A Short-Term Traffic Flow Reliability Prediction Method considering Traffic Safety

With the rapid development and application of intelligent traffic systems, traffic flow prediction has attracted an increasing amount of attention. Accurate and timely traffic flow information is of great significance to improve the safety of transportation. To improve the prediction accuracy of the backward-propagation neural network (BPNN) prediction model, which easily falls into local optimal solutions, this paper proposes an adaptive differential evolution (DE) algorithm-optimized BPNN (DE-BPNN) model for a short-term traffic flow prediction. First, by the mutation, crossover, and selection operations of the DE algorithm, the initial weights and biases of the BPNN are optimized. Then, the initial weights and biases obtained by the aforementioned preoptimization are used to train the BPNN, thereby obtaining the optimal weights and biases. Finally, the trained BPNN is utilized to predict the real-time traffic flow. The experimental results show that the accuracy of the DE-BPNN model is improved about 7.36% as compared with that of the BPNN model. The DE-BPNN is superior to the performance of three classical models for short-term traffic flow prediction.