Predicting spatio-temporal traffic flow: a comprehensive end-to-end approach from surveillance cameras

Abstract

Traffic flow forecasting is an essential aspect of intelligent traffic management. It enables timely and proactive management of modern transport systems, increasing efficiency and resilience. However, accurately predicting short-term traffic flow is challenging due to its uncertain and interconnected nature. Traditional methods like loop detectors and high-resolution cameras have limited scalability. To address this, we propose a two-stage approach using low-resolution surveillance cameras. The first stage involves a vision-based data extraction module with calibration, vehicle detection, and tracking. Integration of Region of Interest, fine-tuning, and post-processing improves the robustness of low-resolution videos. In the second stage, a novel deep learning model extracts spatio-temporal features from historical traffic data for short-term flow prediction. The proposed model outperforms the STGCN model, achieving an 11.19% increase in MAE, a 12.37% improvement in RMSE and a 4.97% reduction in inference time. These advances highlight its potential for further research and applications in the field.