Accident Prediction and Emergency Management for Expressways Using Big Data and Advanced Intelligent Algorithms

Accurate accident risk assessment and timely emergency response are essential for enhancing expressway safety and reducing traffic congestion. This study develops a framework that integrates Deep Neural Networks (DNN), Graph Neural Networks (GNN) and Bayesian Optimization (BO) for accident risk assessment with a Reinforcement Learning (RL)-based strategy for optimizing emergency operations. Based on multi-source traffic data from Putuo District, Shanghai, the model improves accident forecasting by incorporating spatial and temporal traffic characteristics, while reinforcement learning enhances response decision-making under varying traffic conditions. The results show that adaptive optimization improves both prediction accuracy and emergency handling efficiency, underscoring the importance of early intervention and data-informed strategies in mitigating traffic disruptions. The findings contribute to the advancement of intelligent traffic management and offer a practical approach to integrating predictive modeling into real-world expressway operations. Future work should explore multi-agent coordination, real-time traffic behavior analysis and refined traffic flow modeling to further enhance system performance in complex urban road networks.