考虑后视和最优速度记忆的跟驰模型及仿真  

Numerical simulation of car-following model considering optimal velocity changes with memory and backward looking effect

作　　者：李腾龙[1] 惠飞[1] LI Tenglong;HUI Fei(College of Information Engineering, Chang’an University, Xi’an 710064, China)

机构地区：[1]长安大学信息工程学院,西安710064

出　　处：《计算机工程与应用》2017年第12期249-254,270,共7页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61603058);陕西省青年科技新星项目(No.2015KJXX-24)

摘　　要：为提高交通流的稳定性,在考虑后视效应和速度差信息(Backward Looking and Velocity Difference,BLVD)模型的基础上,综合考虑后视和最优速度记忆效应,提出了一个扩展的跟驰模型。采用线性稳定性分析,推导出该模型的交通流稳定判据,发现在模型中引入后视和最优速度记忆效应的共同作用后,交通流的稳定区域有明显增大。通过数值仿真验证了理论分析,仿真结果表明:在初始扰动相同的条件下,与BLVD模型相比,新提出的扩展模型具有更好的交通流致稳性能。最后,使用NGSIM数据对所提出的跟驰模型进行参数标定和评价,证明其能更准确地刻画车流演变规律。In order to improve the stability of traffic flow,an extended car following model is proposed by consideringthe effect of backward looking and the optimal velocity changes with memory,in terms of the Backward Looking andVelocity Difference(BLVD)model.The linear stability condition of the presented model is obtained by using the linearstability analysis.As the optimal velocity changes and backward looking effects are considered,the stable area is obviouslyenlarged.Then,the point is verified by numerical simulation.Compared with the BLVD model,simulation results showthat the extended model can further improve the traffic flow stability under the same initial disturbance conditions.Finally,the proposed car-following model is calibrated and evaluated using the Next Generation Simulation(NGSIM)data.Theresults prove that the extended model can describe the evolution of traffic flow more accurately.

关 键 词：交通流 跟驰模型 线性稳定性分析 数值仿真 参数标定 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]