交通运输工程与信息学报2024，Vol.22Issue(4)：25-36,12.DOI:10.19961/j.cnki.1672-4747.2024.07.008
自动驾驶电动共享车系统自适应集中调度优化
Simulation optimization of adaptive relocation for shared autonomous electric vehicles
摘要
Abstract
In the vehicle-relocation problem of shared vehicles,the traditional threshold-relocation method based on inventory theory requires scheduling vehicles immediately when the number of ve-hicles in an area exceeds the threshold,which generates a large number of relocation tasks and reduc-es vehicle availability.Therefore,this paper proposes a vehicle adaptive centralized relocation opti-mization method in a simulation optimization framework for shared autonomous electric vehicle(SAEV)systems to improve the traditional threshold relocation method.Specifically,a discrete-event simulation model of the SAEV system is first constructed.The model divides the operation ar-ea with the station as the center,divides the user demand into intraregional user demand and interre-gional user demand,and achieves flexible service by automatically picking up and dropping off users by vehicles.Then,in the simulation model,a fixed time interval is set to solve a transportation prob-lem based on regional thresholds,and a centralized vehicle relocation scheme is determined to re-place the traditional threshold-triggered single-vehicle relocation scheme.Finally,the simulation opti-mization framework and the BO-SPSA algorithm are designed to efficiently solve the relocation threshold to maximize the daily profit of the SAEV system.The algorithm optimizes the parameters of the simultaneous perturbation stochastic approximation(SPSA)algorithm by Bayesian optimiza-tion(BO)to achieve a faster and efficient solution.The Chengdu case shows that(1)BO-SPSA can be solved more quickly and efficiently than other algorithms.(2)The small-scale and large-scale op-eration scenarios simultaneously verify that the adaptive centralized relocation strategy proposed in this paper can serve more users,reduce the number of scheduling trips,and obtain more operational profits than the traditional relocation strategy.(3)Regardless of the scale of the operation scenario,when the demand is small,the use of fast charging piles cannot effectively improve the profitability of the system.However,with the increase of the demand scale,the fast charging pile can better en-hance the ability of the system to balance the unbalanced user demand and improve the service level and profit of the system.关键词
智能交通/自适应调度/仿真优化/共享出行/自动驾驶汽车/贝叶斯优化Key words
intelligent transportation/adaptive relocation/simulation optimization/shared mobility/autonomous vehicle/Bayesian optimization分类
交通工程引用本文复制引用
侯博宇,刘洪汛,胡路..自动驾驶电动共享车系统自适应集中调度优化[J].交通运输工程与信息学报,2024,22(4):25-36,12.基金项目
国家自然科学基金项目(72371209,62203367) （72371209,62203367）
四川省国际科技创新合作项目(2024YFHZ0266) （2024YFHZ0266）
