固定公交与柔性公交模式选择研究

doi:10.3976/j.issn.1002-4026.2025012

Abstract

Abstract: Selecting between fixed-route and flexible-route bus systems is a critical strategy for improving the operational efficiency of urban public transit. While most existing studies focus primarily on passenger demand, this study adopts a more comprehensive perspective by incorporating demand intensity, service area size, and block size. Based on the cost structures of both fixed- and flexible-route systems, mathematical models are developed to determine the optimal vehicle capacities and departure intervals that minimize total system costs. Closed-form solutions for these optimal values are derived for both transit modes. In addition, the study identifies the conditions under which each system is preferable, and derives threshold formulas for demand intensity, service area size, and block size. Research demonstrates that the operating costs of flexible bus systems (particularly pickup/drop-off costs) and passenger walking costs in fixed-route systems are pivotal determinants in transit mode selection. These cost factors exhibit threshold-dependent effects: only when their quantitative relationship meets specific conditions do demand intensity, service area scale, and block size significantly influence mode choice. Numerical simulations validate the proposed mode selection methodology between flexible and fixed-route bus systems. The findings provide actionable decision support for real-world transit planning applications.

Key words: transit mode selection, fixed transit, flexible transit, demand intensity, service area size, block size

CLC Number:

U121

Sun Bingqi, Zhang Yong, Qin Feifei. Fixed- and flexible-route transit mode selection[J].Shandong Science, 0, (): 1-.

References

[1] 贺韵竹, 贾鹏, 李海江, 等. 新型需求响应公交发车时刻和票价优化[J]. 系统工程理论与实践, 2022, 42(4):1060-1071. DOI:10.12011/SETP2021-1149.

[2] BERRADA J, POULHÈS A. Economic and socioeconomic assessment of replacing conventional public transit with demand responsive transit services in low-to-medium density areas[J]. Transportation Research Part A: Policy and Practice, 2021, 150: 317-334. DOI: 10.1016/j.tra.2021.06.008.

[3] Pieter V, Lissa M, Dilay A, et al. A survey on demand-responsive public bus systems[J]. Transportation Research Part C, 2022, 137: 103573.

[4] CALABRÒ G, ARALDO A, OH S, et al. Adaptive transit design: Optimizing fixed and demand responsive multi-modal transportationviacontinuous approximation[J]. Transportation Research Part A: Policy and Practice, 2023, 171: 103643. DOI: 10.1016/j.tra.2023.103643.

[5] TANG X D, YANG J, LIN X, et al. Dynamic operations of an integrated mobility service system of fixed-route transits and flexible electric buses[J]. Transportation Research Part E: Logistics and Transportation Review, 2023, 173: 103081. DOI: 10.1016/j.tre.2023.103081.

[6] 搜狐网. 新区公交难等 “上来容易下去难”[EB/OL]. [2024-06-14]. https://www.sohu.com/a/232706120_394265.

[7] 束涵, 周丹旎. 浦东定制公交开了又停:“公交”的公益性和“定制”的市场化，能否调和[EB/OL]. [2024-06-14]. https://www.jfdaily.com/news/detail id=198915.

[8] 裴丰瑶. 问政回声丨乘客反映泾渭定制公交买票难交通部门：将增配运力[EB/OL]. [2024-06-14]. https://www.sohu.com/a/232706120_394265.

[9] QUADRIFOGLIO L, LI X G. A methodology to derive the critical demand density for designing and operating feeder transit services[J]. Transportation Research Part B: Methodological, 2009, 43(10): 922-935. DOI: 10.1016/j.trb.2009.04.003.

[10] LI X G, QUADRIFOGLIO L. Feeder transit services: Choosing between fixed and demand responsive policy[J]. Transportation Research Part C: Emerging Technologies, 2010, 18(5): 770-780. DOI: 10.1016/j.trc.2009.05.015.

[11] NOURBAKHSH S M, OUYANG Y F. A structured flexible transit system for low demand areas[J]. Transportation Research Part B: Methodological, 2012, 46(1): 204-216. DOI: 10.1016/j.trb.2011.07.014.

