The research on traffic accident prediction based on simulated annealing optimized BP neural network algorithm

The number of traffic accidents is an important indicator for assessing road traffic safety. Accurate prediction of the number of accidents is essential for developing effective safety strategies that help reduce the accident rate and its associated consequences. In this paper, we propose a prediction model that combines backpropagation neural network (BP) and simulated annealing algorithm (SA) to predict the number of traffic accidents in China. BP neural network can capture complex relationships in traffic accident data more accurately by virtue of its excellent nonlinear modeling ability. However, traditional BP algorithms are often prone to fall into local optimal solutions. To solve this problem, this paper introduces a simulated annealing algorithm to optimize the BP neural network, which utilizes the global search capability of SA to improve the prediction accuracy of the model. The experimental results show that the BP neural network optimized by SA exhibits a significant accuracy improvement in the prediction of traffic accident casualties, which provides a powerful decision support for the traffic management department in China.