Static and Dynamic Scheduling Method of Demand-Responsive Feeder Transit for High-Speed Railway Hub Area
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 11
Abstract
Demand-responsive feeder transit (DRFT) is an emerging urban public transport mode with the advantage of offering flexible door-to-door services in the high-speed railway hub area. However, the existing bus scheduling schemes can hardly meet personalized and diversified passenger transfer demands in the station-city integrated high-speed railway hub, reducing the attractiveness of DRFT. This paper studies the DRFT scheduling problem considering static and dynamic travel demands under the background of mobility as a service (MaaS). An information-based DRFT system framework is proposed, where the K-means clustering algorithm is implemented to select target bus stops from regional road networks for passengers to get on and off. A two-stage mixed integer programming model is first formulated to generate operational routes and optimize the static and dynamic scheduling before and after departure. The objective functions reflect the operating benefits of public transport enterprises and the travel costs of passengers, and the demand characteristics in different driving directions are taken into account in the model. Then, an improved genetic algorithm is developed to solve the model, which is called the genetic algorithm-exact algorithm (GA-EA) in this paper. Finally, the proposed model and algorithm are evaluated using the case study of the Nanjingnan Railway Station area. The experiment results show that the optimal scheme can provide a 100% demand-response rate, reasonable service time, and valid driving routes. In addition, compared with GA, the average search time of GA-EA is shortened by 43.5% and the total objective function value is increased by 2.16%. The findings in this paper can provide practical guidance on DRFT scheduling and improve the efficiency of bus feeder service.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was supported by the National Natural Science Foundation of China (52072066) and Jiangsu Province Science Fund for Distinguished Young Scholars (BK20200014). The authors are grateful to the anonymous reviewers for providing helpful suggestions for the study.
References
Amirgholy, M., and E. J. Gonzales. 2016. “Demand responsive transit systems with time-dependent demand: User equilibrium, system optimum, and management strategy.” Transp. Res. Part B: Methodol. 92 (Part B): 234–252. https://doi.org/10.1016/j.trb.2015.11.006.
Avermann, N., and J. Schlüter. 2019. “Determinants of customer satisfaction with a true door-to-door DRT service in rural Germany.” Res. Transp. Bus. Manage. 32 (Sep): 100420. https://doi.org/10.1016/j.rtbm.2019.100420.
Boyer, V., O. J. Ibarra-Rojas, and Y. Á. Ríos-Solís. 2018. “Vehicle and crew scheduling for flexible bus transportation systems.” Transp. Res. Part B: Methodol. 112 (Jun): 216–229. https://doi.org/10.1016/j.trb.2018.04.008.
Cao, Z. C., A. Ceder, D. W. Li, and S. L. Zhang. 2019. “Optimal synchronization and coordination of actual passenger-rail timetables.” J. Intell. Transp. Syst. 23 (3): 231–249. https://doi.org/10.1080/15472450.2018.1488132.
Chakraborty, A., and M. Chakraborty. 2010. “Cost-time minimization in a transportation problem with fuzzy parameters: A case study.” J. Transp. Syst. Eng. Inf. Technol. 10 (6): 53–63. https://doi.org/10.1016/S1570-6672(09)60071-4.
Chen, C. L., L. S. Anastasia, J. M. de Ureña, and R. Vickerman. 2019. “Spatial short and long-term implications and planning challenges of high-speed rail: A literature review framework for the special issue.” Eur. Plann. Stud. 27 (3): 415–433. https://doi.org/10.1080/09654313.2018.1562658.
Chen, X., Y. H. Wang, and X. L. Ma. 2021. “Integrated optimization for commuting customized bus stop planning, routing design, and timetable development with passenger spatial-temporal accessibility.” IEEE Trans. Intell. Transp. 22 (4): 2060–2075. https://doi.org/10.1109/TITS.2020.3048520.
Dai, Y. Z., and C. Y. Zhang. 2022. “Comparative study on characteristics of urban road network in station catchment area between China and other countries for station-city integration.” J. Adv. Transp. 2022 (May): 1910404. https://doi.org/10.1155/2022/1910404.
Dakic, I., K. D. Yang, M. Menendez, and Y. J. Chow Joseph. 2021. “On the design of an optimal flexible bus dispatching system with modular bus units: Using the three-dimensional macroscopic fundamental diagram.” Transp. Res. Part B: Methodol. 148 (Jun): 38–59. https://doi.org/10.1016/j.trb.2021.04.005.
