摘要
针对城市道路交通系统的复杂性和随机性,应用灰色理论和神经网络知识,建立了基于灰色理论和BP神经网络的城市道路交通量GM-BP神经网络预测模型.随后运用该预测模型对城市道路的交通量进行预测,预测结果表明:GM-BP神经网络预测模型所得预测结果平均相对误差为1.17%,与单一的灰色新陈代谢预测模型相比具有预测精度高的优点.
Aiming at the randomness and complexity of urban traffic system,the GM-BP neural network forecasting model of the traffic volume was built based on the grey theory and BP neural network.Then,the combination forecasting model was used to forecast the traffic volume of urban road.The results showed that the average relative error of the forecasting results in GM-BP neural network model was 1.17%.Compared with the grey model,the GM-BP neural network forecast model has better prediction precision.
出处
《武汉理工大学学报（交通科学与工程版）》
2014年第3期615-618,共4页
Journal of Wuhan University of Technology（Transportation Science & Engineering）
基金
福建省教育厅资助科技项目(JA11200)
