Assessing Dynamic Neural Networks for Travel Time Prediction

被引：0
|
作者：
Shen, Luou [1 ]
Huang, Min [2 ]
机构：
[1] South China Univ Technol, Sch Civil & Transportat Engn, Guangzhou 510641, Guangdong, Peoples R China
[2] Wuhan Inst Technol, Sch Environm & Civil Engn, Wuhan 430073, Hubei, Peoples R China
来源：
2010 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION (PACIIA2010), VOL I | 2010年
关键词：
Advanced Traffic Information System; Travel Time Prediction; Detector Data; Dynamic Neural Networks;
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：
Dynamic neural networks comprise a class of neural networks that is particularly suitable for predicting variables like travel time, but has not been adequately investigated. This study compares the travel time prediction performance of three dynamic neural network topologies with different memory settings. The results show that the time-delay neural networks out-performed the other two topologies. This topology also performed slightly better than the multilayer perceptron neural networks.
引用
收藏
页码：482 / 485
页数：4
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