DOI QR코드

DOI QR Code

A Estimation of Dwell Time of Low-floor Buses considering S-BRT Operation Behavior

S-BRT 운행행태를 고려한 저상버스의 정차시간 예측모형

  • Shin, S.M. (Department of Transportation Engineering, University of Seoul) ;
  • Lee, S.B. (Department of Transportation Engineering, University of Seoul) ;
  • Kim, Y.C. (Department of Transportation Engineering, University of Seoul) ;
  • Park, S.H. (Department of Transportation Engineering, University of Seoul) ;
  • Yu, Y.S. (Department of Transportation Engineering, University of Seoul) ;
  • Choi, J.H. (Department of Transportation Engineering, University of Seoul)
  • 신소명 (서울시립대학교 교통공학과) ;
  • 이수범 (서울시립대학교 교통공학과) ;
  • 김영찬 (서울시립대학교 교통공학과) ;
  • 박신형 (서울시립대학교 교통공학과) ;
  • 유연승 (서울시립대학교 교통공학과) ;
  • 최정훈 (서울시립대학교 교통공학과)
  • Received : 2020.11.30
  • Accepted : 2021.01.15
  • Published : 2021.02.28

Abstract

This basic study introduces the concept of S-BRT and develops dwell time estimation models that consider road geometry and S-BRT characteristics for a signal operation strategy to meet the S-BRT's operational goal of high speed and punctuality. Field surveys of low-floor buses similar in shape to S-BRTs and data collection of passengers, station elements, vehicle elements, and other factors that can affect stop times were used in a regression analysis to establish statistically significant dwell time estimation models. These dwell time estimation models are developed by categorizing according to the locations of the signal or sidewalk that have the most impact on the dwell time. In this way, the number of people boarding and alighting the bus at the crowded door and the number of people boarding and alighting the bus at the front door considering the internal congestion was analyzed to affect the dwell time. The estimation dwell time models in this study can be used in the establishment of strategies that provide priority signals to S-BRTs.

Keywords

Acknowledgement

This research was supported by a grant from the Development of S-BRT Priority Signal and Safety Management Technology Program funded by the Ministry of Land, Infrastructure, and Transport of Korea [grant number 20SBRT-C158062-01].

References

  1. Ministry of Land, Infrastructure and Transport, "Guideline of Super Bus Rapid Transit", 2019.
  2. Ministry of Land, Infrastructure and Transport, "Korea Highway Capacity Manual 2013", 2013.
  3. J. Xin and S. Chen, "Bus Dwell Time Prediction Based on KNN", Procedia Engineering", Vol. 137, pp. 283-288, 2016. https://doi.org/10.1016/j.proeng.2016.01.260
  4. E. M. Gonzalez, M. G. Romana and O. M. Alvaro, "Bus Dwell-Time Model of Main Urban Route Stops", Transportation Research Record : Journal of the Transportation Research Board, Vol. 2274, Issue 1, pp. 126-134, 2012. https://doi.org/10.3141/2274-14
  5. TRB, "Highway Capacity Manual", 2000.
  6. C. Csiszar and Z. Sandor, "Method For Analysis and Prediction of Dwell Times at Stops in Local Bus Transportation", Transport, Vol. 32, Issue 3, pp. 302-313, 2017. https://doi.org/10.3846/16484142.2016.1190402
  7. Y, G. Kang, S. Y. Go and J. S. Seo, "An Analysis on Bus Dwell Times", Journal of Korean Society of Transportation, 2000.
  8. F. Li, Z. Duan and D. Yang, "Dwell Time Estimation Models for Bus Rapid Transit Stations", Journal of Modern Transportation, Vol. 20, No. 3, pp. 168-177, 2012. https://doi.org/10.1007/BF03325795
  9. A. Kathuria et al., Examining Bus Lost Time Dynamics for a Bus Rapid Transit Station", Journal of Public Transportation, Vol. 19, No. 2, pp. 168-182, 2016. https://doi.org/10.5038/2375-0901.19.2.10
  10. A. Tirachini, "Bus Dwell Time : The Effect of Different Fare Collection Systems, Bus Floor Level and Age of Passengers", Transportmetrica A: Transport Science, Vol. 9, Issue 1, pp. 28-49, 2013. https://doi.org/10.1080/18128602.2010.520277