Research on Traffic Flow Prediction at Intersections Based on DT-TCN-Attention

Abstract

Traditional nonintelligent signal control systems are typically used in road traffic signal systems, which cannot provide optimal guidance and have low traffic efficiency during rush hour. This study proposes a traffic signal phase dynamic timing optimization strategy based on a time convolution network and attention mechanism to improve traffic efficiency at intersections. The corresponding optimization was performed after predicting traffic conditions with different impacts using the digital twinning technique. This method uses a time-convolution network to extract the cross-time nonlinear characteristics of traffic data at road intersections. An attention mechanism was introduced to capture the relationship between the importance distribution and duration of the historical time series to predict the traffic flow at an intersection. The interpretability and prediction accuracy of the model was effectively improved. The model was tested using traffic flow data from a signalized intersection in Shangrao, Jiangxi Province, China. The experimental results indicate that the model generated by training has a strong learning ability for the temporal characteristics of traffic flow. The model has high prediction accuracy, good optimization results, and wide application prospects in different scenarios.