Near-crash risk identification and evaluation for takeout delivery motorcycles using roadside LiDAR.

The proliferation of motorcycles in urban areas has raised concerns regarding traffic safety. However, traditional sensors struggle to obtain precise high-resolution trajectory data, which hinder the accurate identification and quantification of near-crash risks for takeout delivery motorcycles. To fill this gap, this study presents a novel approach utilizing roadside light detection and ranging (LiDAR) to identify and evaluate the risk of near crashes of takeout delivery motorcycles. First, a trajectory amendment method incorporating speed and steering angle was introduced to enhance the accuracy and continuity of the trajectory prediction. Second, a trajectory prediction method combining the steering intention and a repulsive force model was proposed for near-crash risk prediction. Subsequently, a near-crash identification method was developed that relied on the closest distance and risk radius. Finally, near-crash risk fields were constructed to quantify risk levels by leveraging velocity, position, and weight. The experimental results demonstrated 92.10 % accuracy in intention prediction, with mean absolute error (MAE) and root mean square error (RMSE) values of 0.53 m and 0.45 m, respectively. In addition to its higher accuracy, the proposed method makes it easier to quantify near-crash risk and supports a proactive approach for visualizing and analyzing traffic safety.