelman
8. TRB 2015 Annual Meeting Paper revised from original submittal 8
(31) in order to evaluate the evenness in the distribution of BRT-oriented land uses (mixed-
institutional, commercial, single and multifamily residential).
3.4. Data Analysis
Two data reduction strategies were developed given that the number of variables exceeds the
number of observations. First, 24 variables were selected for the exploratory factor analysis
(EFA) after excluding variables that were not adding new information to the analysis because
they were perfectly predicted with other variables within the same category (acting as dummy
variables without excluded category) or they were already included within other variables. The
10 land use types were excluded as raw variables because they were already included in other
variables such as land use index, BRT-oriented land uses, BRT unsupportive land uses, green
areas’ density, park density, NMT friendliness, Vacant & BRT. The low and medium variables
within categories were excluded because they are perfectly predicted by the high values on each
category with the exception of low building height (a variable with high variation across BRT
stops). The no-height variable was excluded because it is perfectly predicted with vacant land
use. The high values for building heights (high-rise), urban density and development level were
included in the analysis because they are related to TOD features. The three parking categories
(on-street, off-street and sidewalk) were not included in the factor analysis because they were
already included in the commercial & parking variable. The 24 variables were tested to confirm
there was no intercorrelation or collinearity between them.
Second, the study develops an exploratory factor analysis (EFA) in order to subdivide the group
of variables (24) into a smaller group of factors where the covariance between variables is high
but the covariance between groups is low. EFA relies exclusively on the correlation between
variables where the weight between factors summarizes the correlation (32). The exploratory
factor analysis treated the variables as continuous with the aim to explore the structure of the
data by identifying the unobservable variables or factors.
Based on the factors identified in the previous step, a hierarchical cluster analysis was developed
by using the factor scores in order to establish groups of BRT stops that are more alike according
to the built environment factors. The cluster analysis seeks to group BRT stops that are more
alike within each cluster, but at the same time, it separates BRT stops based on the heterogeneity
according to the factor scores (33). Ward’s linkage cluster was used in order to identify the
groups of BRT stops. The number of clusters was determined by identifying in the Duda-Hart
stopping rule table the largest (Je(2)/Je(1) values in the same rows of the lowest pseudo-T-
squared (which have the larger T-squared values next to them) (34).
4. Results
Descriptive statistics of all built environment variables are shown in Table 2 for each city. The
density of non-motorized transport (NMT) infrastructure is higher in the sample of stops in
Ahmedabad, the mean values are statistically different between both cities, with a high variation
across stops in both cities (mean 195.23 and std. dev. 185.51 in Ahmedabad; mean 86.15 and std.
dev. 37.71 in Indore). The number of segments facing the BRT corridor within the buffer area of
the sample of stops is higher in Ahmedabad, the mean values are statistically different between
both cities (mean 15.81 and std. dev. 8.72 in Ahmedabad; mean 9.75 and std. dev. 2.26 in
Indore).
9. TRB 2015 Annual Meeting Paper revised from original submittal 9
The density of BRT-oriented land uses (commercial, residential and institutional) is higher in the
sample of BRT stops in Ahmedabad (mean 0.83 and std. dev. 0.24) than in the sample of stops in
Indore (mean 0.62 and std. dev. 0.20), the mean values are statistically different. The results are
the opposite in terms of the density of BRT unsupportive land uses (industrial, industrial-
commercial and vacant) with a higher density in the sample of BRT stops in Indore (mean 0.48
and std. dev. 0.18) than in Ahmedabad (mean 0.26 and std. dev. 0.26). The mean values of
density of BRT unsupportive land uses are statistically different but there is a high variation
across stops Ahmedabad. The level of mixture of land uses in terms of entropy (evenness in the
distribution of land uses) is similar between both samples (mean 0.60 and std. dev. 0.17 in
Ahmedabad and mean 0.64 and std. dev. 0.13 in Indore).
Land development and consolidation differs in the sample of BRT stops between both cities. The
percentage of segments with undeveloped parcels (no height) is different with a higher
percentage in Indore (mean 0.36 and std. dev. 0.16) than in Ahmedabad (mean 0.16 and std. dev.
