基于多智体的自动驾驶汽车停车空载收费策略
Empty-load charging strategy for autonomous vehicle parking based on multi-agent system
黎文皓 1季彦婕 2吴浩 3贾叶雯 2张水潮4
作者信息
- 1. 东南大学交通学院,江苏南京 211189;名古屋大学土木与环境工程系,爱知县名古屋 464-8603
- 2. 东南大学交通学院,江苏南京 211189
- 3. 安徽三联学院交通信息与安全重点实验室,安徽合肥 230601
- 4. 宁波工程学院建筑与交通工程学院,浙江宁波 315211
- 折叠
摘要
为了制订自动驾驶车辆(AV)停车需求管理方案,搭建多智能体停车模拟框架,提出 2 种空载行驶收费策略:基于行驶距离的静态收费和基于道路拥堵水平的动态收费,研究费率计算方法.建立空载行驶收费策略下停车场停车、居住地停车及持续空载巡航 3 种停车模式的成本函数,使用logit模型描述不同停车模式下的选择行为.利用Simulation of urban mobility(SUMO),以南宁市主城区为例开展大规模路网下的仿真实验,研究 2 种策略下的AV停车行为及路网运行状态变化.仿真结果表明,静态收费策略和动态收费策略下的AV空载行驶里程分别减少了 20.16%和 10.85%,车辆总延误分别降低了 39.80%和 43.52%;动态收费策略能够灵活地根据路况变化进行实时调整,路网运行效率提升更显著.
Abstract
A multi-agent parking simulation framework was constructed in order to formulate autonomous vehicle(AV)parking demand management strategies.Two charging strategies for empty-load driving were proposed:a static charge based on driving distance and a dynamic charge based on road congestion levels.Rate calculation method was analyzed.Cost functions for parking lots,residential parking,and continuous empty cruising were established under these charging policies.A logit model was used to describe the choice behavior under different parking modes.The simulation of urban mobility(SUMO)was used to conduct a large-scale road network simulation experiment in Nanning's main urban area.AV parking behavior and road network operation under both strategies were analyzed.The simulation results showed that the empty-load driving mileage of AVs decreased by 20.16%and 10.85%under the static and dynamic charging strategies,respectively.Total vehicle delay decreased by 39.80%and 43.52%,respectively.The dynamic charging strategy was adjustable in real-time based on road conditions,and operational efficiency of the road network was significantly enhanced.
关键词
停车空载收费/自动驾驶汽车/多智能体模拟/SUMOKey words
empty-load parking charge/autonomous vehicle/multi-agent simulation/SUMO引用本文复制引用
基金项目
中央高校基本科研业务费专项资金资助项目(2242020K40063)
江苏省研究生科研与实践创新计划资助项目(KYCX20_0137)
浙江省自然科学基金资助项目(LTGG23E080005)
出版年
2024
NETL
NSTL
万方数据