摘要
为了探究驾驶员记忆和多前车速度差对交通流的影响,本文基于全速度差模型(full velocity difference model,FVDM),结合驾驶员记忆因素和多前车对跟驰车的作用,构建了一种考虑了驾驶员记忆和多前车速度差的跟驰模型。通过改进模型的线性稳定性特征,得出改进模型的稳定性条件。再对改进模型下的车流启动和制动过程进行仿真,并与FVDM的仿真结果作对比。然后采用微小扰动法对改进模型进行数值仿真,研究驾驶员记忆因素和多前车速度差对交通流稳定性的影响。最后,利用下一代仿真(next generation simulation,NGSIM)数据标定了改进模型的参数,并预测了其加速度。研究结果表明:驾驶员记忆在一定程度上不利于交通流的稳定,而多前车速度差对稳定交通流具有积极作用;与FVDM相比,改进模型的启动延迟和制动延迟分别降低了10.0%和19.0%,预测精度更高,均方根误差降低了24.3%。
This study explores the effects of driver memory factors and multi-vehicle speed differences on traffic flow,constructing a model that integrates these elements based on the full velocity difference model(FVDM).The stability conditions of the model were derived by its linear stability characteristics.The traffic initiation and braking processes under the model were simulated and compared with the simulation results of FVDM.A numerical simulation employing the tiny perturbation method was conducted to analyze how driver memory and speed variance among vehicles affect the stability of traffic flow.Finally,the model's parameters were calibrated,and its predictive capability for acceleration was assessed using NGSIM data.The results show that driver memory factor slightly undermines traffic flow stability,while multi-vehicle speed differences has positive effects on the stability of traffic flow.Compared to the FVDM,the proposed model reduces the start-up delay and braking delay by 10.0%and 19.0%,respectively,and achieves a higher prediction accuracy with a reduction of the root-mean-square error by 24.3%.
作者
尹砚铎
龙科军
谷健
YIN Yanduo;LONG Kejun;GU Jian(Hunan Key Laboratory of Smart Highway and Cooperative Vehicle Infrastructure System,Changsha University of Science&Technology,Changsha 410114,China)
出处
《交通科学与工程》
2024年第2期127-137,共11页
Journal of Transport Science and Engineering
基金
国家自然科学基金项目(52172313)
湖南省自然科学基金青年项目(2021JJ40577)
湖南省教育厅优秀青年项目(20B009)。
关键词
驾驶员记忆
多前车速度差
跟驰模型
数值仿真
driver's memory
speed difference of multiple preceding vehicles
car-following model
numerical simulation
