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1. www.TransformingTransportation.org
Examining the Relationship
between Bus Rapid Transit (BRT) and
the Built Environment in Latin America
Erik Vergel-Tovar
PhD Candidate, UNC-Chapel Hill, and 2014 Lee Schipper Memorial
Scholar
Presented at Transforming Transportation 2015

2. Examining the relationship between
Bus Rapid Transit (BRT) and the built
environment in Latin America
C. Guatemala
Bogotá
Quito
Guayaquil
Goiânia
Sao Paulo
Curitiba
Erik Vergel-Tovar
evergel@live.unc.edu
PhD Candidate and Lee Schipper Scholar
Department of City and Regional Planning
University of North Carolina at Chapel Hill
© IPPUC (2011)
7 cities
4,837,631 # passengers per day
15.42 % of world ridership

3. Motivation
Conventional wisdom and some
rail-based evidence about
transit-oriented development
(TOD) suggest:
• there is a positive association
between transit ridership and
population density around stops
BRT trunk corridors and population density in
Curitiba (census tract level)
However, using regression
analysis this research examines:
• whether population density and
other built environment attributes
explain BRT ridership in seven
cities in Latin America

4. 1. Density
2. Diversity
3. Design
4. Destination
accessibility
5. Distance to transit
Transit
ridership
(# passengers per day)
The five 5 “Ds”
Attributes of the built environment in dimensions
Built environment and travel
6. Parking
7. NMT
infrastructure
8. Socioeconomic
characteristics
9. Facilities and
public spaces
Additional dimensions
TOD
Transit-oriented
development
Features

5. Attributes of transit-oriented development (TOD)
• Compact and dense
• High land use mixtures
• High-quality pedestrian environment
• Coordination between transit and the built environment
Transit oriented development TOD
Some benefits of TOD
• Concentrates demand (economies of density)
• Accessibility benefits (local & regional scale)
• Real estate/neighborhood-community development
strategy (development around transit stations)
• Generates revenue for the city (ridership, among others)

6. Built environment and transit ridership
catchment areas around transit stations
measurements of built environment attributes and tests
associations with ridership levels
Heavy Rail
• Taipei
• New York
• Hong Kong
• Seoul
• Montreal
• Nanjing
Light Rail Transit
• Metropolitan Areas
(USA and
Canada)
Gap regarding how
built environment features influence BRT ridership
Bus Rapid Transit
• Bogota
• Los Angeles
Station level studies (aggregated type) on the relationship
between the built environment and transit ridership

7. 1. What is the association between population
density and BRT ridership?
Sample of 87 BRT stations in Curitiba
Sample of 120 BRT stations in seven cities in Latin America
2. What are the associations between built
environment attributes and BRT ridership?
Sample of 120 BRT stations in seven cities in Latin America
3. Are TOD features associated with BRT
ridership?
Built environment factors – TOD features
Cluster analysis – BRT stations typology - clusters
Research questions

8. Data management Curitiba (N=87)
Detailed area
Sample of 87 BRT stations in Curitiba (excluding
those overlapped with BRT terminals)
• BRT Terminals (n=15) area of study: 0.79 Km2
• BRT stations (n=72) area of study: 0.15 Km2 to
0.20 Km2
BRT stop
overlapped
Overlapped area excluded
from the analysis
Detailed
area

9. Data collection seven cities (N=120)
Segment
Block
BRT Stop
(buffer area)
BRT Terminal Portal 80
Bogota (Colombia)
Segments per BRT stations
(N=120)
108.45 109.75
102.38
118.91
136.67
103.90
143.91
0
50
100
150
200
Bogota Sao Paulo Curitiba Goiania CdGuatemala Quito Guayaquil
Sample: 120 BRT stations visited in seven cities
Aggregated data: 13,555 segments and 3,804 blocks studied
N=31 N=12 N=16 N=11 N=9 N=30 N=11

10. All data aggregated at station level (continuous variables)
Methodology: BRT station level
Segment-level data
% of segments in stop
Block-level data
density or count of instances (0-n)
Station-level data: population density, centrality,
segments density, average distance to BRT stop
BRT Terminal Americas
Bogota (Colombia)
BRT Terminal Americas
Bogota (Colombia)

11. 500
700
900
1,100
1,300
1,500
1,700
1,900
2,100
2,300
2,500
0 20 40 60 80 100 120 140 160 180 200
PredictedBRTridership
Population density (people/ha)
Sample BRT Terminals and stations N=87
Sample BRT single stations N=72
Population density is positively associated to BRT ridership
with an elasticity of 0.26
Results Curitiba
Research Question 1
R2 0.8411
R2 0.4260

12. 1,500
2,500
3,500
4,500
5,500
6,500
7,500
8,500
9,500
10,500
11,500
12,500
0 50 100 150 200 250 300 350 400
PredictedBRTridership
Population density (people/ha)
Population density is not statistically significant (N=120)
Centrality (negatively) and BRT Terminals (positively)
associated with BRT ridership
Results seven cities Latin America
Research question 1
Bogota (+)
Ciudad de Guatemala
Curitiba (+)
Guayaquil
Quito
Sao Paulo
Goiania (+)

