考虑车内换乘的多线路模块化自动驾驶公交调度优化

doi:10.16097/j.cnki.1009-6744.2026.03.024

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (3): 259-273.DOI: 10.16097/j.cnki.1009-6744.2026.03.024

• 智能交通系统与信息技术 • 上一篇下一篇

考虑车内换乘的多线路模块化自动驾驶公交调度优化

胡宝雨^*a，王宏轩^a，景维鹏^b

东北林业大学，a.土木与交通学院；b.计算机与控制工程学院，哈尔滨150040

收稿日期:2026-01-02 修回日期:2026-03-25 接受日期:2026-03-30 出版日期:2026-06-25 发布日期:2026-06-23
作者简介:胡宝雨（1987— ），男，黑龙江宾县人，副教授。
基金资助:
黑龙江省自然科学基金 (YQ2022E003)；中国博士后科学基金 (2023M740558)。

Scheduling Optimization for Multi-line Modular Autonomous Bus Systems with In-vehicle Transfers

HU Baoyu^*a, WANG Hongxuan^a, JING Weipeng^b

a. College of Civil Engineering and Transportation; b. College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China

Received:2026-01-02 Revised:2026-03-25 Accepted:2026-03-30 Online:2026-06-25 Published:2026-06-23
Supported by:
Natural Science Foundation of Heilongjiang Province, China (YQ2022E003)；China Postdoctoral Science Foundation (2023M740558)。

摘要/Abstract

摘要： 为解决城市公交系统中供需不匹配及换乘体验差等问题，本文提出一种考虑车内换乘服务的多线路模块化自动驾驶公交（Modular Autonomous Vehicles, MAV）调度优化方法。以运营商成本与乘客时间成本构成的系统总成本最小化为目标，构建车内与车外换乘结合的MAV系统时刻表、车辆编组及模块化单元调度的联合优化模型。针对该模型解空间庞大且约束复杂的特性，设计一种基于多种群遗传算法（Multi-Population Genetic Algorithm, MPGA）的两阶段求解框架。基于北京市多线路运营场景的数值实验表明，所提模型的总成本与传统固定容量公交系统相比降低7.0%，与仅支持车外换乘的MAV系统相比降低3.1%，与全员车内换乘的MAV系统相比降低1.5%。敏感性分析表明，在不同服务体验差异性系数下模型具有良好的稳定性，过高的车内换乘比例会导致车内换乘的边际收益递减，在实际应用中应针对具体的换乘需求选择合适的车内换乘比例。

关键词: 城市交通, 调度优化, 多种群遗传算法, 模块化车辆, 多线路公交, 车内换乘

Abstract: To resolve the mismatch between transport supply and demand and improve transfer efficiency in multi-line bus systems, this paper proposes a scheduling optimization model for Modular Autonomous Vehicles (MAV). The model enables an "in-vehicle transfer" mode, where passengers complete cross-line transfers without alighting, facilitated by the dynamic coupling and decoupling of Modular Units (MU). A dual-stage optimization framework based on a Multi-Population Genetic Algorithm is designed. The first stage optimizes timetables and vehicle formations, while the second schedules specific MUs to minimize total generalized costs, comprising operator expenditures and passenger waiting times. Numerical experiments using real-world data from Beijing demonstrate that the proposed method resulted in a reduction of the total system cost by 7.0% compared to traditional fixed-capacity bus systems, a 3.1% reduction of cost compared to MAV systems relying solely on walking transfers, and a 1.5% reduction of total cost compared to scenarios adopting all-passenger in-vehicle transfers. The study result indicates that while in vehicle transfers enhance service quality, the marginal benefits diminish with an excessive transfer proportion, suggesting that the model effectively balances economic efficiency and passenger experience by identifying the optimal transfer ratio.

Key words: urban transportation, scheduling optimization, Multi-Population Genetic Algorithm, modular vehicle, multi-line bus system, in-vehicle transfer

中图分类号:

U121

胡宝雨, 王宏轩, 景维鹏. 考虑车内换乘的多线路模块化自动驾驶公交调度优化[J]. 交通运输系统工程与信息, 2026, 26(3): 259-273.

HU Baoyu, WANG Hongxuan, JING Weipeng. Scheduling Optimization for Multi-line Modular Autonomous Bus Systems with In-vehicle Transfers[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(3): 259-273.

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http://www.tseit.org.cn/CN/Y2026/V26/I3/259

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考虑车内换乘的多线路模块化自动驾驶公交调度优化

Scheduling Optimization for Multi-line Modular Autonomous Bus Systems with In-vehicle Transfers

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