关键词: 短时交通流预测, 预测模型, 反向传播（BP）神经网络, 粒子群优化算法（PSO）, 双重变异

Abstract: In short-term traffic flow prediction, the traditional PSO optimizes the neural network model for prediction sets escape particle on the boundary directly and has no corresponding variation mechanism by itself, which is bad for maintaining the diversity of particle swarm and finding the optimal solution. To further improve the accuracy of short-term traffic flow prediction, boundary mutation operator and self-adaptive mutation operator called double mutation are proposed in PSO to optimize the network configuration parameters based on the traditional PSO to optimize the BP neural network. The proposed prediction model is tested by measured Beijing 2nd ring road’s traffic flow data and the computational results show that this modified prediction method is more beneficial to search for the global optimal solution and save optimization time, and can improve the performance of short-term traffic flow prediction effectively.

Key words: short-term traffic flow prediction, prediction model, Back Propagation（BP） neural network, Particle Swarm Optimization（PSO）, double mutation