关键词: 交通流量预测, 神经网络, 变异尺度, 改进布谷鸟搜索算法

Abstract: Traffic flow prediction is a very important research area of intelligent transportation systems. Traditional prediction methods does not work very well because of the complexity of the influencing factors. So an ACS algorithm based on [t] distribution adaptive mutation is proposed. By setting dynamic variation control scales and multiple degrees of freedom to construct adaptive mutation algorithm. Overall optimization results can be obtained, which is better than Gaussian mutation and Cauchy mutation. And then short-time traffic flow forecasting method is proposed which based on BP neural network optimized by ACS. By optimizing the initial weights and threshold parameters, it can improve the short-term traffic flow prediction accuracy. The simulation results show that it can get better prediction results.

Key words: short-term traffic flow prediction, neural network, variability factor, improved cuckoo search algorithm