摘要
针对现有跟驰模型因未能综合考虑道路坡度与多前车信息的协同作用,从而无法准确刻画车路协同环境下车辆跟驰行为的问题,提出了车路协同环境下考虑坡度与双前车信息的跟驰模型.首先,通过分析车路协同交通环境与传统交通环境之间的差异,解析了车路协同环境下道路坡度与多前车信息对车辆运行的影响机制,建立了综合考虑坡度与双前车信息的跟驰模型;随后,基于微扰动法对模型进行了线性稳定性分析,分别获取不同道路坡度和前车权重系数时的稳定性临界曲线,进而得到了上坡和下坡2种工况下相关系数对交通流稳定性的影响规律.最后,为验证模型理论分析的正确性,分别对车辆启动过程和微扰动场景进行数值仿真.仿真结果表明,模型不仅能准确刻画车辆在坡道上的跟驰行为,还能减小速度、加速度和车头间距波动,缩短交通流恢复稳定状态的时间,增强交通流的稳定性.
To solve the problem that the existing car-following model cannot accurately describe the car-following behavior in the vehicle-infrastructure cooperation environment because it fails to comprehensively consider the synergy between road gradient and multiple preceding vehicles,a car-following model considering road gradient and double preceding vehicle information in the vehicle-infrastructure cooperation environment is proposed.Firstly,by analyzing the differences between the vehicle-infrastructure cooperation traffic environment and the traditional traffic environment,the influence mechanism of road gradient and multi-preceding vehicle information on vehicle operation in the vehicle-infrastructure cooperation environment is analyzed,and a car-following model considering road gradient and double preceding vehicle information is established.Then,the linear stability of the model is derived based on the micro-perturbation method,and the critical curves of model stability at different road gradients and weight coefficients of the preceding vehicle are obtained.Furthermore,the influence law of the correlation coefficients on the traffic flow stability under the two working conditions of uphill and downhill is obtained.Finally,to verify the correctness of the model,the vehicle starting process and the micro-disturbance scene are numerically simulated.The simulation results show that the model can accurately describe the car-following behavior on the ramp,reduce the fluctuations of velocity,acceleration and headway,shorten the time for the traffic flow to return to a stable state,and enhance the stability of the traffic flow.
作者
陈龙
刘孟协
蔡英凤
刘擎超
孙晓强
Chen Long;Liu Mengxie;Cai Yingfeng;Liu Qingchao;Sun Xiaoqiang(Automotive Engineering Research Institute,Jiangsu University,Zhenjiang 212013,China)
出处
《东南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2022年第4期787-795,共9页
Journal of Southeast University：Natural Science Edition
基金
国家自然科学基金资助项目(U20A20331,U20A20333).
关键词
交通工程
跟驰模型
车路协同
稳定性分析
信息融合
traffic engineering
car-following model
vehicle-infrastructure cooperation
stability analysis
information fusion
