Adaptive Traffic Prediction Model Using Graph Neural Networks Optimized by Reinforcement Learning

Abstract

Traffic prediction is a fundamental challenge in modern urban planning and transportation management, with far-reaching implications for congestion mitigation, resource allocation, and environmental sustainability. Traditional traffic prediction methods, such as statistical models, often suffer from limitations in capturing complex spatiotemporal dependencies, leading to inefficiencies and reduced accuracy. In contrast, Graph Neural Networks (GNNs) offer a more sophisticated approach by effectively modeling the intricate relationships within traffic networks. However, existing GNN-based methods also face challenges, particularly in choosing optimal hyperparameters for diverse traffic scenarios. In this context, we present a novel and effective solution: an adaptive traffic prediction model utilizing Graph Neural Networks (GNNs) optimized through reinforcement learning. Our model addresses the critical need for accurate, automated traffic forecasting, reducing reliance on manual configuration by selecting the most suitable hyperparameters for the GNN. Through extensive experimentation on real-world traffic datasets, we demonstrate the superiority of our approach in prediction accuracy. Additionally, our work provides valuable insights into optimal hyperparameters for traffic prediction tasks, contributing to the broader understanding of hyperparameter optimization in machine learning models. This paper represents a pivotal step towards more effective and automated traffic prediction, with the potential to revolutionize urban planning and transportation management.

Keywords: Traffic predictionGraph Neural NetworksReinforcement learningHyperparameter optimization, Deep learningIntelligent Transportation