0.20), the mean values are statistically different. However, the variation in Ahmedabad is high
suggesting some BRT stops have several segments with undeveloped land parcels. This
difference between cities is confirmed by the percentage of segments with vacant land given that
it is higher in Indore than in Ahmedabad. The mean values are statistically different between
both cities (mean 0.14 and std. dev. 0.21 in Ahmedabad; mean 0.30 and std. dev. 0.12 in Indore).
4.1. Factor analysis results
Factor analysis was run with the 24 variables shown in table 3. The measure of sampling
adequacy for this factor analysis was adequate2
. The exploratory factor analysis scree test
suggested the retention of 6 factors based on the eigenvalues criteria which consists on keeping
the number of factors scoring ≥1.00 (32). All variables loaded > |0.40| on at least one factor with
the exception of high-rise are shown in Table 4. The six factors account for 84.06% of the
variance of all 24 variables. Even though the Cronbach’s alpha for factors 5 and 6 are low, the
average standardized Cronbach’s alpha value for all factors is 0.77, within a range from 0.55 to
0.96.
- Factor 1, “BRT-oriented development with facilities in a high density built-up area with
mixed land uses”: is characterized by the high presence of facilities. This factor is also
characterized by a high urban density environment and a mixture of land uses.
- Factor 2, “Slums NMT friendly, low height and connected”: is characterized by a high
concentration of non-motorized transport infrastructure, small blocks and high connectivity.
This factor represents the presence of informal settlements with low building heights.
- Factor 3, “BRT-OD land uses, non-core consolidated and connected”: is characterized by
land uses oriented towards the BRT in non-core urban areas with segments provided with
urban infrastructure and high connectivity.
2
Bartlett test of sphericity, chi-square = 613.34; p-value = 0.000; H0: variables are not intercorrelated. Kaiser-Meyer-Olkin
Measure of Sampling Adequacy (KMO) = 0.514
10. TRB 2015 Annual Meeting Paper revised from original submittal 10
- Factor 4, “Green spaces and parks”: represents the concentration of green urban spaces and
parks.
- Factor 5, “Vacant on BRT, unfilled and mixed land uses”: is characterized by the presence of
vacant land along the BRT corridor, low population density but with some mixture of land
uses.
- Factor 6, “Rickshaws, urban core high density areas”: represents the concentration of
rickshaws in a high density urban environment at urban core areas with low mixture of land
uses.
4.2. Cluster analysis results
The cluster analysis was run with the six built environment factors identified in the EFA and the
high-rise development variable (standardized), which is the only variable that did not load in the
EFA. Three types of hierarchical cluster analysis (average-linkage, single-linkage and ward-
linkage) were run in order to identify the most suitable for data grouping of BRT stops.
First, it was run the average-linkage or agglomerative cluster analysis, which is based on the
average of all distances between the elements of each cluster and then taking the mean distance
between them. This cluster analysis suggested 6 and 8 groups according to the Calinski-Harabasz
and Duda tests3
(33). Second, it was run the single-linkage or bottom-up cluster analysis, which
is based on the nearest neighbor clustering of single elements within each cluster, process in
which each cluster grows by adding elements. This cluster analysis suggested 8 groups according
to the Calinski-Harabasz and Duda tests (33). Third, it was run a ward linkage or agglomerative
cluster analysis which seeks to minimize the total within-cluster variance. This cluster analysis
suggested 8 and 12 groups according to the Calinski-Harabasz and Duda tests (33). After
comparing the results of these three cluster analyses, the 12 groups cluster analysis (ward-
linkage) was identified as the most suitable for the present study given the similarity between the
stops within each cluster in terms of regional geography (distance to CBD), built environment
attributes and population density. The number of BRT stops and the mean values per cluster are
shown in Table 5.
5. Discussion
12 BRT typologies were identified from a sample of 33 BRT stops in Indore and Ahmedabad,
two cities that have been introducing recently BRT systems. Table 6 shows the 12 BRT
typologies identified including 7 built environment factors and 7 key TOD features variables.