Dario, M., K. Srećko, J. Igor, and B. Rino. 2021. “Adriatic sea hub ports feeder service optimization using multi-criteria decision-making methods.” Sustainability 13 (21): 12325. https://doi.org/10.3390/su132112325.
David, L., B. Wilco, J. Erik, and C. Oded. 2021. “Simulation of fixed versus on-demand station-based feeder operations.” Transp. Res. Part C: Emerging Technol. 132 (Nov): 103401. https://doi.org/10.1016/j.trc.2021.103401.
Franco, P., R. Johnston, and E. McCormick. 2019. “Demand responsive transport: Generation of activity patterns from mobile phone network data to support the operation of new mobility services.” Transp. Res. Part A: Policy Pract. 131 (C): 244–266. https://doi.org/10.1016/j.tra.2019.09.038.
Gong, M. L., Y. C. Hu, Z. W. Chen, and X. P. Li. 2021. “Transfer-based customized modular bus system design with passenger-route assignment optimization.” Transp. Res. Part E: Logist. Transp. Rev. 153 (Sep): 102422. https://doi.org/10.1016/j.tre.2021.102422.
Hansen, T. 2018. “Analysis of paratransit feeder-service pilot: Projected versus actual ridership and cost-benefit results.” Transp. Res. Rec. 2672 (8): 629–638. https://doi.org/10.1177/0361198118794537.
Hensher, D. A. 2017. “Future bus transport contracts under a mobility as a service (MaaS) regime in the digital age: Are they likely to change?” Transp. Res. Part A: Policy Pract. 98 (Apr): 86–96. https://doi.org/10.1016/j.tra.2017.02.006.
Huang, A. L., Z. Q. Dou, L. Z. Qi, and L. W. Wang. 2020. “Flexible route optimization for demand-responsive public transit service.” J. Transp. Eng. Part A. Syst. 146 (12): 04020132. https://doi.org/10.1061/JTEPBS.0000448.
Jaâfar, B., and P. Alexis. 2021. “Economic and socioeconomic assessment of replacing conventional public transit with demand responsive transit services in low-to-medium density areas.” Transp. Res. Part A: Policy Pract. 150 (Aug): 317–334. https://doi.org/10.1016/j.tra.2021.06.008.
Jiang, D. C., and M. Zhou. 2017. “A comparative study of insertion sorting algorithm verification.” In Proc., 2017 IEEE 2nd Information Technology, 356–360. New York: IEEE.
Jittrapirom, P., W. V. Neerven, K. Martens, D. Trampe, and H. Meurs. 2019. “The Dutch elderly’s preferences toward a smart demand-responsive transport service.” Res. Transp. Bus. Manage. 30 (C): 100383. https://doi.org/10.1016/j.rtbm.2019.100383.
Joy, D. C., and A. K. Miller. 2021. “Mobility as a service operating model to enable public policy.” Transp. Res. Rec. 2675 (11): 141–149. https://doi.org/10.1177/03611981211026664.
Kim, M., and P. Schonfeld. 2015. “Maximizing net benefits for conventional and flexible bus services.” Transp. Res. Part A: Policy Pract. 80 (Oct): 116–133. https://doi.org/10.1016/j.tra.2015.07.016.
Leffler, D., W. Burghout, O. Cats, and E. Jenelius. 2020. “Distribution of passenger costs in fixed versus flexible station-based feeder services.” Transp. Res. Procedia 47 (C): 179–186. https://doi.org/10.1016/j.trpro.2020.03.077.
Li, Z. C., and S. Dian. 2016. “Forecasting passenger travel demand for air and high-speed rail integration service: A case study of Beijing-Guangzhou corridor, China.” Transp. Res. Part A: Policy Pract. 94 (Part A): 397–410. https://doi.org/10.1016/j.tra.2016.10.002.
Liu, T., and A. Ceder. 2015. “Analysis of a new public-transport-service concept: Customized bus in China.” Transp. Policy 39 (Apr): 63–76. https://doi.org/10.1016/j.tranpol.2015.02.004.
Liu, T., A. Ceder, R. Bologna, and B. Cabantous. 2016. “Commuting by customized bus: A comparative analysis with private car and conventional public transport in two cities.” J. Publ. Transp. 19 (2): 55–74. https://doi.org/10.5038/2375-0901.19.2.4.
