交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (1): 217-227.DOI: 10.16097/j.cnki.1009-6744.2026.01.020

• 系统工程理论与方法 • 上一篇    下一篇

考虑乘客偏好的需求响应定制公交线路优化

杜太升1，彭正鍾1，张源凯1，田琼*1,2，蒋晓桐1   

  1. 1. 北京航空航天大学，经济管理学院，复杂系统分析与管理决策教育部重点实验室，北京100191；2. 杭州市北京航空航天大学国际创新研究院，杭州311115
  • 收稿日期:2025-09-17 修回日期:2025-11-14 接受日期:2025-11-21 出版日期:2026-02-25 发布日期:2026-02-15
  • 基金资助:
    国家重点研发计划(2023YFE0115600)；国家自然科学基金(72288101)。

Optimization of Customized Bus Routes Considering Passenger Preferences

DU Taisheng1, PENG Zhengzhong1, ZHANG Yuankai1, TIAN Qiong*1,2, JIANG Xiaotong1   

  1. 1. MoE Key Laboratory of Complex System Analysis and Management Decision, School of Economics and Management, Beihang University, Beijing 100191, China; 2. Hangzhou International Innovation Institute of Beihang University, Hangzhou 311115, China
  • Received:2025-09-17 Revised:2025-11-14 Accepted:2025-11-21 Online:2026-02-25 Published:2026-02-15
  • Supported by:
    National Key Research and Development Program of China (2023YFE0115600)；National Natural Science Foundation of China (72288101)。

PDF

摘要： 为提升需求响应定制公交吸引力，本文探讨考虑乘客偏好的两阶段需求响应定制公交线路优化问题。静态阶段，在时空聚类算法基础上挖掘乘客偏好车辆服务类型，并用外生参数表征站点乘客偏好，将其融入需求响应定制公交线路优化模型中。该模型以最小化车辆固定成本、车辆可变成本、运行时间成本和未提供乘客服务的惩罚成本之和为目标，同时，优化车辆线路、车辆到站时间和车辆服务站点后的乘客数量，并设计自适应大邻域搜索算法（ALNS）生成静态阶段初始解。动态阶段，从在车乘客自主决策接送需求视角出发，引入群体决策函数判定车辆是否接送动态请求，并设计动态请求分配算法，在满足乘客偏好、时间窗约束和车辆容量约束下，更新静态阶段生成的初始线路。最后，以北京市公交出行数据为实例验证分析，结果表明：与单阶段静态模型相比，两阶段需求响应定制公交网络设计模型，上车人数增加2.78%；与时空聚类策略相比，融合乘客偏好聚类策略使车辆总运行成本仅增加17.23%，就能满足静态阶段乘客偏好；与不考虑群体决策相比，在群体决策框架下，需求响应定制公交绕行距离减少33.33%，该方法能够为需求响应定制公交线路规划提供决策支持。

关键词: 交通工程, 异质性偏好, 混合整数规划, 需求响应定制公交, 路径优化

Abstract: To enhance the appeal of demand-responsive customized bus systems, this paper addresses a two-stage demand- responsive customized bus route optimization problem that incorporates passenger preferences. In the static stage, passenger preferences for vehicle service types are identified with a spatio-temporal clustering algorithm. These preferences are then characterized as exogenous parameters for each stop and integrated into a demand-responsive customized bus route optimization model. The objective of this model is to minimize the sum of the vehicle fixed cost, vehicle variable cost, operating time cost, and penalty costs for unserved passengers. It simultaneously optimizes vehicle routes, arrival times at stops, and passenger loads after serving each stop. An Adaptive Large Neighborhood Search (ALNS) algorithm is designed to generate initial solutions for this static stage. In the dynamic stage, the problem is approached from the perspective of en-route passengers who makes autonomous decisions about accommodating new ride requests. A group decision-making function is introduced to determine whether a vehicle should accept a dynamic request. A dynamic request assignment algorithm is then designed to update the initial routes generated in the static stage, subject to passenger preferences, time window constraints, and vehicle capacity constraints. Finally, a case study using public transit data from Beijing is presented to validate the proposed method. The results indicate that, compared to the single-stage static model, the two-stage demand-responsive customized bus network design model increases the number of boarding passengers by 2.78%; compared to a standard spatio-tamporal clustering algorithm, the strategy that incorporates passenger preference clustering increases total vehicle operating costs by 17.23% while successfully satisfying passenger preferences in the static stage. Furthermore, compared to a scenario without group decision-making, the group decision-making framework leads to a 33.33% reduction in detour distance for the demand-responsive customized bus. This method can provide a decision support for the route planning of demand-responsive customized bus systems.

Key words: traffic engineering, heterogeneous preferences, mixed-integer programming, demand responsive transit, route optimization

中图分类号: