关键词: 交通工程, 交互风险识别, 行车风险场, 短交织区, 风险可视化

Abstract: Traditional risk identification models cannot continuously identify the interaction risk between vehicles when changing lanes. In order to accurately and intuitively identify the interaction risk between vehicles driving in short weaving sections of expressways, this study first collects vehicle trajectory data in the short weaving area of expressways by using drones and Tracker and filters out pairs of following vehicles and pairs of lane changing vehicles. By considering the vehicle area, the risk difference between the front and rear of the vehicle when moving forward, the risk difference between the left and right when turning, and the lateral distance between vehicles, the existing driving safety field model DRF (Driving Safety Field) is adaptively improved, and the model parameters are calibrated using a genetic algorithm and the Polankov model. To verify the effectiveness of the model and calibrated parameters, the improved driving risk field model is compared with the reciprocal of headway (THWI) and the reciprocal of collision time (TTCI) in identifying the following interaction risk and lane changing interaction risk between vehicles, and verify the effectiveness of the model. The improved driving risk field model is compared with the reciprocal of headway (THWI), the reciprocal of collision time (TTCI), and the driver's recognition of the risk of following and lane changing interactions between vehicles. The results show that the proposed model consistent with the driver's driving psychology better compared to THWI and TTCI, and perceives changes in risk before the driver. The recognition rate of vehicle lane changing interaction risk is increased by 52.45% compared to THWI and 83.66% compared to TTCI. This model performs better in identifying lane changing interaction risk. Finally, based on the proposed model, the risks generated by the joint action of multiple vehicles in the short weaving area can be visualized, which can assist traffic management departments in identifying key areas that require refined organization, and can also serve as a visualization tool for evaluating the effectiveness of management measures for improvement.

Key words: traffic engineering, interaction risk identification, driving risk field, short weaving areas, risk visualization