主讲人: | 姜锐教授 |
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地点: | 经管北楼316闽海报告厅 |
主办方: | 太阳集团官方网站入口(邀请人:田丽君) |
开始时间: | 2024-05-20 09:00:00 |
结束时间: | 2024-05-20 12:00:00 |
报告题目: Trajectory planning at a signalized intersection in a mixed traffic environment considering lane-changing of CAVs and stochasticity of HDVs
内容摘要: Connected and automated vehicles (CAVs) are projected to bring significant benefits to traffic efficiency and driving comfort. However, the realization of full CAV penetration rate will take a long time. In this paper, a framework is proposed for planning the trajectories of vehicles at a signalized road section in a mixed traffic environment consisting of CAVs, human-driven vehicles (HDVs) and connected and automated buses (CABs). In the proposed trajectory planning framework (TPF), both the lane-changing (LC) behavior of CAVs and the stochasticity of HDVs are considered. The whole TPF is composed of a planning module, a running module, and a switching module. In the planning module, the mixed integer programming (MIP) models for trajectory planning with/without LCs are formulated to optimize the trajectories of CAVs/CABs. A parsimonious algorithm is designed to determine a suitable planning time horizon for the MIP models. The running module and the switching module are designed to ensure the driving safety of vehicles. To consider the stochasticity of HDVs, the concept of α-trajectory is employed in simulations to produce predicted trajectories of HDVs, while the actual trajectories of HDVs are generated by a stochastic car-following model. A rolling time horizon scheme is applied for TPF to account for the time-varying traffic situations. Numerical experiments under different traffic states and market penetration rates (MPRs) of CAVs/CABs are conducted to validate the performance of the proposed TPF. The average improvement in travel time and fuel consumption can reach up to 28.9 %, 17.8 % and 52.2 %, 35.3% under medium and heavy traffic, respectively, and the average improvement in driving comfort is over 20% in most traffic scenarios.
报告人简介:姜锐,北京交通大学教授。主要从事道路交通流、行人交通流、交通出行行为、网联自动驾驶车辆等方面的研究工作。主持了国家自然科学基金重点项目、优青项目、国家重点研发计划课题等10余项科研项目。在TS、ISTTT、TRB、POM、TRC/D/E、IEEE T ITS、PRE等国内外重要学术期刊和学术会议上发表学术论文100多篇。连续多年入选Elsevier中国高被引学者,论文单篇最高被SCI他引900余次。获教育部自然科学一等奖、安徽省科技二等奖、北京市自然科学二等奖、安徽省自然科学优秀论文一等奖各一项。担任TRB、Transportmetrica A、Physica A等期刊编委。