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可变线路公交可以在一定距离内偏离固定线路接送乘客，具有提高公共交通服务可达性的优势。然而，公交车过多访问非站点处的乘客，会增加车辆的行驶距离与时间，导致车内和站点等候乘客时间延误。因此，本文提出以浮动票价来调控乘客的接送需求，优化可变线路公交的固定站点个数、发车间隔和基础票价以实现单位时间内的社会福利（运营商利润和消费者剩余之和）最大化。首先，本文利用连续近似法分别在无财政限制（允许盈利赤字）和有财政限制（在财政补贴下确保运营不亏损）的情况下构建了最优化模型；其次，通过BONMIN求解器，求出无财政限制的模型的最优固定站点个数、发车间隔和基础票价；最后，通过对潜在出行需求、服务区域的矩形长宽比、票价调整系数及车容量等进行灵敏度分析，验证了模型的有效性。结果表明，单位时间财政补贴存在两个阈值。实例分析表明，对洛杉矶MTA 646路夜间公交分别实行统一票价和差异化票价时，潜在出行需求存在一个阈值。当潜在出行需求超过120人/h时，票价调整系数为1时对单位时间社会福利的优化度比票价调整系数为0.2、0.4、0.6和0.8时更高，约为1%。本研究为可变线路公交系统的服务设计与财政补贴计算提供了重要参考依据。
Abstract：Flex-route transit, which allow vehicles to deviate from the fixed route to pick up and drop off passengers, offer greater accessibility compared to traditional buses. However, excessive deviations to non-stops increase the distance and travel time of the vehicle, resulting in delays for passengers both on board and waiting at stops. To regulate this demand, a variable fare for pick-up and drop-off requests at non station locations is proposed in this study. The number of fixed stops, headways, and base fares of a flex-route transit system is optimized to maximize the social welfare(the sum of the operator's profit and consumer surplus) per unit time. First, an optimization model is constructed by analyzing the operation process of flex-route transit using the continuous approximation method without financial constraints(allowing profit deficit) or with financial constraints(ensuring no losses in operations under financial subsidies). Second, the optimal number of fixed stops, headway, and base fare for the model without financial constraints are determined using the BONMIN solver. Finally, the model is verified through sensitivity analyses of the potential travel demand,shape of the service area, fare adjustment factor, and vehicle capacity. The results indicate the existence of two thresholds for financial subsidies per unit time. The empirical analysis,focusing on the Los Angeles MTA 646 night bus service, shows that there is a threshold for potential travel demand under uniform and differentiated fares. When the potential travel demand exceeds 120 p/h, the optimization of social welfare per unit time is higher at a fare adjustment factor of one, compared to fare adjustment factors of 0.2, 0.4, 0.6, and 0.8, which is nearly 1%. This study provides a useful reference for optimizing the flex-route transit service design and calculating the required financial subsidies.
[1] LI J, HE Z, ZHONG J. The multi-type demands oriented framework for flex-route transit design[J]. Sustainability,2022, 14(15):9727.
[2] SHAHIN R, HOSTEINS P, PELLEGRINI P, et al. A survey of Flex-Route Transit problem and its link with Vehicle Routing Problem[J]. Transportation Research Part C:Emerging Technologies, 2024, 158:104437.
[3] Potts J F, Marshall M A, Crockett E C, et al. A guide for planning and operating flexible public transportation services[R]. Washington D. C.:Transportation Research Board, 2010.
[4] QIU F, LI W, ZHANG J. A dynamic station strategy to improve the performance of flex-route transit services[J].Transportation Research Part C:Emerging Technologies,2014, 48:229-240.
[5] NOURBAKHSH S M, OUYANG Y. A structured flexible transit system for low demand areas[J]. Transportation Research Part B:Methodological, 2012, 46(1):204-216.
[6] ZHENG Y, LI W, QIU F, et al. The benefits of introducing meeting points into flex-route transit services[J]. Transportation Research Part C:Emerging Technologies, 2019,106:98-112.
