ABSTRACT

Vehicle travel paths provide basic information for improving traffic forecasting models, tracking epidemics transmission, and road construction. Nevertheless, the challenge of recognition and verification still exists, especially in urban dense road networks. This paper proposes a vehicle path recognition model combined with mobile phone data. In path fitting module, the spatio-temporal density-based clustering algorithm and Gaussian filter were combined to smooth the position fluctuations of mobile phone data; then non-uniform rational B-splines were used to fit travel paths. In path recognition module, the modified probabilistic map matching algorithm was used to match fitting knots to road networks; then matching results were repaired considering the road network topology and the direction angles. The results were verified from trip lengths, urban environments, and road categories. The recognition accuracy was around 90%, 24.22% higher than that of existing methods. The error rate was around 6%, 30.28% lower than that of existing methods.

摘要

车辆行驶路径为改进交通预测模型、跟踪流行病传播和道路建设提供了基础信息。尽管如此，识别和验证的挑战仍然存在，尤其是在城市密集的道路网络中。本文提出了一种结合手机数据的车辆路径识别模型。在路径拟合模块中，结合基于时空密度的聚类算法和高斯滤波，平滑手机数据的位置波动；然后使用非均匀有理 B 样条来拟合行进路径。在路径识别模块中，使用改进的概率地图匹配算法将拟合节点与道路网络进行匹配；然后根据路网拓扑和方向角对匹配结果进行修复。结果从行程长度得到验证，城市环境和道路类别。识别准确率在90%左右，比现有方法提高24.22%。错误率在6%左右，比现有方法低30.28%。