关键词: 智能交通, 冲突预测, 拓展TTC模型, 多车道交织区, 风险判别, 分区建模, 微观轨迹数据

Abstract:

Intelligent Vehicle Infrastructure Cooperative Systems(IVICS) is one of the emerging technologies to ensure travel security and efficiency. Combining high- precision vehicle trajectory data with machine learning method, a potential risk identification and conflict prediction method for multi-lane interweaving zones is proposed, which can be applied to IVICS. First, the information about the traffic vector position and speed in the interweaving area was extracted from the wide-area perspective based on UAV video. Several partitions were divided, such as the upstream and downstream sections, and the interweaving influence area. Then, the risk discrimination model was constructed by considering the decision behaviors (vehicle- vehicle edge distances, approach rates) and vehicle behaviors (the transverse and longitudinal velocity, acceleration, velocity angle). Risks were evaluated from the number of conflicts per unit area, duration of conflicts, conflict density and other indicators. Finally, traffic conflicts are predicted according to the Naive Bayes model and the Logistic regression model. Compared with the measured data, the prediction accuracy rates of the two models were 74.86% and 87.10% respectively, and the AUC were 0.84 and 0.88 respectively. It showed that the Logistic regression model has better prediction performance. The corresponding results are helpful for traffic management departments to formulate and optimize traffic control schemes and can be applied to IVICS dynamic early warning.

Key words: intelligent transportation, conflict prediction, extending time to collision model, multi-lane weaving area, risk discrimination, zoning modeling, micro-trajectory date