基于时空轨迹聚类的交叉口机非碰撞风险预测OA

To address the safety risk prevention requirements caused by right-of-way conflicts in the mixed traffic environment of motor and non-motorized vehicles at urban intersections,a collision risk prediction method for motor and non-motorized vehicles integrating trajectory clustering and deep learning was proposed in this paper to improve the accuracy of traffic safety assessment at intersections.First,multi-type vehicle trajectory data in drone videos were extracted based on the DataFromSky software to construct a high-precision spatiotemporal dataset containing vehicle coordinates,velocities,and accelerations,and high-risk scenario trajectories were screened through the feature analysis of risk scenarios.Second,the DBSCAN spatiotemporal trajectory clustering algorithm was adopted,and the spatial radius(REps)and minimum sample size(DMinPts)were optimized in combination with the silhouette coefficient method to divide the non-motorized vehicle trajectories into conservative and aggressive types.Then,an LSTM-based time series prediction model was designed to realize the multi-step prediction of future spatiotemporal trajectories of non-motorized vehicles,and a spatiotemporal collision risk quantification model based on Euclidean distance and TTC threshold was constructed to dynamically evaluate potential collision risks.The test results indicate that the DBSCAN clustering algorithm can effectively identify the driving behavior patterns of non-motorized vehicles(with a silhouette coefficient of 0.256 6),the LSTM model can accurately predict vehicle trajectories for the future three time steps,and the spatiotemporal collision risk quantification model can proactively identify high-risk interaction scenarios.The research results can provide a quantitative decision-making basis and effective technical support for real-time risk prevention and control in mixed traffic environments.