Urban and Rural Bus Scheduling with Alternating Segment Demand
 Response Based on Passengers' Autonomous Choices

doi:10.16097/j.cnki.1009-6744.2025.02.019

Abstract

Abstract: To effectively tackle the issue of inconvenient resident travel caused by the low coverage of public transportation services in rural areas and enhance passengers' option as the main travel entities, this paper proposes a section alternating response based scheduling approach which utilizes the existing fixed route resources of urban and rural bus and considers passengers' autonomous choices. This approach divides the rural operation sections into two alternating response subsections and determines the demand response stations through the vector product method. A two-stage scheduling model is designed, adopting the method of instantaneously processing orders to achieve real-time responses. The demand response stations that have been served are set as "temporary fixed stations" to accelerate the model solution. An autonomous choice mechanism is developed, covering the entire process from the generation of travel demands to the selection of departure time, boarding stations, service trips, and secondary choices after order refusals. Taking the No. 4 urban and rural bus line in Siyang County, Jiangsu Province as an example, numerical experiments under different passenger flow intensities are designed. The experiments indicate that in the scenario of a large passenger flow, the average number of alighting passengers at each demand response station is 3.76. Under different passenger flow intensities, the autonomous choice mechanism enables 15.79% to 34.50% of passengers to make personalized choices, with the highest increase in fare revenue by 13.51%. By charging service fares to balance the additional costs, an average increase of 1.53% in fare revenue can be achieved for every 1% increase in driving distance. The results indicate that the section alternating response can effectively concentrate passengers with similar travel times and arrival stations within the same section, reducing operating costs. Considering passengers' autonomous choices can fully depict the travel behavior of passengers and increase system revenue.

Key words: intelligent transportation, segment demand response, two-stage scheduling, urban and rural bus, passenger selection, vector product method

摘要： 为有效应对农村地区公交服务覆盖率低导致居民出行不便的问题，增强乘客作为出行主体的选择权，本文提出一种利用现有城乡公交固定线路资源，考虑乘客自主选择的区段交替响应式调度方法。该方法将乡村运营区段划分为两个交替响应的小区段，通过向量积法确定需求响应站点；设计两阶段调度模型，采用即时处理订单的方式实现实时响应，将已被服务的需求响应站点设为“临时固定站点”加速模型求解；并构建涵盖从出行需求产生到选择出发时间、上车站点、服务车次，以及拒单后二次选择等全过程的自主选择机制。以江苏省宿迁市泗阳县4路城乡公交为例，设计不同客流强度下的数值实验。实验结果表明：区段交替响应在大客流场景下，每个需求响应站点的平均下车人数为3.76人；不同客流强度下，自主选择机制使15.79%~34.50%的乘客做出个性化选择，至高提升13.51%的票价收入；通过收取服务票价的方式均衡额外成本，平均每增加1%的行驶距离可提升1.53%的票价收入。结果表明：区段交替响应可有效集中出行时间相似且到达站点在同一个区段内的乘客，减少运营成本；考虑乘客自主选择可完整刻画乘客出行全过程行为，并提高系统收入。

关键词: 智能交通, 区段交替响应, 两阶段调度, 城乡公交, 乘客选择, 向量积法

CLC Number:

U491.54

JIANG Xiaohong, LI Jiawei, HUA Jingwen, ZHONG Yunhao, XING Jiping. Urban and Rural Bus Scheduling with Alternating Segment Demand Response Based on Passengers' Autonomous Choices[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 201-213.

姜晓红, 李家伟, 华晶雯, 仲韫豪, 邢吉平. 考虑乘客自主选择的区段交替响应式城乡公交调度[J]. 交通运输系统工程与信息, 2025, 25(2): 201-213.

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References

[1] 交通运输部.交通运输部办公厅关于印发《农村道路客运运营服务指南(试行)》的通知[EB/OL]. (2024-06 28) [2024-07-02]. https://xxgk.mot.gov.cn/2020/jigou/ ysfws/202406 /t20240628_4143359.html. [Ministry of Transport. General office of the ministry of transport on the issuance of the rural road passenger transport operation service guide (trial) notice. [EB/OL]. (2024-06-28) [2024-07-02]. https://xxgk.mot.gov.cn/ 2020/jigou/ysfws/202406 /t20240628_4143359.html.]

