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Minimising average passenger waiting time in personal rapid transit systems

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  • John Lees-Miller

Abstract

Personal Rapid Transit (PRT) is an emerging urban transport mode. A PRT system operates much like a conventional hackney taxi system, except that the vehicles are driven by computer (no human driver) between stations in a dedicated network of guideways. The world’s first two PRT systems began operating in 2010 and 2011. In both PRT and taxi systems, passengers request immediate service; they do not book ahead. Perfect information about future requests is therefore not available, but statistical information about future requests is available from historical data. If the system does not use this statistical information to position empty vehicles in anticipation of future requests, long passenger waiting times result, which makes the system less attractive to passengers, but using it gives rise to a difficult stochastic optimisation problem. This paper develops three lower bounds on achievable mean passenger waiting time, one based on queuing theory, one based on the static problem, in which it is assumed that perfect information is available, and one based on a Markov Decision Process model. An evaluation of these lower bounds, together with a practical heuristic developed previously, in simulation shows that these lower bounds can often be nearly attained, particularly when the fleet size is large. The results also show that low waiting times and high utilisation can be simultaneously obtained when the fleet size is large, which suggests important economies of scale. Copyright Springer Science+Business Media New York 2016

Suggested Citation

  • John Lees-Miller, 2016. "Minimising average passenger waiting time in personal rapid transit systems," Annals of Operations Research, Springer, vol. 236(2), pages 405-424, January.
  • Handle: RePEc:spr:annopr:v:236:y:2016:i:2:p:405-424:10.1007/s10479-013-1492-3
    DOI: 10.1007/s10479-013-1492-3
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    Cited by:

    1. Wei-Hsi Hung & Yao-Tang Hsu, 2020. "Service Quality and Service Gap of Autonomous Driving Group Rapid Transit System," Sustainability, MDPI, vol. 12(22), pages 1-18, November.
    2. Tatiana Babicheva & Wilco Burghout, 2019. "Empty vehicle redistribution in autonomous taxi services," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 745-767, December.
    3. Hadi Charkhgard & Mahdi Takalloo & Zulqarnain Haider, 2020. "Bi-objective autonomous vehicle repositioning problem with travel time uncertainty," 4OR, Springer, vol. 18(4), pages 477-505, December.
    4. Kai Li & Yuqian Pan & Bayi Cheng & Bohai Liu, 2018. "The Setting and Optimization of Quick Queue," Journal of Optimization Theory and Applications, Springer, vol. 178(3), pages 1014-1026, September.
    5. Hu, Beibei & Xia, Xuanxuan & Sun, Huijun & Dong, Xianlei, 2019. "Understanding the imbalance of the taxi market: From the high-quality customer’s perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).

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