[12] PETIT A, OUYANG Y F. Design of heterogeneous flexible-route public transportation networks under low demand[J]. Transportation Research Part C: Emerging Technologies, 2022, 138: 103612. DOI: 10.1016/j.trc.2022.103612.

[13] KIM M (, LEVY J, SCHONFELD P. Optimal zone sizes and headways for flexible-route bus services[J]. Transportation Research Part B: Methodological, 2019, 130: 67-81. DOI: 10.1016/j.trb.2019.10.006.

[14] QIU F, SHEN J X, ZHANG X C, et al. Demi-flexible operating policies to promote the performance of public transit in low-demand areas[J]. Transportation Research Part A: Policy and Practice, 2015, 80: 215-230. DOI: 10.1016/j.tra.2015.08.003.

[15] ATASOY B, IKEDA T, SONG X, et al. The concept and impact analysis of a flexible mobility on demand system[J]. Transportation Research Part C: Emerging Technologies, 2015, 56: 373-392. DOI: 10.1016/j.trc.2015.04.009.

[16] MISHRA S, MEHRAN B. Cost analysis of different vehicle technologies for semi-flexible transit operations[J]. Transportation Research Part D: Transport and Environment, 2024, 130: 104159. DOI: 10.1016/j.trd.2024.104159.

[17] CALABRÒ G, LE PIRA M, GIUFFRIDA N, et al. Designing demand responsive transport services in small-sized cities using an agent-based model[J]. Transportation Research Procedia, 2023, 69: 759-766. DOI: 10.1016/j.trpro.2023.02.233.

[18] LEE E, CEN X K, LO H K, et al. Designing zonal-based flexible bus services under stochastic demand[J]. Transportation Science, 2021, 55(6): 1280-1299. DOI: 10.1287/trsc.2021.1054.

[19] FU L P. Planning and design of flex-route transit services[J]. Transportation Research Record: Journal of the Transportation Research Board, 2002, 1791(1): 59-66. DOI: 10.3141/1791-09.

[20] LI X, QUADRIFOGLIO L. Optimal zone design for feeder transit services[J]. Transportation Research Record, 2009, 2111(1): 100-108.

[21] SHI H G, GAO M Y. Analysis of a flexible transit network in a radial street pattern[J]. Journal of Advanced Transportation, 2020, 2020(1): 5379218. DOI: 10.1155/2020/5379218.

[22] BADIA H, ESTRADA M, ROBUSTÉ F. Competitive transit network design in cities with radial street patterns[J]. Transportation Research Part B: Methodological, 2014, 59: 161-181. DOI: 10.1016/j.trb.2013.11.006.

[23] ZHAO J M, DESSOUKY M. Service capacity design problems for mobility allowance shuttle transit systems[J]. Transportation Research Part B: Methodological, 2008, 42(2): 135-146. DOI: 10.1016/j.trb.2007.07.002.

[24] SMITH B, DEMETSKY M, DURVASULA P. A multiobjective optimization model for flexroute transit service design[J]. Journal of Public Transportation, 2003, 6(1): 81-100. DOI: 10.5038/2375-0901.6.1.5.

[25] HESS P M, VERNEZ MOUDON A, CATHERINE SNYDER M, et al. Site design and pedestrian travel[J]. Transportation Research Record: Journal of the Transportation Research Board, 1999, 1674(1): 9-19. DOI: 10.3141/1674-02.

[26] EWING R, CERVERO R. Travel and the built environment[J]. Journal of the American Planning Association, 2010, 76(3): 265-294. DOI: 10.1080/01944361003766766.

[27] JACOBS J. The death and life of great American Cities[M]. [New York]: Random House, 1961.

[28] SANGVERAPHUNSIRI T, CASSIDY M J, DAGANZO C F. Jitney-lite: A flexible-route feeder service for developing countries[J]. Transportation Research Part B: Methodological, 2022, 156: 1-13. DOI: 10.1016/j.trb.2021.12.015.

[29] 刘鹏, 李成, 崔丁松, 等. 纯电动城市客车实际能耗及影响因素大数据分析[J]. 客车技术与研究, 2022, 44(1): 56-59.

[30] 郭晓俊. 基于需求响应的实时定制公交系统研究[D]. 北京: 北京交通大学, 2016.

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Fixed- and flexible-route transit mode selection

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