交通运输系统工程与信息

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (6): 239-248.DOI: 10.16097/j.cnki.1009-6744.2025.06.022

• 系统工程理论与方法 • 上一篇    下一篇

考虑分段充电策略的电动公交时刻表和车辆调度整体优化

高万晨*1 ，路世昌2 ，赵娅彤1 ，刘锴3   

  1. 1. 辽宁对外经贸学院，经济学院，辽宁大连116052；2.辽宁工程技术大学，工商管理学院，辽宁葫芦岛125000； 3. 北京中拓智森交通科技有限公司，北京100080
  • 收稿日期:2025-08-16 修回日期:2025-09-14 接受日期:2025-09-23 出版日期:2025-12-25 发布日期:2025-12-24
  • 作者简介:高万晨(1989—)，男，辽宁大连人，讲师，博士。
  • 基金资助:
    辽宁省教育厅项目 (JYTMS20231007)。

Integrated Optimization of Electric Bus Timetable and Vehicle Scheduling with Piecewise Charging Strategy

GAO Wanchen*1, LU Shichang2, ZHAO Yatong1, LIU Kai3   

  1. 1. School of Economics, Liaoning University of International Business and Economics, Dalian 116052, Liaoning, China; 2. School of Business Administration, Liaoning Technical University, Huludao 125000, Liaoning, China; 3. Beijing Zhongtuo Zhisen Traffic Science and Technology Co Ltd, Beijing 100080, China
  • Received:2025-08-16 Revised:2025-09-14 Accepted:2025-09-23 Online:2025-12-25 Published:2025-12-24
  • Supported by:
    Project of the Department of Education of Liaoning Province (JYTMS20231007)。

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摘要： 为降低公交企业成本，提升乘客满意度，本文结合分段充电策略、分时电价，以及与时间相关的驻站时间、行程时间、乘客上车率、乘客下车率等参数，构建考虑多车场多车型的时刻表和车辆调度整体优化的双目标模型。模型以乘客出行时间和公交企业成本最小为目标。前者包括乘客等待时间和乘客车内时间，后者包括固定成本、空驶成本和充电成本。针对该模型的特点，设计改进非支配排序遗传算法II进行求解，并选取珠海市26路公交线路进行案例分析。结果表明：在早高峰、中午平峰和晚高峰3个时段中，公交企业现状方案和顺序方法获得的解均被整体优化方法得到的Pareto最优解支配。与公交企业现状方案相比，整体优化方法平均降低5.1%的公交企业成本和4.9%的乘客出行时间；与顺序方法相比，乘客出行时间平均降低1.6%，其中，乘客等待时间降低1.8%，乘客车内时间降低1.6%。本文可为城市公交企业科学调度与管理提供优化建议。

关键词: 城市交通, 公交调度, 改进非支配排序遗传算法II, 电动公交, 分段充电策略

Abstract: To reduce the costs of bus enterprises and enhance passenger satisfaction, this paper proposes a dual-objective model to optimize the timetable and vehicle scheduling considering multiple depots and multiple vehicle types. The model combines the piecewise charging strategy, the time-of-use pricing, and time-dependent parameters such as dwelling time, travel time, passenger boarding rate, and passenger alighting rate. The objectives of the model include minimizing the passenger travel time and the cost of bus enterprises. The former includes passenger waiting time and the riding time, while the latter includes fixed costs, deadheading costs and charging costs. The improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) was designed to solve the model. The bus line No. 26 in Zhuhai City was selected for case analysis. The findings indicate that during the morning rush hour, the noon off-peak hour, and the evening rush hour, the solutions derived from both the current plan employed by bus companies and the sequential method are inferior to the Pareto optimal solutions achieved through an integrated optimization approach. Compared with the current plan of bus enterprises, the integrated optimization approach reduces the cost of bus enterprises by an average of 5.1% and the travel time of passengers by 4.9%. Compared with the sequential method, the average travel time of passengers decreased by 1.6%, among which the waiting time of passengers decreased by 1.8% and the riding time of passengers decreased by 1.6%. This research provides references for the scientific dispatching and management of urban bus transit.

Key words: urban traffic, bus scheduling, improved Non-dominated Sorting Genetic Algorithm II, electric bus, piecewise charging strategy

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