Webinar Session presented by Juan Miguel Velásquez (WRI), on July 20th, 2016. BRT Centre of Excellence (www.brt.cl)

1. JUAN MIGUEL VELASQUEZ, SENIOR ASSOCIATE
PABLO GUARDA, TRANSPORT RESEARCH INTERN
RETHINKING THE NEXT
GENERATION OF BRT IN
CHINA
July Webinar BRT Centre of Excellence

2. THE URGENCY TO DEVELOP PUBLIC
TRANSPORT IN CHINA
Source: International Energy Agency (2016)
China has agreed to reduce CO2 emission per unit GDP by 60-65%
compared to emissions in 2005 (Paris agreement, COP21)

3. EXPLOSIVE GROWTH OF BUS RAPID TRANSIT
IN CHINA
“Over the past eight
years, China has
added BRT lane-kms
at a faster pace than
any part of the world”
(Cervero, 2013).

4. … AND THE CHALLENGE OF BRT SERVICE
QUALITY?
CUSTReC (2016)
The main challenge today is not only increasing
the coverage of BRT but also improving service
quality and performance

5. STUDY OVERVIEW
• Compare design and performance indicators
between Chinese and non-Chinese BRTs.
• Explore the relationship between the design
features of BRTs and their performance.
• Identify specific design elements to improve the
performance of Chinese BRTs.

6. METHODOLOGY
• Step I: Data collection and cleaning
• Step II: Assessment of strengths and opportunities
of Chinese BRTs
• Step III: Quantification of the impact of BRT design
improvements on BRT performance

7. STEP I: DATA COLLECTION AND CLEANING
• Unit of analysis: System/Corridor (99 obs.)
• Data sources
– BRTData.org
– ITDP BRT Standard Editions 2013, 2014
• Representativeness
– 21 countries, 59 cities.
– More than 1,800 km of BRT
• Performance measurements
– Productivity [pax/km], speed, frequency, throughput

8. ITDP BRT STANDARD
• Categories:
1. BRT Basics: minimal requirements
to be qualified as a BRT
2. Service Planning
3. Infrastructure
4. Stations
5. Communications
6. Access and Integration
7. Point Deductions
• Subcategories (38)
• Ranking: BRT Basic, Bronze, Silver, Gold
• Evaluations made by ITDP experts.

9. SAMPLE OF COUNTRIES
99 corridors/systems
1,775 kilometres
21 countries
59 cities
2013 and 2014

10. STEP II: ASSESSMENT OF STRENGTHS AND
OPPORTUNITIES
• Data: Evaluations of BRT corridors and systems in the ITDP Standard
• Output: Average score difference among the BRT design indicators
between the Target and Benchmark groups.
• ANOVA: Assessment of the statistical difference in the average value
of the indicators computed for the Target and Benchmark groups.
• Target group: Chinese BRT corridors/systems
• Benchmark group: Non-Chinese BRT corridors/systems

11. STRENGTHS AND OPPORTUNITIES BY CATEGORY
CHINESE AND NON-CHINESE BRT SYSTEMS
1. Strength in Chinese BRTs: Positive and Significant Difference (Blue)
2. Opportunities in Chinese BRTs: Negative and Significant Difference (Blue)
3. No difference: Non-statistically Significant Difference (Gray)

12. STRENGTHS AND OPPORTUNITIES BY SUBCATEGORY
CHINESE AND NON-CHINESE BRT SYSTEMS (I)

13. STRENGTHS AND OPPORTUNITIES BY SUBCATEGORY
CHINESE AND NON-CHINESE BRT SYSTEMS (II)

14. STRENGTHS AND OPPORTUNITIES BY SUBCATEGORY
CHINESE AND NON-CHINESE BRT SYSTEMS (III)

15. STEP III: QUANTIFICATION OF THE IMPACT OF BRT
DESIGN IMPROVEMENTS ON PERFORMANCE (I)
• Objective: Linking BRT Productivity and BRT standard
• Statistical Method:
Simple Linear Regression (SLR model)
(Productivity vs score)
!" = $ + &'(" + )"
– !": Productivity BRT corridor − system =
>?@
AB
– (": Score BRT corridor − system = [point scale]
– $, &: Estimated parameters
– )": Random error

16. STEP III: QUANTIFICATION OF THE IMPACT OF BRT
DESIGN IMPROVEMENTS ON PERFORMANCE (II)
• Objective: Linking BRT Productivity and BRT standard
• Statistical Method:
Multiple Linear Regression (MLR model)
(Productivity vs score by category)
!" = $L + M &NO(",O
O∈R
+ )"
– !": Average productivity BRT corridor − system =
>?@
AB
– (",O: Score BRT corridor − system = in category d [point scale]
– $, &: Estimated parameters
– )": Random error

