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Rethinking the Next Generation of BRT in China

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

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Rethinking the Next Generation of BRT in China

  1. 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. 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. 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. 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. 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. 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. 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. 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. 9. SAMPLE OF COUNTRIES 99 corridors/systems 1,775 kilometres 21 countries 59 cities 2013 and 2014
  10. 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. 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. 12. STRENGTHS AND OPPORTUNITIES BY SUBCATEGORY CHINESE AND NON-CHINESE BRT SYSTEMS (I)
  13. 13. STRENGTHS AND OPPORTUNITIES BY SUBCATEGORY CHINESE AND NON-CHINESE BRT SYSTEMS (II)
  14. 14. STRENGTHS AND OPPORTUNITIES BY SUBCATEGORY CHINESE AND NON-CHINESE BRT SYSTEMS (III)
  15. 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. 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. 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. 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. 19. AVERAGE IMPACT IN BRT PRODUCTIVITY BY SUBCATEGORY (STRENGTHS)
  20. 20. AVERAGE IMPACT IN BRT PRODUCTIVITY BY SUBCATEGORY (OPPORTUNITIES)
  21. 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. 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. 23. FURTHER RESEARCH - Include data from the ITDP Standard, Edition 2016 to increase the sample size - Perform sensitivity analysis - Implement an online dashboard
  24. 24. ACKNOWLEDGEMENTS • Bus Rapid Transit Centre of Excellence (BRT-CoE) funded by VREF
  25. 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. 26. JUAN MIGUEL VELASQUEZ PABLO GUARDA RETHINKING THE NEXT GENERATION OF BRT IN CHINA July Webinar BRT Centre of Excellence

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