Adaptive traffic prediction model using Graph Neural Networks optimized by reinforcement learning

Traffic prediction is critical for urban planning and transportation management, with significant implications for congestion reduction, resource allocation, and sustainability. Traditional statistical models struggle to capture the complex spatiotemporal dependencies in traffic data, leading to reduced accuracy. Graph Neural Networks (GNNs) have emerged as a more practical approach due to their ability to model these intricate relationships. However, GNN-based methods face challenges in hyperparameter selection, which impacts their performance across diverse traffic scenarios. To address this, this paper proposes an adaptive traffic prediction model that uses reinforcement learning to optimize GNN hyperparameters. Our model significantly reduces the need for manual tuning and improves prediction accuracy across real-world traffic datasets, achieving a 9.8% reduction in Mean Absolute Error (MAE) and 3.6% improvement in Root Mean Squared Error (RMSE) compared to state-of-the-art baselines. In conclusion, dynamic hyperparameter adaptation boosts robustness and efficiency in traffic forecasting. This approach also helps us better understand how to fine-tune GNNs, contributing to the broader knowledge of optimizing machine learning models. Our work helps make traffic prediction more automated and improves how to manage urban transportation. • Novel Traffic Prediction Framework: Introduces a reinforcement learning-based framework for optimizing Graph Neural Networks (GNNs) to enhance traffic prediction accuracy. • Advanced Hyperparameter Optimization: Dynamically adjusts GNN hyperparameters to achieve optimal predictive performance without manual intervention. • Real-World Data Validation: Validated using benchmark traffic datasets, including METR-LA and PeMS07, covering diverse urban traffic scenarios. • Superior Prediction Accuracy: Achieves a 9.8% reduction in Mean Absolute Error (MAE) and a 3.6% improvement in Root Mean Squared Error (RMSE) compared to baseline models. • Practical Applicability: Demonstrates robustness in predicting complex traffic patterns across varying conditions, enhancing intelligent transportation systems. • Scalability and Performance: Maintains computational efficiency while providing accurate predictions for large-scale, real-world traffic data. • Potential for Future Enhancements: Proposes integration of anomaly detection mechanisms and multi-agent optimization techniques for further improvements.