17. PRODUCTIVITY AND SCORES (CITIES)
REGRESSION ANALYSIS
Chinese BRTs
ηL = αYZ + β]S
αY = −36,253.2 (−3.2)
β = 737.1 (4.2)
Rhij
k
= 0.57
N = 14
Non-Chinese BRTs
ηL = αYo] + βo]S
αY] = −15,467.6 (−2.2)
β] = 322.9 (3.4)
Rhij
k
= 0.22
N = 38

18. PRODUCTIVITY AND SCORES (CORRIDOR / SYSTEM)
REGRESSION MODEL RESULTS
Variable (t-test)
MLR model SLR model
China No China All China No China All
β' (Score) - - - 673.2 (4.3) 272.2 (4.4) 328.2 (5.7)
βr (BRT Basics) 154.5 (0.8) 44.5 (0.6) 35.2 (0.5) - - -
βk (Service planning) 116.0 (1.1) 207.6 (5.1) 172.2 (4.5) - - -
βs (Infrastructure) 93.6 (0.9) 88.2 (2.3) 54.0 (1.5) - - -
βt (Station Design) 189.5 (1.2) -1.3 (0.0) 74.5 (1.5) - - -
βu (Communications) -57.4 (-0.6) -35.5 (-0.9) -50.2 (-1.5) - - -
βv (Access & Integration) 283.2 (2.5) 67.3 (1.3) 112.3 (2.9) - - -
βw (Point Deductions) 277.0 (0.9) -22.5 (-0.2) -25.8 (-0.3) - - -
$ (Intercept) -33,584.8 (-2.7) -16,861.6 (-3.5) -17,372.0 (-4.2) -34,267.2 (-3.4) -10,158.8 (-2.3) -13,789.5 (-3.4)
x 20 72 92 20 72 92
yzij
k
0.62 0.39 0.36 0.48 0.21 0.26
The MLR model estimated with data from Chinese BRTs will be
used for our further analysis

19. AVERAGE IMPACT IN BRT PRODUCTIVITY
BY SUBCATEGORY (STRENGTHS)

20. AVERAGE IMPACT IN BRT PRODUCTIVITY
BY SUBCATEGORY (OPPORTUNITIES)

21. MAIN RESULTS
• In China, the score difference in the category Integration and Access
had a significant effect on BRT productivity. In this item, Chinese BRTs
obtained 2.42 points lower than the benchmark group, which is
equivalent to a decrease in productivity of 4,895 [pax/km].
• In the subcategory Intersection Treatments, within the category BRT
Basics, Chinese BRTs obtained significantly lower scores than non-
Chinese BRTs.
• However, in the Multiple Linear Regression (MLR) model, the
estimated parameter associated with the category BRT Basics was
non-statistically significant, which could be explained by the small
sample size and the high variability in the scores.

22. CONCLUSIONS AND POLICY IMPLICATIONS
• This study identifies priorities to improve the standard of
Chinese BRTs based on international practices.
• The use of regression models allows to quantify the
differences of BRT design quality in terms of BRT
productivity (pax/km).
• This study integrated two large and public datasets
(BRTData.org and ITDP Standards) to perform the
quantitative analysis.

23. FURTHER RESEARCH
- Include data from the ITDP Standard, Edition
2016 to increase the sample size
- Perform sensitivity analysis
- Implement an online dashboard

24. ACKNOWLEDGEMENTS
• Bus Rapid Transit Centre of Excellence
(BRT-CoE) funded by VREF

25. WORK CITED
• Cervero, R., 2013. Bus Rapid Transit (BRT): An Efficient and Competitive Mode of Public
Transport, IURD Working Paper 2013-01. http://escholarship.org/uc/item/4sn2f5wc.pdf
• Fjellstrom, K., 2010. Bus Rapid Transit in China. Built Environment 36, 363–374.
http://dx.doi.org/10.3141/2193-03.
• Munoz, J.C. and Paget-Seekins, L., 2016. Restructuring Public Transport Through Bus
Rapid Transit: An International and Interdisciplinary Perspective. Policy Press, Bristol,
United Kingdom.
• Pucher, J., Peng, Z., Mittal, N., Zhu, Y. and Korattyswaroopam, N., 2007. Urban Transport
Trends and Policies in China and India: Impacts of Rapid Economic Growth. Transport
Reviews 27, 379–410. http://dx.doi.org/10.1080/01441640601089988.
• Schwenk, J.C., 2002. Evaluation guidelines for bus rapid transit demonstration projects
(RPRT). Federal Transit Administration (FTA), U.S. Department of Transportation.
http://ntl.bts.gov/lib/29000/29200/29273/13831_files/13831.pdf

26. JUAN MIGUEL VELASQUEZ
PABLO GUARDA
RETHINKING THE NEXT
GENERATION OF BRT IN
CHINA
July Webinar BRT Centre of Excellence