The emergence of these BRT systems implies this BRT typology might change in the future if
some land development and redevelopment changes occur after the introduction of the BRT. The
BRT typology constitutes a useful base line for local transportation and land use planners as well
as decision makers for current and future BRT corridors. The 12 BRT typologies identified in the
present study are described below:
1. Noncore commercial residential: represents an urban environment far from activity nodes
with low presence of NMT infrastructure with a mixture of land uses but mainly commercial
3
Distinct clustering is characterized by large Calinski–Harabasz pseudo-F values, large Duda–Hart Je(2)/Je(1)
values, and small Duda–Hart pseudo-T-squared values.
11. TRB 2015 Annual Meeting Paper revised from original submittal 11
linked to parking and next to vacant land. This typology includes eight BRT stops, seven
from Indore and one from Ahmedabad (“Shivranjani”), and is characterized by the presence
of some commercial land developments of five or more floors linked to parking and some
green areas and parks.
2. High-rise and dense consolidated node: is characterized by high-rise developments in urban
areas with high population density, lack of NMT infrastructure including some vacant land
and parking. This typology is characterized by BRT stops centrally located with high
concentration of population and presence of rickshaws. This typology is represented by two
BRT stops from Indore (“Industry House” and “Palasia”) with presence of land
developments of five or more floors.
3. Institutional activity node pedestrian friendly: represents stops with presence of facilities
linked by NMT infrastructure within a mixed land use urban environment, especially
institutional without vacant land available. This typology includes two BRT stops
(“Lokmanya Tilak Bag” located in the old city of Ahmedabad; “Shivaji Vatika” in Indore)
with high presence of facilities, green areas and parks and mixed land uses with a significant
presence of non-motorized transport infrastructure, but high levels of parking associated to
commercial land uses.
4. Noncore pedestrian friendly commercial-residential: is a unique type represented by
“Shastrinagar” BRT stop in Ahmedabad characterized by a low mixture of land uses, mainly
commercial and residential connected with NMT infrastructure. This is a unique type
represented by BRT stop “Shastrinagar” in Ahmedabad. This stop is located far from the
CBD with presence of green areas and parks linked by a high concentration of segments and
non-motorized transport infrastructure, especially parks and BRT-oriented land uses.
5. Noncore industrial-commercial: is characterized by the presence of commercial land uses
with some industrial developments where there are high levels of parking within a high
density urban environment. This typology includes two BRT stops from Ahmedabad (“Soni
Ni Chali” and “Gurudwara”) which are characterized by the presence of slums; pedestrian
streets and low building heights with high levels of population density. This typology shows
a high concentration of commercial land uses associated to parking.
6. Noncore industrial: is a unique type represented by “Narol” BRT stop which represents the
industrial urban environment at the Southeast of Ahmedabad characterized by the lack of
NMT infrastructure and some levels of parking associated to commercial-industrial land
uses. This typology is represented by “Narol” BRT stop located in the industrial area of
Ahmedabad characterized by some presence of commercial and parking.
7. High-rise exclusively commercial node: represents an urban environment without NMT
infrastructure with presence of car-oriented commercial developments and some vacant land
as a result of redevelopment dynamics. This typology is represented by two BRT stops from
Indore (“MR-9” and “Vijay Nagar”) with a significant presence of commercial land uses
associated to parking and high rise redevelopments but some presence of vacant land as a
result of redevelopment dynamics.
12. TRB 2015 Annual Meeting Paper revised from original submittal 12
8. Facilities and transfer-node pedestrian friendly: is characterized by a high mixed of land
uses with presence of facilities with considerable levels of parking mostly associated to
commercial land uses. This typology is represented by two BRT stops with a high
concentration of facilities such as “Maninagar Railway Station” in Ahmedabad and the Post
Office in Indore, which are centrally located with presence of several facilities, BRT oriented
land uses and access to other transportation modes.
9. High-rise commercial-residential consolidated: represents a high density and consolidated
urban environment with mixed land uses and presence of some facilities and parks. This
typology is represented by two BRT stops in Ahmedabad (“Kankaria Lake” and “Anjali”)
with a significant presence of facilities such as theaters or public buildings, mixed land uses
with transit orientation.
10. High-dense commercial residential: is characterized by a consolidated and connected urban
environment with high-rise residential developments with commercial land uses at the street
level. This typology includes four BRT stops from Ahmedabad that are characterized by land
uses oriented towards the BRT with a high concentration of the population in a well-
connected urban environment.
