基于车辆身份感知数据的路段轨迹重构方法研究

车辆轨迹蕴含着大量丰富的交通流时空信息，对于全面解构城市交通路网运行具有至关重要的意义.传统车辆轨迹重构模型大多基于定点线圈检测数据或者浮动车轨迹数据作为输入数据，并且普遍未考虑过饱和交通状态.本文提出了一种基于车辆身份感知数据的车辆路段轨迹重构方法，通过构建一种绿灯相位回溯框架，基于交通流激波理论分段重构车辆行程轨迹，每次回溯过程包含两个主要步骤，即估计车辆状态和分状态重构车辆行程轨迹；然后在Paramics 微观交通仿真平台上对本方法模型的准确性进行了验证.结果表明，该方法在各种饱和状态下均能达到令人满意的应用效果.

Vehicle Trajectory Reconstruction in Signalized-link Using Vehicle Identification Data

Vehicular trajectory data provide a detailed picture of the whole traffic dynamics in time and space, which are critical in analyzing traffic characteristics. However, most of existing methods upon reconstructing vehicle trajectory are carried out using data from inductive loop detectors or/and mobile sensors, and oversaturated conditions are always ignored. In this paper, we present a vehicle identification data- based trajectory reconstruction method for signalized-link, by constructing a phase-to-phase backtracking framework and using the shockwave theory to reconstruct vehicular trajectory segments involved in each backtracking step; in addition, each step consists of two main operations, vehicle state estimation and trajectory reconstruction for two states of vehicles separately. The proposed method is validated using micro-simulation data from Paramics, and the experimental results is satisfactory in both normal condition and over-saturated condition.