基于神经网络的道路通行能力与交通需求匹配度判别研究

交通的供需是否匹配关系到城市和枢纽的发展前景。文中在探讨道路通行能力与需求匹配特性的基础上,用BP神经网络理论建立一种交通匹配预测模型。该模型发挥神经网络的优势,对数据并行处理和分布存储,通过训练、学习产生一个非线性映射,自适应地对数据进行预测。通过相关数据实验证明,该神经网络模型有较高的精度,并有较好的适用性。

Discrimination on Matching Degree of Road Capacity and Traffic Demand Based on Neural Network Approach

The matching capability degree of supply and demand of traffic facilities is important to the prospect of the city and the traffic terminal. In the present paper, characteristics of road capacity and traffic demand were firstly discussed. Subsequently, based on neural network approach, a model was built up to predict traffic matching degree. With the model, endowed with the advantage of neural network approach, the data was parallel processed and distributed stored. A nonlinear map was created by training and studying, and finally, prediction was obtained adaptively. The predicted matching capability degree was compared to the experimental data, the result shows that the model possesses the characteristics of high accuracy and good applicability.