BP神经网络在通道交通流预测中的应用探索

利用通道交通流参数在通道相邻路段的传动机理，分析上下游交通流特征参数的耦合关系，提出通道交通流预测技术，并基于传统数理方法建立交通流预测模型的局限性，提出神经网络模型。利用遗传算法不断优化网络权值，并通过改变网络隐含层节点数以及网络各层之间的转移函数提高网络模型的预测精度，实现通道下游交通流的实时预测。

Probe into Application of BP Neural Network in Prediction of Traffic Flow of Passageways

By means of transmission mechanism of traffic flow parameters of passageways in adjacent sections of passageways, this paper analyzes the coupling relationship between characteristic parameters of upstream and downstream traffic flows, proposes prediction technique for traffic flow of passageways, and puts forward neural network model based on limitation of prediction model of traffic flow established based on traditional mathematical method. The paper continuously optimizes network weights by means of genetic algorithm, and improves the prediction precision of network model by changing the number of nodes in hidden layers of network and transfer functions between all layers of networks to realize real-time prediction of traffic flow in downstream of passageways.