基于车牌自动识别数据的车辆OD轨迹还原

交通信息采集设备捕获的车牌数据是研究车辆出行轨迹的天然载体, 可用于追踪、还原车辆在路网的完整出行轨迹. 但是, 受技术与设备覆盖等限制, 采集的时序车牌数据总是呈现出稀疏不完整的性质. 为充分利用车牌数据, 研究并提出一种基于稀疏车牌数据的OD轨迹还原算法. 该算法首先以间隔时间阈值分离车辆的OD出行链. 然后基于K则最短路径算法(KSP)生成多个近似的候选轨迹. 最后, 采用变分自编码器(VAE)选择决策最优估计轨迹, 以获取车辆完整出行轨迹. 该方法已在杭州市萧山区实际交通小区进行实施验证. 结果显示, 所提出的还原算法在测试小区可达95%的综合准确率. 此外, 在节点缺失率高、摄像点位覆盖率低的情况下, 重构算法依然具备良好的性能(高于50%).

Vehicle OD Trajectory Restoration Based on Automatic License Plate Recognition Data

The license plate data captured by a traffic information collection device is a natural carrier for studying the trajectory of a vehicle and is useful to track and restore the complete trajectory of the vehicle on the road network. However, due to the limitations of technology and device coverage, the collected time series data of license plates is inevitably sparse and incomplete. To make full use of license plate data, this study proposes an origin-destination (OD) trajectory restoration algorithm based on sparse license plate data. The algorithm first separates the OD trip chain of the vehicle by the interval time threshold. Then, it generates multiple approximate candidate trajectories with the K-shortest path (KSP) algorithm. Finally, a variational autoencoder (VAE) is used to select the optimal estimated trajectory for decision-making so that the complete trajectory of the vehicle can be obtained. This method has been implemented and verified in an actual transportation analysis zone in Xiaoshan District, Hangzhou City. The results show that the proposed method achieves a comprehensive accuracy of 95%. Additionally, the reconstruction method still has sound performance (higher than 50%) in the case of a high node loss rate and low camera coverage.