[7] QIU F, SHEN J, ZHANG X, 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.
[8] LI M, TANG J, ZENG J, et al. A Kriging-based optimization method for meeting point locations to enhance flexroute transit services[J]. Transportmetrica B:Transport Dynamics, 2023, 11(1):1281-1310.
[9] ZHANG J, LI W, ZHENG Y, et al. Dynamic clustering meeting points strategy to improve operational service capability of flex-route transit[J]. Journal of Transportation Engineering, Part A:Systems, 2023, 149(6):04023038.
[10] ZHENG Y, LI W, QIU F. A slack arrival strategy to promote flex-route transit services[J]. Transportation Research Part C:Emerging Technologies, 2018, 92:442-455.
[11]刘梦琪，瞿何舟.基于轨道交通与常规公交组合的出行路径选择研究[J].交通运输工程与信息学报, 2018,16(4):63-68.LIU Mengqi, QU Hezhou. Study on the route choice using the combination of rail and bus transit[J]. Journal of Transportation Engineering and Information, 2018, 16(4):63-68.
[12]杨西宁，邓琼华，杨硕.建成环境对居民出行方式选择影响效应的异质性研究[J].交通运输工程与信息学报，2019, 17(2):128-137.YANG Xining, DENG Qionghua, YANG Shuo. Investigation of heterogeneous effects of built environment on a household member’s travel mode choice[J]. Journal of Transportation Engineering and Information, 2019, 17(2):128-137.
[13] CHAVIS C, GAYAH V V. Development of a mode choice model for general purpose flexible-route transit systems[J]. Transportation Research Record:Journal of the Transportation Research Board, 2017, 2650(1):133-141.
[14]于晓桦，刘欣萍，毕亚茹，等.基于出行效用无差异阈值的组合交通客流分配模型[J].交通运输工程与信息学报，2023, 21(4):25-34.YU Xiaohua, LIU Xinping, BI Yaru, et al. Passenger flow distribution model for combined transportation modes based on travel utility indifference threshold[J].Journal of Transportation Engineering and Information,2023, 21(4):25-34.
[15] QIU F, LI W, HAGHANI A. An exploration of the demand limit for flex-route as feeder transit services:a case study in Salt Lake City[J]. Public Transport, 2015,7:259-276.
[16] QIU F, LI W, HAGHANI A. A methodology for choosing between fixed-route and flex-route policies for transit services[J]. Journal of Advanced Transportation,2015, 49(3):496-509.
[17] ZHENG Y, LI W, QIU F, et al. Travelers’potential demand toward flex-route transit:Nanjing, China, case study[J]. Journal of Urban Planning and Development,2020, 146(1):05019018.
[18] LI X, QUADRIFOGLIO L. Feeder transit services:Choosing between fixed and demand responsive policy[J]. Transportation Research Part C:Emerging Technologies, 2010, 18(5):770-780.
[19] CHEN F, YIN Z, YE Y, et al. Taxi hailing choice behavior and economic benefit analysis of emission reduction based on multi-mode travel big data[J]. Transport Policy, 2020, 97:73-84.
[20]范文博，陈香，刘涛.模块化自动驾驶穿梭公交服务频率优化及时刻表设计[J].交通运输工程与信息学报，2023, 21(2):160-176.FAN Wenbo, CHEN Xiang, LIU Tao. Modular autonomous shuttle transit service:frequency setting and timetabling[J]. Journal of Transportation Engineering and Information, 2023, 21(2):160-176.
[21] GUO Q, SUN Y, SCHONFELD P, et al. Time-dependent transit fare optimization with elastic and spatially distributed demand[J]. Transportation Research Part A:Policy and Practice, 2021, 148:353-378.
[22] SUN Y, SCHONFELD P M. Optimization models for public transit operations under subsidization and regulation[J]. Transportation Research Record:Journal of the Transportation Research Board, 2015, 2530(1):44-54.
[23] TALLEY W K. An economic theory of the public transit firm[J]. Transportation Research Part B:Methodological, 1988, 22(1):45-54.
