Urban intersection traffic flow prediction: A physics-guided stepwise framework utilizing spatio-temporal graph neural network algorithms
Yuyan Pan, Fuliang Li, Anran Li, Zhiqiang Niu, Zhen Liu
Abstract
Accurate traffic flow forecasting at urban intersections is critical for optimizing transportation infrastructure and reducing congestion. This manuscript introduces a novel framework, the Physics-Guided Spatio-Temporal Graph Neural Network (PG-STGNN), specifically designed for traffic flow prediction. By integrating the principles of traffic flow physics with advanced spatio-temporal graph neural network algorithms, the framework captures complex spatio-temporal dependencies in traffic networks. PG-STGNN adopts a stepwise approach, addressing key performance metrics like queue formation and signal timing complexities at intersections. To validate its effectiveness, the model was applied to real-world traffic data from the Yizhuang District of Beijing. Compared to traditional models such as ARIMA, KNN, and Random Forest, PG-STGNN significantly improves prediction accuracy, achieving MAPE reductions of 19.9%, 18.6%, 6.1%, 20.7%, 5.0%, 1.8%, and 1.1% against KNN, ARIMA, RF, BP, T-GCN, STGCN, and ST-ED-RMGC, respectively. With the lowest MAPE (9.452%), MAE (2.485), and RMSE (4.364), PG-STGNN demonstrates superior prediction performance. These results underscore its potential to provide reliable short-term traffic forecasts, offering essential insights for the strategic planning and management of urban intelligent transportation systems.