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A novel control strategy in mitigating bus bunching: Utilizing real-time information

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  • Zhou, Chang
  • Tian, Qiong
  • Wang, David Z.W.

Abstract

A variety of control mechanisms have been recommended to concentrate on the issue of bus bunching. However, the majority of existing measures focus on controlling the bus operation directly from the operator's perspective. This study, nevertheless, presents a novel control technique by providing passengers with real-time wait time information and degrees of in-vehicle congestion. Specifically, passengers are expected to use the given information to calculate the overall travel cost and, therefore, make decisions on whether boarding on the arriving bus or waiting for the next buses. In this way, bus service operators would reduce bus bunching by adjusting passengers' boarding choice behavior rather than controlling the bus vehicles directly. We propose a bus traffic propagation model to simulate the bus movements, through which several system performance metrics, such as the shortest vehicle spacing distribution and the average ridership per bus vehicle at each stop, can be evaluated for different bus bunching control measures. The numerical results show that providing in-vehicle congestion information is as effective as the schedule-based and headway-based control methods in achieving mitigation of bus bunching. Passengers on long trips are more inclined to wait for a few more shifts for a bus that is not very crowded. This study proposes a novel control strategy by offering passengers real-time information to serve as a supplement, never a substitute, to the existing control measures in mitigating bus bunching problems.

Suggested Citation

  • Zhou, Chang & Tian, Qiong & Wang, David Z.W., 2022. "A novel control strategy in mitigating bus bunching: Utilizing real-time information," Transport Policy, Elsevier, vol. 123(C), pages 1-13.
  • Handle: RePEc:eee:trapol:v:123:y:2022:i:c:p:1-13
    DOI: 10.1016/j.tranpol.2022.04.022
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    2. Stefan Voß, 2023. "Bus Bunching and Bus Bridging: What Can We Learn from Generative AI Tools like ChatGPT?," Sustainability, MDPI, vol. 15(12), pages 1-19, June.

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