Real-time Traffic Flow Forecasting based on Wavelet Neural Network

被引：1

作者：

Li, Rihan ^{[1
]}

Xu, Jianmin ^{[1
]}

Luo, Qiang ^{[1
]}

Hu, Sangen ^{[1
]}

机构：

[1] South China Univ Technol, Sch Civil Engn & Transportat, Guangzhou 510641, Guangdong, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ONLINE ENGINEERING | 2013年 / 9卷 / 03期

基金：

中国国家自然科学基金;

关键词：

ITS; Real-time Forecasting; Traffic Flow; WNN;

D O I：

10.3991/ijoe.v9i3.2710

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Real-time traffic flow forecasting is the core of Intelligent Transportation System (ITS), and the foundation of multi-subsystem's implementation in ITS. Traffic flow, which is highly time-relevant, with the features of high nonlinear and non-determinism, can be treated as the time sequence forecast. On the basis of these features of traffic flow, this paper tries to deal with this issue based on Wavelet Neural Network (WNN) specially. At the same time, the paper realizes the analogue simulation through the Matlab software programming, by taking a road for example. And the simulation results show that the traffic flow can be precisely forecasted using Wavelet Neural Network, and its value is close to the expectations.

引用

页码：72 / 76

页数：5

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