Real-Time Short-Term Traffic Flow Forecasting Based on Process Neural Network

被引：0
|
作者：
He, Shan [1 ]
Hu, Cheng [1 ]
Song, Guo-jie [1 ]
Xie, Kun-qing [1 ]
Sun, Yi-Zhou [2 ]
机构：
[1] Peking Univ, Minist Educ, Key Lab Machine Percept, Beijing 100871, Peoples R China
[2] Univ Illinois, Dept Comp Sci, Urbana, IL USA
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2008, PT 2, PROCEEDINGS | 2008年 / 5264卷
基金：
中国国家自然科学基金;
关键词：
Process neural network; Short-time traffic flow forecasting; Traffic engineering;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Existing short-term Traffic flow forecasting models have not frilly considered the characteristics of spatio-temporal process and online analysis, we imported the process neural network which can model spatio-temporal process well into short-term traffic forecasting. The model rise wavelet radix as weighted function expanding radix of process neurons to deal with the inputs on multi-scale. By using principal component, analysis to consider the space affect of traffic flow; the model was optimized. In addition, online learning algorithm of the model was proposed. The experimental result's show that the forecasting accuracy of the model is better than ordinary neural networks, and the model can meet the demand of real-time forecasting of short-term traffic flow.
引用
收藏
页码：560 / +
页数：3
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