Designing Customised Bus Routes for Urban Commuters with the Existence of Multimodal Network – A Bi-Level Programming Approach

  • Cheng Cheng Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University https://orcid.org/0000-0002-4367-0376
  • Tianzuo Wang Urban Mobility Institute, Tongji University https://orcid.org/0000-0003-4808-6811
  • Wei Wang Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University
  • Junqiang Ding Beijing University of Civil, Engineering and Architecture
Keywords: bus network design, customised bus, bi-level programming, genetic algorithm, metro transaction data

Abstract

Customised bus (CB) is a cutting-edge mean of transportation and has been implemented worldwide. To support the spread of the CB system, methodologies for CB network design have been conducted. However, a majority of them cannot be adopted directly for multi-modal transportation environment. In this paper, we proposed a bi-level programming model to fill this gap. The upper-level problem is to maximise the usage of the CB system with the limitation of operation constraints. Meanwhile, the lower-level problem is to capture the traveller’s choice by minimising traveller’s generalised cost during travel. A solving procedure via genetic algorithm is further proposed and validated via the metro data at Shanghai. The results indicated that the proposed CB route network would attract nearly 5,000 users during morning peak period under the given metro transaction data. We further studied the features of the selected routes and found that the CB network mainly served residence to commercial or industrial parks travellers and would provide travel service with fewer stops, and higher travel efficiency by travelling through expressway.

References

Liu T, Ceder A. Analysis of a new public-transport-service concept: Customized bus in China. Transport Policy. 2015;39: 63-76. doi: 10.1016/j.tranpol.2015.02.004.

Li J, Lv Y, Ma J, Ouyang Q. Methodology for extracting potential customized bus routes based on bus smart card data. Energies. 2018;11(9): 1-15. doi: 10.3390/en11092224.

Liu T, Ceder A, Bologna R, Cabantous B. Commuting by customized bus: A comparative analysis with private car and conventional public transport in two cities. Journal of Public Transportation. 2016;19(2): 55-74. doi: 10.5038/2375-0901.19.2.4.

Zhang J, Wang D, Meng M. Analyzing customized bus service on a multimodal travel corridor: An analytical modeling approach. Journal of Transportation Engineering, Part A: Systems. 2017;143(11): 04017057. doi: 10.1061/JTEPBS.0000087.

Zhang J, Wang D, Meng M. Which service is better on a linear travel corridor: Park & ride or on-demand public bus?. Transportation Research Part A: Policy and Practice. 2018;118: 803-818. doi: 10.1016/j.tra.2018.10.003.

Tong L, Zhou L, Liu J, Zhou X. Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing. Transportation Research Part C: Emerging Technologies. 2017;85: 451-475. doi: 10.1016/j.trc.2017.09.022.

Li Z, Song R, He S, Bi M. Methodology of mixed load customized bus lines and adjustment based on time windows. PloS ONE. 2018;13(1): 0189763. doi: 10.1371/journal.pone.0189763.

Qiu G, et al. Clustering passenger trip data for the potential passenger investigation and line design of customized commuter bus. IEEE Transactions on Intelligent Transportation Systems. 2019;20(9): 3351-3360. doi: 10.1109/TITS.2018.2875466.

Cen J, Ye Q, Chen X, Zhang H. Inferring irregular demand of last-mile service from taxi origin-destination data. Proceedings of Transportation Research Board 96th Annual Meeting, 8-12 Jan. 2017, Washington DC, United States. 2017. p. 1-15.

Qian Y, Cheng C, Li X, Wang W. Identifying potential point-to-point customized bus routes via smart card transaction data and open source travel time data. Proceedings of the 26th ITS World Congress, 21-25 Oct. 2019, Singapore. 2019. p. 1-10.

Stopková M, Stopka O, Klapita V. Modeling the distribution network applying the principles of linear programming. 21st International Scientific on Conference Transport Means 2017, 20-22 Sep. 2017, Juodkrante, Lithuania. 2017. p. 73-77.

Stopka O, Čejka J, Kampf R, Bartuška L. Draft of the novel system of public bus transport lines in the particular territory. Proceedings of 19th International Conference. Transport Means 2015, Kaunas, Lithuania. 2015. p. 39-42.

Guo R, Guan W, Zhang W. Route design problem of customized buses: Mixed integer programming model and case study. Journal of Transportation Engineering, Part A: Systems. 2018;144(11): 04018069. doi: 10.1061/JTEPBS.0000185.

Ma J, et al. A model for the stop planning and timetables of customized buses. PloS ONE. 2007;12(1): 0168762. doi: 10.1371/journal.pone.0168762.

Cao Y, Wang J. An optimization method of passenger assignment for customized bus. Mathematical Problems in Engineering. 2017;2017: 7914753. doi: 10.1155/2017/7914753.

Ma J, et al. Large-scale demand driven design of a customized bus network: A methodological framework and Beijing case study. Journal of Advanced Transportation. 2017;2017: 3865701. doi: 10.1155/2017/3865701.

Lyu Y, et al. CB-Planner: A bus line planning framework for customized bus systems. Transportation Research Part C: Emerging Technologies. 2019;101: 233-253. doi: 10.1016/j.trc.2019.02.006.

Zhao Z, Chen Y, An S. Redistribution of revenue from road congestion pricing based on the improvement of public transport services. Zhongguo Tiedao Kexue/China Railway Science. 2009;30(2): 131-136.

Yu Y, Machemehl R, Xie C. Demand-responsive transit circulator service network design. Transportation Research Part E: Logistics and Transportation Review. 2015;76: 160-175. doi: 10.1016/j.tre.2015.02.009.

Oliveira S, et al. Design of a bike-bus network for a city of half a million citizens. Journal of Urban Planning and Development. 2021;147(3): 04021029. doi: 10.1061/(ASCE)UP.1943-5444.0000709.

Sinha A, Malo P, Deb K. A review on bilevel optimization: From classical to evolutionary approaches and applications. IEEE Transactions on Evolutionary Computation. 2017;22(2): 276-295. doi: 10.1109/TEVC.2017.2712906.

Chen Q. An optimization model for the selection of bus-only lanes in a city. PLoS ONE. 2015;10(7): 1-12. doi: 10.1371/journal.pone.0133951.

Cheng C, et al. Designing a time limited–parking management plan for large-scale parking lots. Journal of Transportation Engineering. 2018;144(7): 04018027. doi: 10.1061/JTEPBS.0000153.

Gaode. Gaode open platform. https://lbs.amap.com/ [Accessed 20th Jan. 2020].

Dijkstra E. A note on two problems in connexion with graphs. Numerische Mathematic. 1959;1(1): 269-271.

Shanghai Shentong Metro Group Operations Management Department. Metro micro data. http://weibo.com/1742987497/CFthkl8i7 [Accessed 2nd Nov. 2015].

Saidi S, et al. Planning urban ring rail transit lines: Case study of Shanghai, China. Transportation Research Record. 2016;2540(1): 56-65. doi: 10.3141/2540-07.

Shanghai Metro. http://service.shmetro.com/en/ [Accessed 31st July 2019].

Fang D, et al. Estimation method of crowding cost in urban rail transit carriages. Journal of Traffic and Transportation Engineering. 2018;6(18): 121-130.

Published
2022-06-15
How to Cite
1.
Cheng C, Wang T, Wang W, Ding J. Designing Customised Bus Routes for Urban Commuters with the Existence of Multimodal Network – A Bi-Level Programming Approach. Promet [Internet]. 2022Jun.15 [cited 2022Nov.29];34(3):487-98. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3980
Section
Articles