顾及交叉路口和车辆模态的轨迹重构与分析

Trajectory Reconstruction Based on Road Intersections and Vehicle Modes and Its Analysis

由于数据传输和存储成本的限制,大多数轨迹数据采样率低且不确定,而城市精细模型往往需要高频轨迹数据,例如,微观交通碳排模型需要时间间隔为1 s的轨迹数据。因此,对低频轨迹数据进行高频重构有非常重要的意义。提出了一种顾及交叉路口和车辆模态的轨迹重构方法,采用高频轨迹数据训练车辆运动模态的理论概率模型,结合交叉路口来确定低频轨迹点之间的模态序列,并通过遗传算法求解理论概率模型来完成各模态时间和距离的分配,进而完成轨迹点的高频重构。结果表明,所提方法重构轨迹的均方根误差(root mean square error,RMSE)值相较于传统的数学插值方法降低了62.9%,相较于未考虑交叉路口的模态方法,降低了12.2%。因此,该方法在低频轨迹数据重构中具有很好的应用价值。

Objectives:Due to the limitation of data transmission and storage cost,the sampling rates of most trajectories are low and uncertain.However,detailed urban models often require high-frequency trajectory data,for example,microscopic vehicle emission models often require trajectory data with a time interval of 1 s.Therefore,it is of great significance to reconstruct the trajectory data using the technique of interpolation.Methods:We propose a method to interpolate low-frequency trajectories considering the road intersections and vehicle modes.First,high-frequency trajectory data are used to train the theoretical probability model of vehicle motion modes.Second,the road intersections are used to determine the mode sequence between low-frequency trajectory points.Third,the theoretical probability model is solved by the genetic algorithm to calculate the distribution of time and distance of each mode,and then complete the high-frequency reconstruction of trajectory points.Results:The results suggest that the proposed method performs better than the conventional interpolation method by decreasing the root mean square error(RMSE)value with 62.9%,and better than the mode method that does not consider the road intersection by reducing the RMSE value by 12.2%.Conclusions:Therefore,the proposed method is valuable for the reconstruction of low-frequency vehicle trajectories.