[2]林宙.预约响应式农村客货运结合车辆路径问题研究[D]. 北京: 北京交通大学, 2021. [LIN Z. Research on vehicle routing problem of rural passenger and freight transportation based on reservation[D]. Beijing: Beijing Jiaotong University, 2021.]

[3] DILAY A, KENNETH S, PIETER V. A variable neighborhood search algorithm for a public bus line with a demand-responsive operation during peak hours[J]. Transportation Planning and Technology, 2023, 46(5): 615-652.

[4]张进,李文权,郭蓉蓉.可变线路公交接驳地铁时刻表构建方法[J/OL]. 西南交通大学学报, (2024 01-30) [2024-12-23]. http://kns.cnki.net/kcms/detail/ 51.1277.U.20240130.0946.002.html. [ZHANG J, LI W Q, GUO R R. Optimal feeder flex-route transit schedule design of urban rail transit[J/OL]. Journal of Southwest Jiaotong University, (2024-01-30) [2024-12-23]. http:// kns.cnki.net/kcms/detail/51.1277.U.20240130.0946.002. html.]

[5]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(3): 52-67. [FAN W B, TANG H X, YANG H T, et al. Design optimization of flex-route transit services considering passengers' departure and destination choice[J]. Journal of Transportation Engineering and Information, 2024, 22(3): 52-67.]

[6]郑乐,高良鹏,沈金星, 等. 可变线路式公交运行服务能力优化策略[J]. 哈尔滨工业大学学报,2021,53 (9): 107-115. [ZHENG L, GAO L P, SHEN J X, et al. Operational service capability optimization strategies for flex-route transit service[J]. Journal of Harbin Institute of Technology, 2021, 53(9): 107-115.]

[7] WANG Z W, YU J, HAO W, et al. Joint optimization of running route and scheduling for the mixed demand responsive feeder transit with time-dependent travel times [J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(4): 2498-2509.

[8]孙倩,胡大伟,钱一之,等.考虑车辆随机到站时间的动态需求响应型接驳公交线路优化[J].交通运输系统工程与信息,2022, 22(5): 196-204. [SUN Q, HU D W, QIAN Y Z, et al. Dynamic bus routing optimization for demand-responsive feeder transit considering stochastic bus arrival time[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 196-204.]

[9] WU W T, ZHU Y C, LIU R H. Dynamic scheduling of flexible bus services with hybrid requests and fairness: Heuristics-guided multi-agent reinforcement learning with imitation learning[J]. Transportation Research Part B, 2024, 190: 103069.

[10] 巫威眺, 周霄,朱彦辰,等.考虑乘客时空灵活性的需求响应客货联运动态调度[J].交通运输系统工程与信息, 2023, 23(4): 211-227. [WU W T, ZHOU X, ZHU Y C, et al. Demand-responsive dynamic scheduling of intermodal passenger and cargo transportation considering passengers' spatial and temporal flexibility [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(4): 211-227.]

[11] NARAYAN J, CATS O, OORT V N, et al. Integrated route choice and assignment model for fixed and flexible public transport systems[J]. Transportation Research Part C, 2020, 115: 102631.

[12] 涂辉招, 刘建泉,郑叶明,等. 面向个性化需求的自动驾驶微公交动态调度方法[J].同济大学学报(自然科学版),2024, 52(11): 1731-1741. [TU H Z, LIU J Q, ZHENG Y M, et al. Dynamic autonomous minibus scheduling for personalized travel demand[J]. Journal of Tongji University (Natural Science Edition), 2024, 52 (11): 1731-1741.]

[13] 任婧璇, 常孝亭,巫威眺,等.考虑候选站点和全服务过程的需求响应接驳公交调度[J].交通运输系统工程与信息,2023, 23(5): 202-214. [REN J X, CHANG X T, WU W T, et al. Demand responsive feeder transit scheduling considering candidate stops and full-service process[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5): 202-214.]

[14] 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.

[15] 陈红, 李晨光,王铎,等.空间异质下地铁建成环境与站点覆盖客流吸引度关系研究[J].交通运输系统工程与信息, 2024, 24(4): 253- 262. [CHEN H, LI C G, WANG D, et al. Relationship between built environment of metro station and passenger attraction considering spatial heterogeneity[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(4): 253-262.]

Urban and Rural Bus Scheduling with Alternating Segment Demand Response Based on Passengers' Autonomous Choices

考虑乘客自主选择的区段交替响应式城乡公交调度

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