Vehicle Trajectory-Based Traffic Volume Prediction on Urban Roads With Fast-Communication License Plate Recognition Data

Abstract

Real-time traffic volume prediction is critical for proactive traffic control and road guidance via fast-communication networks. However, existing research relies on historical traffic volume and fails to consider vehicle travel route choices, leading to lower prediction accuracy and insufficient capture of real-time travel dynamics. To address this limitation, we propose a novel approach for real-time traffic volume prediction on urban links that incorporates vehicle travel trajectories extracted from recorded license plate recognition data using Radio Frequency Identification. A Recurrent Neural Network is used to identify route patterns and predict future routes, and link traffic volume is estimated by aggregating all predicted vehicle trajectories that pass through each base station. Our approach achieves a high prediction accuracy with a mean value of 85.19% and a standard deviation of 5.29%. Comparing our approach with a traditional Recurrent Neural Network based on historical traffic volume data, the vehicle trajectory-based approach yields an average accuracy improvement of 39.70%. These results highlight the superiority of our approach for predicting traffic volume in real-time and demonstrate its potential as a valuable tool for traffic control and road guidance.