基于小波神经网络的道路交通流量实时预测模型研究

实时交通流量预测是智能交通系统的核心内容，智能交通系统中多个子系统的功能实现都以其为基础。交通流具有高度非线性和不确定等特征，且与时间高度相关，可以看成是时间序列预测问题。根据交通流的这些特点，提出基于小波神经网络的道路交通流量实时预测模型，并以某条道路为例，通过Matlab编程实现模拟仿真。仿真结果表明，小波神经网络能够比较精确、快速地对实时交通流量进行预测，网络预测值接近期望值。

Research on, Real-time Prediction Model of Road Traffic Flow based on Wavelet Neural Network

Real-time traffic flow prediction is the core content of intelligent traffic system and the basis for realization of functions of multiple subsystems in intelligent traffic system. The traffic flow is characterized by high nonlinearity and uncertainty, etc. , and is highly related to time and can be regarded as a time series prediction problem. In accordance with these features of traffic flow, this paper proposes a real-time prediction model of road traffic flow based on wavelet neural network and realizes simulation via Matlab programming with some road as an example. The results of simulation indicate that the wavelet neural network can predict real-time traffic flow precisely and quickly, and the values predicted by the network are close to the expected values.