13. Entropy is positively associated with BRT ridership
% segments with high-rise developments and developments (>5
stories) are positively associated with BRT ridership
Results seven cities Latin America
Research question 2
Predicted BRT ridership and entropy
(evenness commercial, residential, institutional land uses)
5,000
7,500
10,000
12,500
15,000
17,500
20,000
0 10 20 30 40 50 60 70 80 90 100
PredictedBRTridership
Percentile
R 2 0.7353
Predicted BRT ridership and developments
(% segments high-rise development - % segments with >5 stories)
6,000
7,000
8,000
9,000
10,000
11,000
12,000
13,000
0 10 20 30 40 50 60 70 80 90 100
PredictedBRTridership
Percentile
High-rise developments >5 stories
R 2 0.7247
R 2 0.7256

14. 0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
0 10 20 30 40 50 60 70 80 90 100
PredictedBRTridership
Percentile
NMT friendliness BRT oriented facility index
100,000
125,000
150,000
175,000
200,000
225,000
250,000
0 10 20 30 40 50 60 70 80 90 100
PredictedBRTridership
Percentile
NMT and facilities: positively associated with BRT ridership
BRT Terminals and % segments with high-rise developments:
positively associated with BRT ridership
Predicted BRT ridership, NMT
friendliness and facilities
(density of non-motorized transport infrastructure and facilities)
R 2 0.7461
R 2 0.6747
Predicted BRT ridership, Terminals and
high-rise developments
(interaction BRT terminals and % segments high-rise developments)
R 2 0.6992
Results seven cities Latin America
Research question 2

15. 8,000
9,000
10,000
11,000
12,000
13,000
14,000
15,000
16,000
17,000
0 10 20 30 40 50 60 70 80 90 100
PredictedBRTridership
Percentile
High-rise multifamily BRT-oriented mixed land uses Institutional facilities facing BRT corridor
• High-rise multifamily developments, BRT-oriented mixed land uses
• Institutional land uses, facilities, high building heights facing BRT
corridor
Predicted BRT ridership and built environment factors
R 2 0.7184
TOD features with positive association with BRT ridership:
Results seven cities Latin America
Research question 3

16. C1
(0.467)
C2
(0.038)
C3
(0.335)
C4
(ref)
C5
(0.024)
C6
(0.415)
C7
(0.036)
C8
(0.003)
C9
(0.393)
C10
(0.329)
C11
(0.326)
C12
(0.047)
C13
(0.965)
Percentage change 25.99% 144.04% 73.24% 156.79% 110.40% 150.54% 325.63% 31.82% 81.41% 59.06% 161.11% -9.13%
25.99%
144.04%
73.24%
156.79%
110.40%
150.54%
325.63%
31.82%
81.41%
59.06%
161.11%
-9.13%
-50%
0%
50%
100%
150%
200%
250%
300%
350%
CoefficientafterKennedy
Predicted percentage change on BRT ridership by cluster (BRT station type)
Results seven cities Latin America
Research question 3
BRT
Terminals
High-rise
multifamily
mixed land
use
Historic
Center

17. Discussion
• Positive association between population density and BRT
ridership in Curitiba with an elasticity of 0.26
• At the city level, there is a positive association between
population density and BRT ridership in the sample of
BRT stations in Bogota, Curitiba and Goiania
• Population density is not statistically significant in the
data analysis developed with the sample of 120 BRT stations
in seven cities in Latin America
• The introduction of built environment attributes in the
analysis of the sample of 120 BRT stations increases the
explanatory power of the model by 12.3% or 8 percentage
points

18. Conclusion
• Population density is necessary but not sufficient in order to
achieve high levels of ridership at the BRT station level
• Built environment attributes commonly considered as part
of transit-oriented development (TOD) features are
positively associated with BRT ridership
• Characteristics of urban development typologies around
BRT stations with positive associations with BRT ridership:
• High-rise multifamily and commercial developments
• Mixture of BRT oriented land uses (commercial,
residential and institutional)
• High building heights (more than 5 stories)
• Presence of facilities facing BRT corridors

19. Applicability in other countries (India)
• Development of BRT Terminals as urban development
projects including:
• High-rise developments
• Mixture of land uses (commercial, residential,
institutional)
• Network of NMT infrastructure
• Implementation of land use planning measures around
current and future BRT stations by promoting:
• Land readjustment schemes
• Public space for NMT infrastructure
• Land developments (> 5 stories)
• Mixture of commercial, residential and institutional land
uses
• Facilities on the BRT right of way.

20. Policy Implications
Promotion of BRT station area plans in two scenarios
• Design and planning stage (future BRT stations):
• Data collection of built environment attributes
• Land use planning regulations
• Land readjustment and value capture measures
• Implementation and operation stage (current BRT stations):
• Base line of TOD features
• Promote high-rise developments around BRT Terminals
• Priority to NMT infrastructure around BRT stations
located in Historic Centers
• Slum upgrading measures in close proximity to BRT
station in low income areas
• Urban renewal and revitalization around BRT stations in
areas with urban decay

21. Recommendations
Further research on the relationship between the built
environment and transit ridership
• Extend this methodology in other countries implementing
BRT systems (India, Mexico, China, Turkey and South Africa)
• Include additional variables in the analyses such as
socioeconomic data and BRT systems design features
• Test several hypotheses by comparing different buffer
areas addressing methodologically the overlap between BRT
stations
• Data collection for longitudinal studies looking at changes
before and after around BRT stations in order to conduct data
analyses to establish causality on this relationship

22. Bus Rapid Transit (BRT) and urban development in Latin America and India
Acknowledgements
Professor Daniel A. Rodriguez
Mr. Ramon Munoz-Raskin
Mr. Sam Zimmerman
Dr. Dario Hidalgo
Local Governments
Relatives, colleagues and friends
who supported this research
Amanda Klepper (GIS)