A Graph Neural Network-based Traffic Flow Prediction System with Enhanced Accuracy and Urban Efficiency

Abstract

This research culminates in a robust Traffic Flow Prediction System poised to redefine the landscape of Intelligent Transportation Systems (ITS). Our findings highlight the substantial promise of this system through a meticulously structured methodology spanning data generation, dynamic network construction, multi-modal data integration, and the employment of state-of-the-art Graph Neural Networks (GNNs). Notably, the "Current Framework" stands out, demonstrating superior performance over alternative regression models, substantiated by a remarkable 35% reduction in Mean Squared Error (MSE) and a commendable 7% increase in R-squared (R²). Nevertheless, this system is not without its caveats. Ongoing model refinement, adaptability to the ever-evolving traffic landscape, and scalability considerations are essential for future exploration. These achievements usher in a new era for traffic management, with the potential to curtail congestion by up to 20%, bolster safety measures, and usher in an era of enhanced urban transportation efficiency.