11. Satellite commercial & slum node: is characterized by the presence of slums in a NMT
environment with several pedestrian streets with high levels of connectivity. This typology
includes four stops from Ahmedabad that are characterized by a high concentration of slums
in an urban environment with high connectivity and several pedestrian streets.
12. Urban expansion areas: is characterized by urban expansion areas far from activity nodes
where there is still presence of large vacant land parcels without NMT infrastructure and
some presence of car-oriented commercial developments. This typology is represented by
two stops from Ahmedabad (“Jayantial Park” and “ISKON Mandir”) located at urban
expansion areas with a significant presence of vacant land where new developments are
taking place.
The present study has some limitations. The number of observations (33) is lower than the total
number of built environment variables (50) collected during the fieldwork visits, so that a data
reduction strategy was implemented in order to run the factor analysis. With an increase in the
number of observations (including additional BRT stops from other cities in India) this study
could identify built environment factors based on a richer data set. This study cannot claim
causality after the introduction of the BRT system, in other terms; it cannot be argued that the
BRT typology is a result of the introduction of this mass transportation system. This study also
has the limitation of not including socioeconomic characteristics of population living around
these BRT stops.
6. Conclusion
The concept of TOD in the context of Indian cities is still under discussion and the present study
seeks to contribute to this debate. The variation of TOD features such as NMT infrastructure,
13. TRB 2015 Annual Meeting Paper revised from original submittal 13
entropy, vacant land, commercial and parking among the 12 BRT types identified in this study
suggests the high density and mixture of land uses characterizing Indian cities is not
homogeneous at the BRT stop level. BRT typologies 4, 5, 6 and 7 represent urban development
environments around stops without an even distribution of commercial, residential and
institutional land uses (entropy). The presence of vacant land is very low with the exception of
BRT typologies 2, 7 and 12, suggesting that these BRT types provide local planners an
opportunity to promote land developments oriented towards an investment in transit. BRT
typology 4 has a high transit orientation as a result of positive mean values in factors 3 and 4 as
well as on TOD features such as NMT, segment density, BRT oriented land uses and population
density. BRT typology 8 also has a high transit orientation given the positive mean values on
factors 1 and 3 as well as on TOD features such as entropy, NMT, segment density, and BRT
oriented land uses.
The BRT typology identified in this study captures city-specific factors. BRT typologies 2 and 7
are represented by BRT stops from Indore and BRT types 4, 5, 6, 9, 10, 11 and 12 include BRT
stops exclusively from Ahmedabad. Only BRT typologies 1, 3, and 8 are applicable to both
cities. This finding suggests that while there are differences between BRT corridors, there are
also local characteristics specific for Indore and Ahmedabad with major implications upon
planning. Further research is recommended by increasing the sample of BRT stops from other
cities in India in order to test hypotheses related to the similarity between BRT corridors and
stops to confirm the level of heterogeneity of the urban environment around BRT stops that
seems to be the pattern for cities in India.
In relation to the BRT typologies identified in Latin America by previous research (6), this study
suggests there is a higher mixture of land uses in India (mean 0.61, n=33) than in Latin America
(mean 0.58, n=82) measured in terms of entropy at the BRT stop level. The presence of NMT
infrastructure is also higher in India (mean 155.5, n=33) than in Latin America (mean 57.06,
n=82) as a result of the high presence of slums in close proximity to BRT stops, especially in
Ahmedabad. The higher number of typologies in India (12) than in Latin America (10), despite
having a smaller sample of BRT stops (33 and 82 respectively), suggests there is a higher level
of heterogeneity of the built environment around BRT stops in India. Both regions differ in terms
of parking (on-street and off-street), the sample of BRT stops in Latin America (mean 0.30
n=82) shows higher levels of parking than the sample of BRT stops in India (mean 0.16, n=33).
