Heterogeneous traffic flow modeling with drivers’ timid and aggressive characteristics

The driver’s characteristics(e.g.,timid and aggressive)has been proven to greatly affect the traffic flow performance,whereas the underlying assumption in most of the existing studies is that all drivers are homogeneous.In the real traffic environment,the drivers are distinct due to a variety of factors such as personality characteristics.To better reflect the reality,we introduce the penetration rate to describe the degree of drivers’heterogeneity(i.e.,timid and aggressive),and proceed to propose a generalized heterogeneous car-following model with different driver’s characteristics.Through the linear stability analysis,the stability conditions of the proposed heterogeneous traffic flow model are obtained based on the perturbation method.The impacts of the penetration rate of drivers with low intensity,the average value and standard deviation of intensity parameters characterizing two types of drivers on the stability of traffic flow are analyzed by simulation.Results show that higher penetration of aggressive drivers contributes to traffic flow stability.The average value has a great impact on the stability of traffic flow,whereas the impact of the standard deviation is trivial.

一种考虑个性化驾驶风格的车辆跟驰模型

介绍了一个全新的考虑驾驶员原始的驾驶风格体现的车辆跟驰模型,使用非线性自回归神经网络和直接逆模型法对驾驶员实际车辆跟驰行为进行学习,进而完成了对驾驶员在车辆跟驰状态下的个性化建模。通过设计跟驰测试系统和采用能量谱密度作为分类指标对所提出的模型进行了测试和风格聚类。将测试结果与原始驾驶风格数据进行对比,验证了所提出方案的有效性。

Impact of next-nearest leading vehicles on followers’ driving behaviours and traffic stability in mixed traffic

There has been an increase in the number of on-road vehicles of all types,especially in some developing countries.The rise of traffic heterogeneity causes larger mixed traffic conge stion.This study examines the impact of next-nearest leading vehicles on the driving of following drivers in mixed traffic.Although previous studies reported that traffic stability can be improved with the introduction of followers’anticipatory driving that refers to multiple leaders,the effect of anticipatory driving on mixed traffic has not yet been examined.Using data collected from experiments conducted with groups of two and three vehicles,we found that operational delay,maximum acceleration and deceleration of the followers were affected by the presence of next-nearest leaders.In addition,we developed regression models of the affected followers’behaviours with respect to the next-nearest leaders and identified the factors affecting these behaviours.For example,the followers’deceleration is directly affected by the height of the next-nearest leading vehicles.Hence,the model parameters for determining the deceleration of following vehicles should take the height of the next-nearest leading vehicle into consideration.Finally,based on the regression models,we estimated values of parameters in the intelligent driver model when the type of the next-nearest leader was changed.Stability analysis based on these estimated parameters implied that a tall or short next-nearest leader with a large engine power would stabilise traffic when anticipatory driving of followers is possible.