基于安全距离的元胞自动机交通流模型研究

随着交通拥堵状况日益显著，整体交通安全性下降，交通事故率逐渐增大.基于提高驾驶安全性考虑，细化元胞长度，引入被广泛证明在描述车辆驾驶行为方面具有很高精度的Gipps 安全距离规则，对NaSch模型进行改进，提出一个新的基于安全距离的元胞自动机交通流模型.采用实测数据对模型进行标定和评估，进一步对模型进行数值模拟分析.模型评估结果显示，新建立的模型相对NaSch 模型精度更高.数值模拟结果表明，改进模型能够很好地表现交通流特性，再现实际交通中的自由流、同步流及拥堵流等交通现象.此外，还发现驾驶员对前车最大减速度估计过高时，会导致道路通行能力下降，而驾驶员对自身车辆最大减速度估计过高时，会在一定程度上增大道路的通行能力，但是很可能会造成不安全的驾驶行为，增加了事故率.

Cellular Automata Model Based on Safety Distance

With the traffic congestion increasing significantly, traffic safety level declines and traffic accident rate increases gradually. To improve driving safety, the length of the cellular cells is fined, and the Gipps’safe distance rule is introduced to improve the NaSch model, further, a new cellular automata traffic flow model is proposed. The Gipps’safe distance rule is widely proved to have good performance in describing the vehicle driving behavior. In addition, we use the field data to calibrate and evaluate the proposed model. The numerical simulation analysis is carried out to analyze the model. Model evaluation results show that the performance of the new model is better than NaSch model. The simulation results show that the improved model can describe the traffic flow characteristics well and can reproduce free flow, synchronized flow, congestion and other traffic phenomenon in the real traffic flow. Furthermore, the study also found that the drivers’overestimation of the maximum deceleration of vehicle ahead will lead to decreased road capacity. However, the drivers’overestimation of their own vehicle maximum deceleration will increase the capacity of the road, but is likely to cause unsafe driving behaviors and increase accident rate.