Modeling and Optimization of Interaction Behavior between Right Turning Motor Vehicles and Pedestrians at Signalized Intersections based on Machine Learning

This project first conducted a comprehensive analysis of the interaction process between pedestrians and right turning motor vehicles and its influencing factors through a summary of existing literature and observation of actual situations. On the basis of comprehensive and accurate data, machine learning algorithms were used to model and predict the interaction behavior between pedestrians and right turning motor vehicles. By comparing different algorithms, the optimal models for predicting the yielding behavior of right turning motor vehicles and pedestrian crossing behavior in the current dataset were obtained. According to the model evaluation report, the obtained models have better predictive performance on the test set. And based on the obtained optimal model, the feature importance ranking was carried out for the pedestrian crossing model and the right turn motor vehicle yielding model, and the importance scores of the influencing factors of pedestrian crossing behavior and right turn motor vehicle yielding behavior were obtained respectively. Finally, a comparison was made between the modeling effects of the crossing gap feature group and the speed distance feature group, further clarifying the importance of the speed distance index for right turning motor vehicles in predicting interactive behavior.