This study identified built environment factors and variables in order to assess the level of transit
orientation of BRT types. Three built environment factors (1, 3 and 4) and 5 variables (entropy,
NMT, segment density, BRT-oriented land uses and population density) can be used to measure
the TOD performance of BRT stops based on local characteristics. The urban development
environment identified in BRT typologies 4 and 8 suggests a favorable environment for TOD if
some changes occur in those factors and variables that registered negative mean values. For
instance, local planners could increase the level of transit orientation in these BRT types by
encouraging the presence of facilities (factor 1) in BRT type 4 and promoting green areas and
parks (factor 4) in BRT type 8. This study recommends that local planners and communities in
Indore and Ahmedabad continue the assessment of TOD performance of BRT stops by
continuing the same measurement procedures developed in this study.
14. TRB 2015 Annual Meeting Paper revised from original submittal 14
The lack of current land-use data and information about built environment features where BRT
corridors will be implemented or have been introduced implies several challenges to decision
makers and planners. The methodology developed in the present study seeks to contribute to data
collection and analysis techniques in data-poor areas by providing built environment factors and
variables as well as methodological procedures to collect them so that planners can continue to
collect this information and build a comprehensive data-base of urban development around BRT
stops in India.
The present study provides a BRT typology that can be used as a foundation for assessing future
land development around transit. The assessment of development potential thus becomes an
application of this work for local planners, with this study also being useful for highlighting the
development potential of the land around BRT stops in these cities. This study additionally
provides a set of built environment factors and variables to assess the performance of BRT stops
in terms of TOD features that can help planners to establish goals in terms of: increasing the
mixture of land uses (entropy), parking management around BRT stops, the provision of NMT
infrastructure, connectivity (segment density), and the presence of facilities and land uses
oriented towards the BRT. The BRT typology also becomes a useful tool for local planners when
evaluating which areas should become targets for increased density and which existing stops
have development potential. Local planners can also use the BRT typologies in the design,
planning and implementation of TOD station-area plans. For instance, BRT typology 11 offers
the opportunity to identify slum rehabilitation areas in order to promote inclusionary measures as
part of TOD station-area plans.
There are certainly emerging questions after the identification of the 12 BRT typologies
presented in this study. What is the relationship of these types with employment and land value
data? How can the presence of rickshaws in close proximity to BRT stop be or positively or
negatively associated with BRT ridership? What role does urban design play in relation to access
to BRT stops as well as the barrier effect of exclusive lanes? What changes have been or will be
introduced by the BRT system? These are empirically-based questions that require further
research; however, the BRT typology identified in the present study constitutes a point of
departure from which to answer them.
7. Acknowledgments
This research was sponsored by EMBARQ and received support from EMBARQ India’s staff in
Mumbai and Indore and the support from the Center for Environmental Planning and
Technology (CEPT) University’s staff in Ahmedabad.
15. TRB 2015 Annual Meeting Paper revised from original submittal 15
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17. TRB 2015 Annual Meeting Paper revised from original submittal 17
Table 1. Segments and blocks per city.
City No. BRT Stops Segments Segments/Stop Blocks Blocks/Stop
Indore 12 1,114 92.83 316 26.33
Ahmedabad 21 3,630 172.86 710 33.81
Total 33 4,744 132.85* 1,020 30.05*
Source: data collection, Indore (Madhya Pradesh) and Ahmedabad (Gujarat), India (2013).
*Average.
