交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (1): 76-89.DOI: 10.16097/j.cnki.1009-6744.2026.01.008

• 智能交通系统与信息技术 • 上一篇    下一篇

不同共乘参与程度下异构自动驾驶车队规模研究

谢金苹a,崔洪军*a,朱敏清b,马新卫a   

  1. 河北工业大学,a.土木与交通学院;b.建筑与艺术设计学院,天津300401
  • 收稿日期:2025-11-04 修回日期:2025-12-16 接受日期:2025-12-29 出版日期:2026-02-25 发布日期:2026-02-15
  • 作者简介:谢金苹(1995—),女,河北唐山人,博士生。
  • 基金资助:
    国家自然科学基金(52172304)。

Fleet Size of Heterogeneous Autonomous Vehicles Under Different Ride sharing Participation Levels

XIE Jinpinga, CUI Hongjun*a, ZHU Minqingb, MA Xinweia   

  1. a. School of Civil and Transportation; b. School of Architecture and Art Design, Hebei University of Technology, Tianjin 300401, China
  • Received:2025-11-04 Revised:2025-12-16 Accepted:2025-12-29 Online:2026-02-25 Published:2026-02-15
  • Supported by:
    National Natural Science Foundation of China (52172304)。

摘要: 为系统分析不同共乘参与程度对异构自动驾驶车队规模的影响,本文针对由容量为2和容量为4的混合车型组成的异构车队,提出一种基于乘客不同共乘参与程度的共享自动驾驶车辆(Shared Autonomous Vehicles, SAV)调度优化方法,在保障服务质量的前提下,实现资源利用效率与乘客满意度的协同提升。首先,构建请求间可共享性网络,并采用带花树算法求解2人共乘的最大权重匹配问题;然后,引入图论中边收缩概念,将共乘关系进一步扩展至4人,通过重构网络结构得到至多4人共乘的最大权重匹配方案;在此基础上,采用Kuhn-Munkres算法求解最小车队规模问题,获得车辆调度方案。基于成都市真实路网和出租车订单数据的实例结果表明,异构车队有效减少了车队规模,提高了资源利用效率,并降低了燃油消耗与运营成本。敏感性分析表明,随着共乘参与比例从10%提升至90%,异构车队的总规模呈下降趋势,由1174辆下降至331辆,其中,车辆构成由0.94∶0.06逐渐转变为0.05∶0.95;整体车队的行驶时间、行驶里程及耗油量均呈下降趋势,而平均行驶时间、平均行驶里程及平均耗油量均呈上升趋势。此外,共乘通过乘客间的成本分摊有效降低了出行费用,并且平均等待时间与延误时间也始终保持在乘客可接受范围内。

关键词: 智能交通, 车队规模, 可共享性网络, 共享自动驾驶车辆, 异构车队

Abstract: To systematically analyze the impact of different levels of ride-sharing participation on the fleet size of heterogeneous shared autonomous vehicles, this paper proposed a scheduling optimization approach for the heterogeneous SAV fleet composed of vehicles with capacities of 2 and 4, based on different levels of ride-sharing participation among passengers. The approach aims to enhance the resource utilization efficiency and passenger satisfaction while ensuring service quality. Then, it achieved a maximum weight matching for up to four passengers by introducing the concept of edge contraction from graph theory, and reconstructing the network structure. Based on this, the Kuhn-Munkres algorithm was applied to solve the minimum fleet size problem, and obtained the optimal scheduling solution. Case studies based on the real data of road networks and taxi order from Chengdu show that the heterogeneous fleet effectively reduces the fleet size, and improves the efficiency of resource utilization and lower the costs of fuel consumption and operational. Sensitivity analysis further indicates that as the proportion of ride-sharing participation increases from 10% to 90%, the total fleet size decreases from 1 174 vehicles to 331 vehicles, and the fleet composition shifts from 0.94∶0.06 to 0.05∶0.95. Moreover, the total travel time, total travel distance, and total fuel consumption of fleet all exhibit a decreasing trend, whereas the average travel time, average travel distance, and average fuel consumption per vehicle show an increasing trend. In addition, ride-sharing effectively reduces travel fares through cost-sharing among passengers, and the average waiting time and delay time remain within acceptable ranges.

Key words: intelligent transportation, fleet size, shareability network, shared autonomous vehicles, heterogeneous fleet

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