基于混沌小波网络的交通流预测算法研究

实时准确的交通流量预测是实现智能交通诱导及控制的前提与关键,也是智能化交通管理的客观需要.结合交通流预测的特点,提出了一种基于小波网络的路段交通流预测方法,把混沌优化算法引入小波网络的拓扑构造,结合提出的相似时段的预测思想,给出了一种基于混沌优化算法的小波网络交通流量预测模型.实验结果表明,引入相似时段的预测思想可以有效提高交通流的预测精度,基于混沌优化算法的小波网络在交通流预测的精度和收敛速度方面明显优于常规BP网络

Study of traffic flow forecast algorithms based on chaotic wavelet networks

Real-time and accurate traffic flow forecast is very important to the intelligent traffic guidance, control and management. According to the characteristics of traffic flow, this paper proposes a new model of traffic flow forecast based on wavelet networks and the forecast algorithm of comparable time intervals. The structures of wavelet networks are optimized by a modified chaos optimization algorithm. The experiment results show that this model is superior to the common BP neural networks in the aspects of flow forecasting precision and network convergence.