Liu, Y. N., and Y. F. Ouyang. 2021. “Mobility service design via joint optimization of transit networks and demand-responsive services.” Transp. Res. Part B: Methodol. 151 (Sep): 22–41. https://doi.org/10.1016/j.trb.2021.06.005.
Lukas, K., and S. J. Christian. 2021. “The attitude of potentially less mobile people towards demand responsive transport in a rural area in central Germany.” J. Transp. Geogr. 96 (Oct): 103202. https://doi.org/10.1016/j.jtrangeo.2021.103202.
Lyu, Y., C. Y. Chow, V. C. S. Lee, J. K. Y. Ng, Y. H. Li, and J. Zeng. 2019. “CB-Planner: A bus line planning framework for customized bus systems.” Transp. Res. Part C: Emerging Technol. 101 (Apr): 233–253. https://doi.org/10.1016/j.trc.2019.02.006.
Ma, C. X., C. Wang, and X. C. Xu. 2021. “A multi-objective robust optimization model for customized bus routes.” IEEE Trans. Intell. Transp. 22 (4): 2359–2370. https://doi.org/10.1109/TITS.2020.3012144.
Marta, T., D. T. Galvão, and F. Tânia. 2021. “A multi objective approach for DRT service using tabu search.” Transp. Res. Procedia 52 (Jan): 91–98. https://doi.org/10.1016/j.trpro.2021.01.092.
Militão, A. M., and T. Alejandro. 2021. “Optimal fleet size for a shared demand-responsive transport system with human-driven vs automated vehicles: A total cost minimization approach.” Transp. Res. Part A: Policy Pract. 151 (Sep): 52–80. https://doi.org/10.1016/j.tra.2021.07.004.
Monzon, A., A. Andrea, and L. L. Maria. 2017. “Joint analysis of intermodal long distance-last mile trips using urban interchanges in EU cities.” Transp. Res. Procedia 27 (Jan): 1074–1079. https://doi.org/10.1016/j.trpro.2017.12.133.
Najma, S., K. Alamgir, M. W. Khan, A. Sharifah, A. M. Sanaa, and S. Kamal. 2021. “On the Properties of the new generalized Pareto distribution and its applications.” Math. Probl. Eng. 2021 (Dec): 6855652. https://doi.org/10.1155/2021/6855652.
Park, C., J. Lee, and S. Y. Sohn. 2019. “Recommendation of feeder bus routes using neural network embedding-based optimization.” Transp. Res. Part A: Policy Pract. 126 (Aug): 329–341. https://doi.org/10.1016/j.tra.2019.05.005.
Pei, M. Y., P. Q. Lin, J. Du, X. P. Li, and Z. W. Chen. 2021. “Vehicle dispatching in modular transit networks: A mixed-integer nonlinear programming model.” Transp. Res. Part E: Logist. Transp. Rev. 147 (Mar): 102240. https://doi.org/10.1016/j.tre.2021.102240.
Perugia, A., L. Moccia, J. F. Cordeau, and G. Laporte. 2011. “Designing a home-to-work bus service in a metropolitan area.” Transp. Res. Part B: Methodol. 45 (10): 1710–1726. https://doi.org/10.1016/j.trb.2011.05.025.
Sangveraphunsiri, T., M. J. Cassidy, and C. F. Daganzo. 2022. “Jitney-lite: A flexible-route feeder service for developing countries.” Transp. Res. Part B: Methodol. 156 (Feb): 1–13. https://doi.org/10.1016/j.trb.2021.12.015.
Shen, C., Y. Sun, Z. J. Bai, and H. J. Cui. 2021. “Real-time customized bus routes design with optimal passenger and vehicle matching based on column generation algorithm.” Physica A 571 (Jun): 125836. https://doi.org/10.1016/j.physa.2021.125836.
Sörensen, L., A. Bossert, J. P. Jokinen, and J. Schlüter. 2021. “How much flexibility does rural public transport need?–Implications from a fully flexible DRT system.” Transp. Policy 100 (Jan): 5–20. https://doi.org/10.1016/j.tranpol.2020.09.005.
