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A model for the fleet sizing of demand responsive transportation services with time windows

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  • Diana, Marco
  • Dessouky, Maged M.
  • Xia, Nan

Abstract

We study the problem of determining the number of vehicles needed to provide a demand responsive transit service with a predetermined quality for the user in terms of waiting time at the stops and maximum allowed detour. We propose a probabilistic model that requires only the knowledge of the distribution of the demand over the service area and the quality of the service in terms of time windows associated of pickup and delivery nodes. This methodology can be much more effective and straightforward compared to a simulation approach whenever detailed data on demand patterns are not available. Computational results under a fairly broad range of test problems show that our model can provide an estimation of the required size of the fleet in several different scenarios.

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  • Diana, Marco & Dessouky, Maged M. & Xia, Nan, 2006. "A model for the fleet sizing of demand responsive transportation services with time windows," Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 651-666, September.
    • Handle: RePEc:eee:transb:v:40:y:2006:i:8:p:651-666
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    4. Figliozzi, Miguel Andres, 2009. "Planning approximations to the average length of vehicle routing problems with time window constraints," Transportation Research Part B: Methodological, Elsevier, vol. 43(4), pages 438-447, May.
    5. Dai, Rongjian & Ding, Chuan & Gao, Jian & Wu, Xinkai & Yu, Bin, 2022. "Optimization and evaluation for autonomous taxi ride-sharing schedule and depot location from the perspective of energy consumption," Applied Energy, Elsevier, vol. 308(C).
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    7. Nguyen-Hoang, Phuong & Yeung, Ryan, 2010. "What is paratransit worth?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 841-853, December.
    8. Ansari, Sina & Başdere, Mehmet & Li, Xiaopeng & Ouyang, Yanfeng & Smilowitz, Karen, 2018. "Advancements in continuous approximation models for logistics and transportation systems: 1996–2016," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 229-252.
    9. Luca Quadrifoglio & Randolph W. Hall & Maged M. Dessouky, 2006. "Performance and Design of Mobility Allowance Shuttle Transit Services: Bounds on the Maximum Longitudinal Velocity," Transportation Science, INFORMS, vol. 40(3), pages 351-363, August.
    10. Tsubouchi, Kota & Yamato, Hiroyuki & Hietaka, Kazuo, 2009. "A research on the new Demand Responsive Transit service in Japan," 50th Annual Transportation Research Forum, Portland, Oregon, March 16-18, 2009 207730, Transportation Research Forum.
    11. Xuemei Zhou & Guohui Wei & Yunbo Zhang & Qianlin Wang & Huanwu Guo, 2023. "Optimizing Multi-Vehicle Demand-Responsive Bus Dispatching: A Real-Time Reservation-Based Approach," Sustainability, MDPI, vol. 15(7), pages 1-18, March.
    12. Lu, Chang & Wu, Yuehui & Yu, Shanchuan, 2022. "A Sample Average Approximation Approach for the Stochastic Dial-A-Ride Problem on a Multigraph with User Satisfaction," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1031-1044.
    13. Mathew, Tom V. & Khasnabis, Snehamay & Mishra, Sabyasachee, 2010. "Optimal resource allocation among transit agencies for fleet management," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(6), pages 418-432, July.
    14. Rahimi, Mahour & Amirgholy, Mahyar & Gonzales, Eric J., 2018. "System modeling of demand responsive transportation services: Evaluating cost efficiency of service and coordinated taxi usage," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 112(C), pages 66-83.
    15. Ngo, Huan Hoang & Shah, Rohan & Mishra, Sabyasachee, 2018. "Optimal asset management strategies for mixed transit fleet," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 103-116.
    16. Marković, Nikola & Kim, Myungseob (Edward) & Schonfeld, Paul, 2016. "Statistical and machine learning approach for planning dial-a-ride systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 41-55.
    17. Gonzales, Eric J., 2016. "Demand responsive transit systems with time-dependent demand: User equilibrium, system optimum, and management strategyAuthor-Name: Amirgholy, Mahyar," Transportation Research Part B: Methodological, Elsevier, vol. 92(PB), pages 234-252.
    18. Hai Wang, 2019. "Routing and Scheduling for a Last-Mile Transportation System," Service Science, INFORMS, vol. 53(1), pages 131-147, February.
    19. Neven, An & Braekers, Kris & Declercq, Katrien & Wets, Geert & Janssens, Davy & Bellemans, Tom, 2015. "Assessing the impact of different policy decisions on the resource requirements of a Demand Responsive Transport system for persons with disabilities," Transport Policy, Elsevier, vol. 44(C), pages 48-57.
    20. Di Huang & Weiping Tong & Lumeng Wang & Xun Yang, 2019. "An Analytical Model for the Many-to-One Demand Responsive Transit Systems," Sustainability, MDPI, vol. 12(1), pages 1-17, December.
    21. Kirchler, Dominik & Wolfler Calvo, Roberto, 2013. "A Granular Tabu Search algorithm for the Dial-a-Ride Problem," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 120-135.
    22. Yves Molenbruch & Kris Braekers & An Caris, 2017. "Typology and literature review for dial-a-ride problems," Annals of Operations Research, Springer, vol. 259(1), pages 295-325, December.
    23. Konrad Steiner & Stefan Irnich, 2018. "Strategic Planning for Integrated Mobility-on-Demand and Urban Public Bus Networks," Working Papers 1819, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    24. Braekers, Kris & Caris, An & Janssens, Gerrit K., 2014. "Exact and meta-heuristic approach for a general heterogeneous dial-a-ride problem with multiple depots," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 166-186.

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