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Designing Sustainable Public Transportation: Integrated Optimization of Bus Speed and Holding Time in a Connected Vehicle Environment

Author

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  • Wei Wu

    (School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410004, China)

  • Wanjing Ma

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, TongjiUniversity, Shanghai 201804, China)

  • Kejun Long

    (School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410004, China)

  • Heping Zhou

    (School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410004, China)

  • Yi Zhang

    (State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    Key Laboratory of Special Environment Road Engineering of Hunan Province, Changsha University of Science & Technology, Changsha 410004, China)

Abstract

Developing public transportation and giving priority to buses is a feasible solution for improving the level of public transportation service, which facilitates congestion alleviation and prevention, and contributes to urban development and city sustainability. This paper presents a novel bus operation control strategy including both holding control and speed control to improve the level of service of transit systems within a connected vehicle environment. Most previous work focuses on optimization of signal timing to decrease the bus signal delay by assuming that holding control is not applied; the speed of buses is given as a constant input and the acceleration and deceleration processes of buses can be neglected. This paper explores the benefits of a bus operation control strategy to minimize the total cost, which includes bus signal delay, bus holding delay, bus travel delay, acceleration cost due to frequent stops and intense driving. A set of formulations are developed to explicitly capture the interaction between bus holding control and speed control. Experimental analysisand simulation tests have shown that the proposed integrated operational model outperforms the traditional control, speed control only, or holding control only strategies in terms of reducing the total cost of buses. The sensitivity analysis has further demonstrated the potential effectiveness of the proposed approach to be applied in a real-time bus operation control system under different levels of traffic demand, bus stop locations, and speed limits.

Suggested Citation

  • Wei Wu & Wanjing Ma & Kejun Long & Heping Zhou & Yi Zhang, 2016. "Designing Sustainable Public Transportation: Integrated Optimization of Bus Speed and Holding Time in a Connected Vehicle Environment," Sustainability, MDPI, vol. 8(11), pages 1-15, November.
  • Handle: RePEc:gam:jsusta:v:8:y:2016:i:11:p:1170-:d:82717
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    References listed on IDEAS

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    Cited by:

    1. Jingwei Wang & Yin Han & Peng Li, 2022. "Integrated Robust Optimization of Scheduling and Signal Timing for Bus Rapid Transit," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
    2. Zhensheng Wang & Yang Yue & Qingquan Li & Ke Nie & Wei Tu & Shi Liang, 2017. "Analyzing Risk Factors for Fatality in Urban Traffic Crashes: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 9(6), pages 1-13, May.
    3. Kai Liu & Dong Liu & Cheng Li & Toshiyuki Yamamoto, 2019. "Eco-Speed Guidance for the Mixed Traffic of Electric Vehicles and Internal Combustion Engine Vehicles at an Isolated Signalized Intersection," Sustainability, MDPI, vol. 11(20), pages 1-13, October.
    4. Qian Gao & Shuyang Zhang & Guojun Chen & Yuchuan Du, 2020. "Two-Way Cooperative Priority Control of Bus Transit with Stop Capacity Constraint," Sustainability, MDPI, vol. 12(4), pages 1-13, February.
    5. Rafidah Md Noor & Nadia Bella Gustiani Rasyidi & Tarak Nandy & Raenu Kolandaisamy, 2020. "Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study," Sustainability, MDPI, vol. 13(1), pages 1-24, December.

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