Modeling and analysis of vehicle path dispersion at signalized intersections using explainable backpropagation neural networks

Abstract

The dispersion of vehicular paths is a common phenomenon in the inner area of signalized intersections due to heterogeneous driver behavior and interactions. This study aims to develop an explainable neural network-based model to describe the vehicle path dispersion by exploring the relationship between the path dispersion and external factors. A backpropagation neural network model was established to analyze the effects of external factors on the dispersion of through and left-turn paths based on real trajectory data collected from 20 intersections in Shanghai, China. Twelve influencing factors in varying geometric, traffic, signalization, and traffic management conditions were considered. The predictive power and transferability of the model were verified by applying the trained model on the four new intersections. The contributions of the influencing factors on the path dispersion were explored based on the neural interpretation diagram, relative importance of influencing factors, and sensitivity analysis to offer explanatory insights for the proposed model. The results show that the mean absolute percentage errors of the path dispersion models for the through and left-turn movements are only 14.67% and 17.65%, respectively. The through path dispersion is primarily influenced by the number of exit lanes, the offset degree between the approach and exit lanes, and the traffic saturation degree on the through lane. In contrast, the path dispersion of the left turn is mainly affected by the number of exit lanes, the left-turn angle, and the setting of guide lines.