基于灰色神经网络的短期交通预测模型

Qiongqin Jiang, Zhigang Liu, Youfu Du
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引用次数: 2

摘要

阐述了三种灰色神经网络组合模型在城市交通速度短期预测中的应用,并通过荆州北京道路的实验验证了其有效性和可行性。并行灰色神经网络、串联灰色神经网络和嵌套灰色神经网络。与单模型GM(1,1)和神经网络相比,实验证明了三种模型的可行性和有效性。实际交通速度变化平稳或不会显著影响预测的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Short-term Traffic Prediction Model Based on Grey Neural Network
This paper expounds three kinds of grey neural network combined model for short-term prediction of urban traffic speed, and confirms their validity and feasibility by conducting experiment in Beijing road of Jingzhou. Three kinds of networks are parallel grey neural network, series grey neural network, and inlaid grey neural network. The experiment proves that the three kinds of modes are feasible and effective in comparison with single model GM(1,1) and neural network. And actual traffic speed varies smoothly or will not influence significantly the accuracy for prediction.
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