18. TRB 2015 Annual Meeting Paper revised from original submittal 18
Table 2. Descriptive statistics BRT stops in Indore (N=12) and Ahmedabad (N=21)
Variable Definition
Indore Ahmedabad diff mean
valueObs Mean Std. Dev. Min Max Obs Mean Std. Dev. Min Max
Public facility index index of 7 facility types, excludes big box
development (0-7)
12 3.17 1.11 1.00 5.00 21 2.71 1.23 1.00 5.00 0.45
Public facility
density
density of all facilities (except big box
development)
12 37.77 21.44 15.28 76.39 21 28.38 21.40 5.09 81.49 9.40
BRT-oriented
facility index
index of presence of hospitals, libraries,
markets/squares, temples (0-4)
12 1.33 0.78 0.00 2.00 21 1.19 0.81 0.00 3.00 0.14
BRT-oriented
facility density
density of hospitals, libraries, markets/squares,
temples
12 12.31 9.07 0.00 30.56 21 9.94 9.32 0.00 30.56 2.36
Green areas’
density
density of # parks, squares, pocket parks,
green areas, boulevards
12 92.95 42.56 56.02 213.90 21 58.21 54.61 0.00 203.72 34.74
Park density density of # parks, squares, pocket squares 12 43.71 23.13 20.37 106.95 21 35.17 40.26 0.00 157.88 8.55
NMT friendliness density of parks, squares, pocket squares,
boulevards, pedestrian segments, pedestrian
bridges, bike-paths
12 86.16 37.71 30.56 168.07 21 195.23 185.51 5.09 651.90 -109.07
†
Rickshaw/street
vendors density
density of on-street/sidewalk rickshaws
parking and street vendors
12 88.28 48.77 30.56 203.72 21 55.05 43.86 0.00 168.07 33.23
Average block size Average block size in square kilometers 12 6402.33 1777.30 3745.00 10580.00 21 7425.48 4659.21 1980.00 18052.00 -1023.14
Segment on BRT # of segments on BRT right of way 12 9.75 2.26 6.00 14.00 21 15.81 8.72 5.00 41.00 -6.06
†
Land use index # of land uses in stop (1-10) 12 7.50 1.09 6.00 9.00 21 8.00 1.18 5.00 10.00 -0.50
Institutional % segments with institutional use in it 12 0.07 0.09 0.00 0.27 21 0.05 0.09 0.00 0.42 0.03
Industrial % segments with industrial use in it 12 0.01 0.01 0.00 0.04 21 0.00 0.00 0.00 0.01 0.01
Commercial % segments with exclusively commercial use
in it
12 0.10 0.11 0.00 0.33 21 0.07 0.08 0.00 0.24 0.02
Mixed commercial % segments with mixed commercial use in it 12 0.11 0.09 0.00 0.35 21 0.11 0.10 0.00 0.43 -0.01
Residential single
family (attached)
% segments with single residential use in it 12 0.31 0.17 0.02 0.62 21 0.44 0.28 0.00 0.80 -0.13
Residential
multifamily
% segments with multifamily residential use in
it
12 0.18 0.10 0.00 0.36 21 0.20 0.18 0.00 0.74 -0.03
Mixed: Industrial-
commercial
% segments with industrial/commercial use in
it
12 0.01 0.03 0.00 0.12 21 0.07 0.19 0.00 0.85 -0.06
Mixed: commercial
residential
% segments with commercial/residential use in
it
12 0.21 0.11 0.01 0.44 21 0.23 0.13 0.02 0.50 -0.03
Vacant % segments with vacant use in it 12 0.30 0.12 0.14 0.49 21 0.14 0.21 0.03 0.81 0.16
†
Open Green Area % segments with open green area use in it 12 0.07 0.10 0.00 0.35 21 0.05 0.06 0.00 0.20 0.02
BRT-oriented land
uses
density of BRT supportive uses -comercial +
residential+ institutional
12 0.62 0.20 0.18 0.94 21 0.83 0.24 0.15 1.20 -0.21
†
BRT unsupportive
land uses
density of brt unsupportive uses -industrial,
industrial & commercial, vacant
12 0.48 0.18 0.23 0.75 21 0.25 0.26 0.03 0.89 0.23
†
No building height % of Segments with Building Height = None 12 0.36 0.16 0.16 0.73 21 0.16 0.20 0.02 0.81 0.20
†
Low building height % of Segments with Building Height = 1 12 0.28 0.14 0.10 0.51 21 0.28 0.24 0.01 0.77 0.00
19. TRB 2015 Annual Meeting Paper revised from original submittal 19
Medium building
height
% of Segments with Building Height = 2_3 12 0.56 0.15 0.29 0.81 21 0.59 0.24 0.22 0.93 -0.03
High building
height
% of Segments with Building Height = 4_5 12 0.29 0.15 0.06 0.51 21 0.19 0.16 0.01 0.51 0.10
High-rise % of Segments with Building Height More
than 5
12 0.11 0.09 0.00 0.26 21 0.06 0.07 0.00 0.21 0.05
Low urban density % of Segments with low density of built-up
area
12 0.57 0.12 0.35 0.76 21 0.65 0.20 0.34 0.96 -0.08
Medium urban
density
