定制客运动态乘降点与车辆路线联合优化

doi:10.16097/j.cnki.1009-6744.2026.01.021

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (1): 228-238.DOI: 10.16097/j.cnki.1009-6744.2026.01.021

定制客运动态乘降点与车辆路线联合优化

李小静^*，袁静，秦怡豪

兰州交通大学，交通运输学院，兰州730070

收稿日期:2025-11-07 修回日期:2025-12-11 接受日期:2025-12-23 出版日期:2026-02-25 发布日期:2026-02-15
作者简介:李小静（1981—），女，河南安阳人，副教授，博士。
基金资助:
国家自然科学基金(71861023)；甘肃省科技厅重点研发计划项目(23YFFA0058)。

Joint Optimization of Dynamic Ride and Drop-off Sites and Vehicle Routes for Customized Passenger Transport

LI Xiaojing^*, YUAN Jing, QIN Yihao

School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

Received:2025-11-07 Revised:2025-12-11 Accepted:2025-12-23 Online:2026-02-25 Published:2026-02-15
Supported by:
National Natural Science Foundation of China (71861023)；Key Research and Development Project, Science and Technology Department of Gansu Province (23YFFA0058)。

摘要/Abstract

摘要： 针对定制客运中乘降点与车辆路线分步优化导致的效率损失问题，本文提出一种动态乘降点与车辆路线联合优化的方法。首先，构建多目标联合优化模型，以最小化乘客出行成本、最大化企业运营效益为目标，在约束中限定乘客需求点仅能通过接驳或车辆路线进行连接，并引入多车型容量限制，同步优化乘客上下车时空安排与车辆服务计划；然后，设计一种混合启发式算法，以NSGA-III（Non-dominated Sorting Genetic Algorithm III）为框架，融合差分进化和自适应大邻域搜索，协同实现全局探索与局部精修；此外，同时集成ST-DBSCAN（Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise）算法，从需求点中识别动态乘降点并引导种群进化；最后，以重庆主城至铜梁区为例进行验证。结果表明：所构建的联合优化模型能在不同需求规模下自适应选择最优车型配置，并协同优化服务效率与运营效益；所提算法能表现出更优的收敛性能与解集分布质量。实际运营中，联合优化使乘客平均接驳距离大约降低至未联合优化的1/10，单次运营利润增加31.62元，载客率提升2.77%。

关键词: 综合交通运输, 联合优化, 混合启发式算法, 定制客运, 动态乘降点, 路线规划

Abstract: This paper addresses the efficiency loss in customized passenger transport caused by the separate optimization of passenger stops and vehicle routes and proposes a joint optimization method for dynamic ride and drop-off site and vehicle routing. A multi-objective optimization model is developed to minimize passenger travel costs while maximizing operational benefits, under the constraint that passenger demand points must be connected either via feeder services or direct vehicle routes. The model incorporates multi-vehicle type capacity limits and simultaneously optimizes passenger ride and drop-off schedules and vehicle service plans. A hybrid metaheuristic algorithm is designed using the NSGA-III (Non-dominated Sorting Genetic Algorithm III) framework, integrating DE and ALNS to achieve coordinated global exploration and local refinement. The ST-DBSCAN (Spatial Temporal Density-Based Spatial Clustering of Applications with Noise) is used to identify dynamic stops from demand points and guide population evolution. The case study of the Chongqing-Tongliang corridor shows that the model adaptively selects optimal vehicle fleets across demand levels while boosting both service efficiency and operational profit, with the algorithm exhibiting superior convergence and solution diversity. Practically, it could reduce the average passenger transfer distance to roughly one tenth that of non-joint optimization, and increase the single-trip profit by 31.62 yuan and the vehicle load rate by 2.77 percentage points.

Key words: integrated transportation, joint optimization, hybrid metaheuristic algorithms, customized passenger transport; dynamic passenger ride and drop-off site, route planning

中图分类号:

U125

李小静, 袁静, 秦怡豪. 定制客运动态乘降点与车辆路线联合优化[J]. 交通运输系统工程与信息, 2026, 26(1): 228-238.

