关键词: 交通工程, 交通仿真, 元胞自动机, 自然轨迹数据, Kalman滤波, K-means聚类

Abstract: To investigate the law of vehicle operation in the merging area of urban expressways, this paper proposes a cellular automata simulation model of the confluence area considering the heterogeneity of driving behavior based on vehicle natural trajectory data. The model divides the confluence into the upstream area, merging area, downstream area, and these three areas are composed of 11 roadway sections. This paper first uses the Kalman filter algorithm to denoise the natural trajectory data, and then calculates the driving behavior characteristic parameters of each vehicle. The K-means clustering analysis is also performed and the clustering effect evaluation index Silhouette coefficient is introduced to divide driving behavior into four typical types: conservative-cautious, aggressive-cautious, conservativereckless, and aggressive- reckless. Based on the classification results, the paper develops a car- following model that considers acceleration and random slowdown probability heterogeneity and a multi-level heterogeneous lane-changing model that considers the safety distance and decision-making of lane changing behavior. In the context of each space occupancy rate, the model is simulated using Matlab software, and the traffic volume, density, speed, space-time position, lane changing frequency and other parameters of the lanes in the merging area are analyzed under the conditions of homogeneous driving behavior and heterogeneous driving behavior. The simulation results show that when the space occupancy is 10% to 20% , the homogeneous traffic flow is more likely to produce local traffic congestion and traffic flow failure conditions than the heterogeneous traffic flow. The peak traffic volume of thehomogeneous traffic is 27.1% lower than the heterogeneous traffic volume. With the increase in space occupancy, the driving frequency of homogeneous vehicles and heterogeneous vehicles both show an increase-steady-decrease trend, while the maximum value of lane-changing frequency for heterogeneous driving behavior is 20.74% higher than that of homogeneous traffic flow

Key words: traffic engineering, traffic simulation, cellular automata, natural trajectory data, Kalman filtering, K-means clustering