考虑后视效应和多前车信息的跟驰模型

网联车跟驰模型的研究可为未来实施大规模的实地测试提供模型参考,已成为交通流及智能交通领域的研究热点.为了更好地研究智能网联车的跟驰特性,在MVD模型的基础上,提出了一种考虑后视效应和多前车信息的跟驰模型(BL-MVDAM),利用线性稳定性分析方法推导出BL-MVDAM模型的交通流稳定性判断依据,并分别分析了模型中各参数对系统稳定性的影响,给出分析结果并进行了数值仿真实验.仿真实验选取在环形道路上给行驶过程中的车队施加轻微扰动,并根据跟驰车对后车的关注程度P和前车数量k设计数值模拟实验,当其他条件一致时,本文模型相比FVD,MVD,OMVC和BLVD模型,BL-MVDAM模型中车队的速度波动率较小,尤其是当P=0.8,k=3 时,车队速度平均波动率最小可以达0.24％,实验分析结果表明,所提出模型在引入后视效应和多前车信息后,具备更优的稳定区域,能较好地吸收扰动且有利于增强车队行驶的稳定性.

Car-Following Model Considering Multiple Headway Information and Backward Looking Effect

The research on the car-following model of Connected and Autonomous Vehicles (CAVs) can provide a model reference for large-scale field testing in the future, and the model has become a research hotspot in the field of traffic flow and intelligent transportation. To better study the car-following characteristics of CAVs, this study proposes a car-following model BL-MVDAM considering the multiple preceding vehicle information and backward looking effect on the basis of the MVD model. The judgment basis for the traffic flow stability of the BL-MVDAM model is deduced by linear stability analysis. The effects of different parameters in the model on the system stability are analyzed. The analysis results are verified by a simulation experiment. In the experiment, a slight disturbance is applied to a vehicle group in the car-following process on a circular road. This experiment is designed according to the attention P of a car in the group to the follower and the number k of cars in front. The speed fluctuation of the vehicle group in the proposed model is small in comparison with the FVD, MVD, OMVC and BLVD models under the same initial conditions. Especially, when P is 0.8 and k is 3, the average speed fluctuation can be as low as 0.24%. The experimental results show that the model considering the multiple preceding vehicle information and backward looking effect has a better stability region, which can better absorb the disturbance and enhance the driving stability of a vehicle group.