高速公路异构交通流HDV建模及其特征

为了更好地模拟高速公路中网联自动驾驶车辆(connected and autonomous vehicles, CAV)与人工驾驶车辆(human-driven vehicles, HDV)组成的异构交通流,开展高速公路异构交通流跟驰与换道行为研究。首先,以NGSIM轨迹数据集为基础分析HDV的微观交通特性,根据驾驶人在相同车头时距条件下的加、减速策略,将驾驶人分为保守型、普通型和激进型3类。其次,从驾驶人感知判断结果存在不确定性的角度,结合车辆跟驰数据分析不同类型驾驶人车头时距和速度判断误差的特性,同时引入信息效用理论模拟驾驶人对CAV认知程度的变化及其对驾驶决策的影响,在智能驾驶人模型(intelligent driver model, IDM)和对称双车道元胞自动机换道模型的基础上,提出一种改进的HDV跟驰与换道模型。最后,将改进模型与IDM模型预测结果进行对比分析,并通过MATLAB对高速公路异构交通流特征进行仿真分析。研究结果表明:改进模型能够更准确地模拟车辆跟驰行为,相比于IDM模型,保守型HDV跟驰模型平均绝对误差MAE降低24.7%,均方误差MSE降低11.9%,皮尔逊相关系数PCCs提高2.6%;普通型HDV跟驰模型MAE降低45.6%,MSE降低38.6%,PCCs提高4.0%;激进型HDV跟驰模型MAE降低41.2%,MSE降低45.9%,PCCs提高0.4%;在接近自由流状态下,HDV在车流中的占比对异构交通流的速度、流量和稳定性等特征影响较小,随着密度的增大HDV占比对交通流的影响也随之增大,直到达到临界密度,车辆组成对交通流的影响开始减小;在相同交通流密度下,HDV在车辆组成中的占比与交通流的速度、流量和稳定性呈负相关性。模型丰富了对异构交通流HDV跟驰与换道行为的研究,在高速公路HDV和CAV混行的异构交通流的交通管理和基础设施设计等方面具有一定参考价值。

An Improved Discrete-Time Model for Heterogeneous High-Speed Train Traffic Flow

This paper aims to present a simulation model for heterogeneous high-speed train traffic flow based on an improved discrete-time model（IDTM）.In the proposed simulation model,four train control strategies,including departing strategy,traveling strategy,braking strategy,overtaking strategy,are well defined to optimize train movements.Based on the proposed simulation model,some characteristics of train traffic flow are investigated.Numerical results indicate that the departure time intervals,the station dwell time,the section length,and the ratio of fast trains have different influence on traffic capacity and train average velocity.The results can provide some theoretical support for the strategy making of railway departments.