% of Segments with medium density of built-
up area
12 0.23 0.06 0.10 0.33 21 0.25 0.15 0.02 0.62 -0.03
High urban density % of Segments with high density of built-up
area
12 0.19 0.10 0.07 0.43 21 0.09 0.09 0.00 0.35 0.10
†
Low development
level
% of Segments with low development level 12 0.17 0.11 0.06 0.39 21 0.21 0.21 0.01 0.74 -0.03
Medium
development level
% of Segments with medium development
level
12 0.29 0.07 0.19 0.45 21 0.18 0.14 0.03 0.57 0.11
†
High development
level
% of Segments with high development level 12 0.53 0.13 0.16 0.66 21 0.61 0.27 0.17 0.96 -0.07
Urban decay % of Segments with low maintenance
condition
12 0.29 0.11 0.16 0.50 21 0.34 0.23 0.03 0.80 -0.05
Medium condition
& maintenance
% of Segments with medium maintenance
condition
12 0.36 0.14 0.14 0.63 21 0.30 0.13 0.02 0.55 0.06
High condition &
maintenance
% of Segments with high maintenance
condition
12 0.33 0.18 0.05 0.65 21 0.35 0.23 0.01 0.75 -0.01
Affordable Housing % of Segments with presence of Affordable
Housing
12 0.01 0.02 0.00 0.05 21 0.05 0.13 0.00 0.59 -0.04
Slums % of Segments with slums 12 0.06 0.09 0.00 0.26 21 0.15 0.22 0.00 0.67 -0.09
On-street parking % of Segments with on-street parking 12 0.60 0.17 0.17 0.88 21 0.65 0.23 0.00 0.92 -0.05
Off-street parking % of Segments with off-street parking 12 0.15 0.12 0.00 0.34 21 0.21 0.16 0.02 0.55 -0.06
Sidewalk parking % of Segments with parking on sidewalks 12 0.14 0.12 0.03 0.48 21 0.13 0.09 0.00 0.36 0.01
Sidewalks % of Segments with sidewalks 12 0.32 0.14 0.12 0.58 21 0.35 0.19 0.10 0.78 -0.03
Local accessibility
BRT
% of Segments with access to BRT within the
buffer area
12 0.95 0.07 0.81 1.00 21 0.93 0.06 0.80 1.00 0.01
Commercial and
parking
% of Segments with Commercial and Parking 12 0.19 0.10 0.04 0.33 21 0.15 0.09 0.00 0.32 0.04
Vacant and BRT % of Segments Vacant and on BRT Corridor 12 0.04 0.02 0.01 0.09 21 0.02 0.05 0.00 0.19 0.02
Entropy evenness in the distribution of land uses 12 0.64 0.13 0.33 0.81 21 0.60 0.17 0.21 0.90 0.04
Distance to CBD distance to closest activity node (km) 12 4.47 1.53 2.26 6.82 21 9.98 3.48 0.00 15.26 -5.50
†
Segment density density of segments -segs per sqkm- 12 472.80 128.16 259.74 682.46 21 880.35 405.47 188.44 1777.44 -407.56
Population Population within the block (share of block
within ward area)
12 3065.76 2158.61 468.16 7625.75 21 2870.90 1409.28 531.24 6220.98 194.86
Population density Population density (people over gross area) 12 15613.77 10993.73 2384.32 38837.63 21 14621.36 7177.42 2705.58 31683.20 992.41
†
means are statistically different
20. TRB 2015 Annual Meeting Paper revised from original submittal 20
Table 3. Descriptive statistics built environment variables used for factor analysis (N=33)
Variable Obs Mean Std. Dev. Min Max
Public facility index 33 2.88 1.19 1.00 5.00
Public facility density 33 31.79 21.57 5.09 81.49
BRT-oriented facility index 33 1.24 0.79 0.00 3.00
BRT-oriented facility density 33 10.80 9.16 0.00 30.56
Green areas’ density 33 70.84 52.67 0.00 213.90
Park density 33 38.27 34.85 0.00 157.88
NMT friendliness 33 155.57 157.59 5.09 651.90
Rickshaw/street vendors density 33 67.13 47.78 0.00 203.72
Average block size 33 7,053.42 3,860.48 1,980.00 18,052.00
Segment on BRT 33 13.61 7.62 5.00 41.00
Land use index 33 7.82 1.16 5.00 10.00
BRT-oriented land uses 33 0.75 0.24 0.15 1.20
BRT unsupportive land uses 33 0.33 0.26 0.03 0.89
Low building height 33 0.28 0.21 0.01 0.77
High-rise 33 0.08 0.08 0.00 0.26
High urban density 33 0.13 0.11 0.00 0.43
High development level 33 0.58 0.23 0.16 0.96
Slums 33 0.12 0.19 0.00 0.67
Commercial and parking 33 0.16 0.10 0.00 0.33
Vacant and BRT 33 0.03 0.04 0.00 0.19
Entropy 33 0.61 0.16 0.21 0.90
Distance to CBD 33 7.98 3.95 0.00 15.26
Segment density 33 732.15 384.76 188.44 1777.44
Population density 33 14,982.24 8,601.075 2,384.32 38,837.63
21. TRB 2015 Annual Meeting Paper revised from original submittal 21
Table 4. Factor Analysis results (rotated factor loading >|0.40|, N=33).
