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Maximizing net benefits for conventional and flexible bus services

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  • Kim, Myungseob (Edward)
  • Schonfeld, Paul

Abstract

Transit ridership is usually sensitive to fares, travel times, waiting times, and access times, among other factors. Therefore, the elasticities of demand with respect to such factors should be considered in modeling bus transit services and must be considered when maximizing net benefits (i.e. “system welfare”=consumer surplus+producer surplus) rather just minimizing costs. In this paper welfare is maximized with elastic demand relations for both conventional (fixed route) and flexible-route services in systems with multiple dissimilar regions and periods. As maximum welfare formulations are usually too complex for exact solutions, they have only been used in a few studies focused on conventional transit services. This limitation is overcome here for both conventional and flexible transit services by using a Real Coded Genetic Algorithm to solve such mixed integer nonlinear welfare maximization problems with constraints on capacities and subsidies. The optimized variables include service type, zone sizes, headways and fares. We also determine the maximum welfare threshold between optimized conventional and flexible services) and explore the effects of subsidies. The proposed planning models should be useful in selecting the service type and optimizing other service characteristics based on local geographic characteristics and financial constraints.

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  • Kim, Myungseob (Edward) & Schonfeld, Paul, 2015. "Maximizing net benefits for conventional and flexible bus services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 116-133.
    • Handle: RePEc:eee:transa:v:80:y:2015:i:c:p:116-133
    DOI: 10.1016/j.tra.2015.07.016
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    Cited by:

    1. Becker, Henrik & Balac, Milos & Ciari, Francesco & Axhausen, Kay W., 2020. "Assessing the welfare impacts of Shared Mobility and Mobility as a Service (MaaS)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 228-243.
    2. Wu, Jiaming & Kulcsár, Balázs & Selpi, & Qu, Xiaobo, 2021. "A modular, adaptive, and autonomous transit system (MAATS): A in-motion transfer strategy and performance evaluation in urban grid transit networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 81-98.
    3. Sayarshad, Hamid R. & Gao, H. Oliver, 2020. "Optimizing dynamic switching between fixed and flexible transit services with an idle-vehicle relocation strategy and reductions in emissions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 198-214.
    4. Jin Zhang & Wenquan Li & Guoqing Wang & Jingcai Yu, 2021. "Feasibility Study of Transferring Shared Bicycle Users with Commuting Demand to Flex-Route Transit—A Case Study of Nanjing City, China," Sustainability, MDPI, vol. 13(11), pages 1-21, May.
    5. Lee, Enoch & Cen, Xuekai & Lo, Hong K., 2021. "Zonal-based flexible bus service under elastic stochastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    6. Xudong Li & Zhongzhen Yang & Feng Lian, 2023. "Optimizing On-Demand Bus Services for Remote Areas," Sustainability, MDPI, vol. 15(9), pages 1-20, April.
    7. Edward Kim, M. & Schonfeld, Paul & Roche, Austin & Raleigh, Chelsie, 2022. "Optimal service zones and frequencies for flexible-route freight deliveries," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 182-199.
    8. (Edward) Kim, Myungseob & Levy, Joshua & Schonfeld, Paul, 2019. "Optimal zone sizes and headways for flexible-route bus services," Transportation Research Part B: Methodological, Elsevier, vol. 130(C), pages 67-81.
    9. Lee, Enoch & Cen, Xuekai & Lo, Hong K., 2022. "Scheduling zonal-based flexible bus service under dynamic stochastic demand and Time-dependent travel time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    10. Fenling Feng & Tianzuo Zhang & Chengguang Liu & Lifeng Fan, 2020. "China Railway Express Subsidy Model Based on Game Theory under “the Belt and Road” Initiative," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    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. Shang, Huayan & Chang, Yi & Huang, Haijun & Zhao, Fangxia, 2022. "Integration of conventional and customized bus services: An empirical study in Beijing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).

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