Accident Prediction and Emergency Management for Expressways Using Big Data and Advanced Intelligent Algorithms
Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Xiting Sun, Dongyong Wei, Chaopeng Liu, Ting Wang
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414009039