[24] QUADRIFOGLIO L, DESSOUKY M M, ORDó?EZ F.A simulation study of demand responsive transit system design[J]. Transportation Research Part A:Policy and Practice, 2008, 42(4):718-737.
[25]胡严艺，蒲政，王沛.基于Logit模型的碳排放收费对居民出行方式选择的研究[J].交通运输工程与信息学报，2018, 16(4):57-62.HU Yanyi, PU Zheng, WANG Pei. Study on the impacts of traffic carbon emission pricing on resident trip behavior using logit model[J]. Journal of Transportation Engineering and Information, 2018, 16(4):57-62.
[26] JARA-DíAZ S, GSCHWENDER A. Towards a general microeconomic model for the operation of public transport[J]. Transport Reviews, 2003, 23(4):453-469.
[27] KIM M E, SCHONFELD P. Integrating bus services with mixed fleets[J]. Transportation Research Part B:Methodological, 2013, 55:227-244.
[28] LüTHI M, WEIDMANN U, NASH A. Passenger arrival rates at public transport stations[C]//TRB 86th Annual Meeting Compendium of Papers. Washington D. C.:Transportation Research Board, 2007:07-0635.
[29] YANG H, ZHANG Z, FAN W, et al. Optimal design for demand responsive connector service considering elastic demand[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(4):2476-2486.
[30]简文良，何艺，胡永仕，等.集装箱公铁联运“门到门”运输时间可靠性分析[J].交通运输工程与信息学报，2023, 21(4):14-24.JIAN Wenliang, HE Yi, HU Yongshi, et al. Analysis of“door-to-door”transportation time reliability for container road-rail intermodal transportation[J]. Journal of Transportation Engineering and Information, 2023, 21(4):14-24.
[31]张文会，刘委，王圣鼎，等.基于结构方程的城市常规公交满意度评价[J].交通运输工程与信息学报，2021,19(1):43-51.ZHANG Wenhui, LIU Wei, WANG Shengding, et al.Evaluation of passenger satisfaction to conventional public transit based on a structural equation[J]. Journal of Transportation Engineering and Information, 2021, 19(1):43-51.
[32]冯小伟，刘路.老年人公交出行特征及引导策略研究综述[J].交通运输工程与信息学报, 2023, 21(1):29-48.FENG Xiaowei, LIU Lu. A review on the characteristics and guidance strategies of bus travel for the elderly[J].Journal of Transportation Engineering and Information,2023, 21(1):29-48.
[33]沈玮薇，肖为周，杨磊，等.基于TPB的居民通勤出行公交选择意向模型[J].交通运输工程与信息学报，2016, 14(3):106-112.SHEN Weiwei, XIAO Weizhou, YANG Lei, et al. Public transport choice intention model of commute travel mode based on TPB[J]. Journal of Transportation Engineering and Information, 2016, 14(3):106-112.
[34] YU J, ZHENG Y, LI W, et al. Understanding flex-route transit adoption from a stage of change perspective[J].Transportation Research Record:Journal of the Transportation Research Board, 2023, 2677(6):743-758.
基本信息:
DOI：10.19961/j.cnki.1672-4747.2023.12.015
中图分类号:U491.17
引用信息:
[1]范文博,唐慧祥,杨鸿泰等.考虑乘客起讫点选择的可变线路公交服务设计优化[J].交通运输工程与信息学报,2024,22(03):52-67.DOI:10.19961/j.cnki.1672-4747.2023.12.015.
基金信息:
中央高校基本科研业务费专项资金项目（2682023ZTPY012）; 四川省国际科技合作项目（24GJHZ0342）; 城市公共交通智能化交通运输行业重点实验室开放课题（2023-APTS-06）; 广东省科技创新战略专项资金（粤港澳联合实验室）项目（2020B1212030009）; 成都市技术创新研发项目（2022-YF05-00839-SN）; 四川省自然科学基金（2022NSFSC0465）