考虑驾驶行为异质性的城市快速路合流区仿真模型

为研究城市快速路合流区车辆运行规律，基于车辆自然轨迹数据，提出考虑驾驶行为异质性的合流区元胞自动机仿真模型。模型将合流区分为上游区域、合流区域及下游区域，3个区域由11条路段组成。首先，利用Kalman滤波算法对自然轨迹数据进行降噪处理；然后，计算每辆车驾驶行为特征参数并进行K-means聚类分析，结合聚类效果评价指标Silhouette系数将驾驶行为分为：保守-谨慎型、激进-谨慎型、保守-轻率型及激进-轻率型这4种类型；最后，依据分类结果， 建立考虑加速度、随机慢化概率异质性的跟驰模型和考虑换道安全间距、换道决策的多级异质性换道模型。在各空间占有率的情境下，基于Matlab进行数值仿真，统计同质驾驶行为和异质驾驶 行为条件下，合流区域车道的流量、密度、速度、时空位置及换道频率等参数。仿真结果表明：在空间占有率为10%~20%时，同质交通流相比异质交通流更容易产生局部交通拥堵和交通流失效情境，并且同质交通流量峰值比异质交通量小27.1%；随着空间占有率的增加，同质车辆和异质车辆驾驶频率均呈现增加-稳定-下降的趋势，而异质驾驶行为换道频率的极大值比同质交通流高 20.74%。

Urban Expressway Merging Area Simulation Model Based on Heterogeneity of Driving Behavior

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