An Improved Discrete Grey Model Based on BP Neural Network for Traffic Flow Forecasting

被引：0

作者：

Wu, Ziheng ^{[1
]}

Wu, Zhongcheng ^{[1
]}

Zhang, Jun ^{[1
]}

机构：

[1] Chinese Acad Sci, High Magnet Field Lab, Hefei, Anhui, Peoples R China

来源：

ADVANCES IN COMPUTER COMMUNICATION AND COMPUTATIONAL SCIENCES, VOL 1 | 2019年 / 759卷

关键词：

Intelligent transportation; Traffic flow forecasting; Discrete grey model; Time coefficient; Backpropagation neural network;

D O I：

10.1007/978-981-13-0341-8_17

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The forecasting of traffic flow is an important part of intelligent transportation system; actual and accurate forecasting of traffic flow can give scientific support for urban traffic guidance and control. As there is big forecast error when modeling toward traffic flow data with discrete grey model DGM (1, 1), this paper amends the equal interval time sequence. According to the characteristic of time coefficient and backpropagation (BP) neural network, we propose an improved grey model by combining DGM (1, 1) model with BP neural network model. The experimental result indicates that the improved grey model is scientific and effective for the forecasting of traffic flow.

引用

页码：189 / 197

页数：9

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