# Variable
BRT-OD
facility-high
density built-
up area, mixed
land uses
(Factor 1)
Slums NMT
friendly, low
height and
connected
(Factor 2)
BRT-OD land
uses, non-core
consolidated
and connected
(Factor 3)
Green
spaces and
parks
(Factor 4)
Vacant on
BRT, unfilled
and mixed
land uses
(Factor 5)
Rickshaws,
urban core
high density
areas
(Factor 6)
1 Public facility index 0.87
2 Public facility density 0.82
3 BRT-oriented facility index 0.81
4 BRT-oriented facility density 0.81
5 Green areas’ density 0.93
6 Park density 0.94
7 NMT friendliness 0.80
8 Rickshaw/street vendors density 0.75
9 Land use index -0.41
10 Average block size -0.54
11 Segment on BRT 0.44
12 BRT-oriented land uses 0.89
13 BRT unsupportive land uses -0.71
14 Low building height 0.88
15 High-rise*
16 High urban density 0.62 0.43
17 High development level -0.53 0.41 -0.56
18 Slums 0.89
19 Commercial and parking -0.56
20 Vacant and BRT 0.82
21 Entropy 0.62 0.52
22 Distance to CBD 0.52 -0.41
23 Segment density 0.73 0.49
24 Population density -0.47 0.44
Eigenvalue 4.93 4.76 2.82 1.90 1.45 1.23
Cronbach’s Alpha 0.88 0.84 0.78 0.96 0.59 0.55
Note: Factors loading <|0.40| are left blank. *High-rise is the only variable did not load in any factor.
22. TRB 2015 Annual Meeting Paper revised from original submittal 22
Table 5. Mean values for built environment factors and high rise developments per cluster.
Built environment factors
And high-rise
Clusters
1 2 3 4 5 6 7 8 9 10 11 12
# of stops in cluster 8 2 2 1 2 1 2 2 2 5 4 2
BRT-OD Facility-high density built-up area, mixed
land uses
-0.18 -0.25 1.08 -1.12 -0.09 -1.85 0.40 1.73 1.81 -0.27 -0.33 -1.14
Slums NMT friendly, low height and connected -0.16 0.24 -0.17 -0.51 0.60 -1.19 -0.17 0.32 -0.87 -0.78 2.21 -0.92
BRT-OD land uses, non-core consolidated and
connected
-0.25 -0.47 -0.28 1.19 -1.02 -1.69 -2.24 0.42 0.40 1.36 0.48 0.06
Green spaces and parks 0.37 0.03 1.90 3.43 -0.93 -0.64 -0.17 -0.61 -0.25 -0.80 -0.18 -0.47
Vacant on BRT and mixed land uses 0.20 -0.01 -0.16 -0.69 -0.60 -1.89 -0.39 0.30 -0.23 -0.78 0.21 3.11
Rickshaws, core high density areas 0.48 2.96 -0.07 -1.45 -0.83 -1.11 -0.44 0.48 -0.61 -0.09 -0.43 -1.02
High-rise developments* 0.23 1.23 -0.69 -0.64 -0.85 -0.95 2.02 -0.49 1.44 -0.66 -0.71 0.28
*standardized value