LI Xiaojing, YUAN Jing, QIN Yihao. Joint Optimization of Dynamic Ride and Drop-off Sites and Vehicle Routes for Customized Passenger Transport[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(1): 228-238.

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参考文献

[1]薛浩楠,王佳.基于时空聚类的定制公交需求响应机制[J].公路交通科技,2021,38(3):113-121. [XUEHN, WANGJ. Demand responsemechanismof customized publictransportationbasedonspatial-temporalclustering [J]. JournalofHighwayandTransportationResearchand Development,2021,38(3):113-121.]

[2] LYUY,CHOWCY,LEEVCS, et al. CB-planner: A bus lineplanningframeworkforcustomizedbussystems [J]. Transportation Research Part C: Emerging Technologies,2019,101:233-253.

[3]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. [ZHENGL, GAOL P, SHEN J X, et al. Optimization strategy of variable route bus service capacity[J]. Journal of Harbin Institute of Technology,2021, 53(9): 107-115.]

[4]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 bus scheduling considering candidate stops and full-service process[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5): 202-214.]

[5]常玉林,蔡宇航,孙超,等.异质需求下响应型接驳公交车速引导与路径规划协同研究[J].交通运输系统工程与信息,2023, 23(5): 83-95. [CHANG Y L, CAI Y H, SUN C, et al. A collaborative study of speed guidance and path planning for responsive feeder buses under heterogeneous demand[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5): 83-95.]

[6]郑汉,张星臣,王志美.混合车型需求响应公交服务定制问题研究[J]. 交通运输系统工程与信息,2018,18 (2): 157-163. [ZHENG H, ZHANG X C, WANG Z M. Research on the customization problem of hybrid model demand response bus service[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(2): 157-163.]

[7]冯树民,胡宝雨,马栋才,等.城际快速路客运站点设置方法[J]. 哈尔滨工业大学学报,2016, 48(3): 71-76. [FENG S M, HU B Y, MA D C, et al. Setting method of intercity expressway passenger station[J]. Journal of Harbin Institute of Technology, 2016, 48(3): 71-76.]

[8]GUO J Q, LONG J C, XU X M, et al. The vehicle routing problem of intercity ride-sharing between two cities[J]. Transportation Research Part B: Methodological, 2022, 158: 113-139.

[9]HU D, WANG C, CHEN J. Intercity customized passenger transportation service plan optimization design with spatial-temporal accessibility based on BIRCH-VNS [J]. Neural Computing and Applications, 2024, 36(21): 13127-13154.

[10] ZHU C, WU J J, GUO X, et al. Joint optimization of bus scheduling and seat allocation for reservation-based travel[J]. Transportation Research Part C: Emerging Technologies, 2024, 163: 104631.

[11] WU M, YU C H, MA W J, et al. Joint optimization of timetabling, vehicle scheduling, and ride-matching in a flexible multi-type shuttle bus system[J]. Transportation Research Part C: Emerging Technologies, 2022, 139: 103657.

[12] GUAN Y, WANG X, WANG Y, et al. Bi-level optimization for customized bus routing serving passengers with multiple-trips based on state-space-time network[J]. Physica A: Statistical Mechanics and its Applications, 2023, 614: 128517.

[13] BIRANT D, KUT A. ST-DBSCAN: An algorithm for clustering spatial-temporal data[J]. Data & Knowledge Engineering, 2007, 60(1): 208-221.

[14] DEB K, JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601.

[15] YUAN J, LI X J, QIN Y H, et al. Research on dynamic ride and drop-off site setting for customized passenger transport based on spatial-temporal clustering[J]. Engineering Letters, 2025, 33(5): 1382-1394.

[16] GUERREIRO A P, FONSECA C M, PAQUETE L. The hypervolume indicator: Computational problems and algorithms[J]. ACM Computing Surveys, 2021, 54(6): 1-42

定制客运动态乘降点与车辆路线联合优化

Joint Optimization of Dynamic Ride and Drop-off Sites and Vehicle Routes for Customized Passenger Transport

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