无信号环形交叉口机非冲突机器学习预测方法.

In order to efficiently and accurately predict traffic conflicts between motorized and non-motorized vehicles at unsignalized roundabouts, a combined prediction model (SVR-GA-BP) based on genetic algorithm optimized BP neural network (GA-BP) and support vector machine regression (SVR) was proposed. The high-precision mixed traffic flow trajectory data was collected using drone video at an unsignalized roundabout. The video recognition software Tracker was used to extract the trajectory data of motorized and non-motorized vehicles conflicts. The time to collision (TTC) parameter was chosen as the discriminant index to determine the severity of motorized and non-motorized vehicles conflicts. Based on partial correlation analysis, the traffic volume, average speed and percentage of heavy vehicles were determined as the significant influencing factors for conflicts. Five evaluation metrics, such as root mean square error (RMSE) and mean absolute error (MAE), were selected to analyze the accuracy of the predicted values of SVR model, BP neural network and SVR-GA-BP model. The results show that the accuracy of the combined model in minor conflict prediction is 97. 1%, which is 6. 9% and 2. 5% higher than that of SVR and BP model respectively. The accuracy of the combined model in serious conflicts prediction is 96. 1%, which is 7. 3% and 5. 1% higher than that of SVR and BP model respectively. It can be seen that the SVR-GA-BP combined model can effectively predict the motorized and non-motorized vehicles traffic conflict of unsignalized roundabout with the highest accuracy, which can provide reference for the safety evaluation of the same type of intersections. [ABSTRACT FROM AUTHOR]