Wang, A., H. Z. Guan, P. F. Wang, L. Q. Peng, and Y. Q. Xue. 2021. “Cross-regional customized bus route planning considering staggered commuting during the COVID-19.” IEEE Access 9 (Jan): 20208–20222. https://doi.org/10.1109/ACCESS.2021.3053351.
Wang, C., and C. X. Ma. 2020. “Multi-objective optimization of customized bus routes based on full operation process.” Mod. Phys. Lett. B 34 (25): 27. https://doi.org/10.1142/S0217984920502668.
Wang, J. B., T. Yamamoto, and K. Liu. 2020. “Key determinants and heterogeneous frailties in passenger loyalty toward customized buses: An empirical investigation of the subscription termination hazard of users.” Transp. Res. Part C: Emerging Technol. 115 (C): 102636. https://doi.org/10.1016/j.trc.2020.102636.
Wei, M., T. Liu, and B. Sun. 2021. “Optimal routing design of feeder transit with stop selection using aggregated cell phone data and open source GIS tool.” IEEE Trans. Intell. Transp. 22 (4): 2452–2463. https://doi.org/10.1109/TITS.2020.3042014.
Wong, Y. Z., D. A. Hensher, and C. Mulley. 2019. “Mobility as a service (MaaS): Charting a future context.” Transp. Res. Part A: Policy Pract. 131 (C): 5–19. https://doi.org/10.1016/j.tra.2019.09.030.
Wu, M., C. H. Yu, W. J. Ma, K. An, and Z. H. Zhong. 2022. “Joint optimization of timetabling, vehicle scheduling, and ride-matching in a flexible multi-type shuttle bus system.” Transp. Res. Part C: Emerging Technol. 139 (Jun): 103657. https://doi.org/10.1016/j.trc.2022.103657.
Xue, S. Q., R. Song, S. W. He, J. Y. An, and Y. M. Wang. 2022. “An improved adaptive large neighborhood search algorithm for the heterogeneous customized bus service with multiple pickup and delivery candidate locations.” J. Adv. Transp. 2022 (May): 1679469. https://doi.org/10.1155/2022/1679469.
Yang, L. X., Z. Di, M. M. Dessouky, Z. Y. Gao, and J. G. Shi. 2020. “Collaborative optimization of last-train timetables with accessibility: A space-time network design based approach.” Transp. Res. Part C: Emerging Technol. 114 (C): 572–597. https://doi.org/10.1016/j.trc.2020.02.022.
Yin, W. C., S. W. He, H. D. Li, and B. S. He. 2016. “Optimal feeder service plan design of cargo express train in railway hub.” Int. J. Modell. Simul. 36 (3): 79–86. https://doi.org/10.1080/02286203.2016.1186585.
Yu, Q., W. F. Li, H. R. Zhang, and D. Y. Yang. 2020. “Mobile phone data in urban customized bus: A network-based hierarchical location selection method with an application to system layout design in the urban agglomeration.” Sustainability 12 (15): 6203. https://doi.org/10.3390/su12156203.
Zacharias, J., T. X. Zhang, and N. Nakajima. 2011. “Tokyo station city: The railway station as urban place.” Urban Des. Int. 16 (4): 242–251. https://doi.org/10.1057/udi.2011.15.
Zhang, B., Z. S. Zhong, Z. Sang, M. Y. Zhang, and Y. Q. Xue. 2021. “Two-level planning of customized bus routes based on uncertainty theory.” Sustainability 13 (20): 11418. https://doi.org/10.3390/su132011418.
Zhang, F. N., and W. Liu. 2020. “Responsive bus dispatching strategy in a multi-modal and multi-directional transportation system: A doubly dynamical approach.” Transp. Res. Part C: Emerging Technol. 113 (Jan): 21–37. https://doi.org/10.1016/j.trc.2019.04.005.
Zhang, M. Y., M. Yang, Y. Li, J. X. Chen, and D. Lei. 2023. “Optimal electric bus scheduling with multiple vehicle types considering bus crowding degree.” J. Transp. Eng. Part A: Syst. 149 (2): 04022138. https://doi.org/10.1061/JTEPBS.TEENG-7518.
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Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 11 • November 2023
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© 2023 American Society of Civil Engineers.
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Received: Dec 5, 2022
Accepted: Jul 27, 2023
Published online: Sep 14, 2023
Published in print: Nov 1, 2023
Discussion open until: Feb 14, 2024
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