Estimate traffic control patterns using a hybrid neural network

Author

Chang, Edmond Chin-Ping

Author_Institution

Texas Transp. Inst., Texas A&M Univ., College Station, TX, USA

Volume

4

fYear

1999

fDate

1999

Firstpage

2763

Abstract

Many operating agencies are currently developing computerized freeway traffic management systems to support traffic operations as part of the intelligent transportation system (ITS) user service improvements. This study illustrates the importance of using simplified data analysis and presents a promising approach for improving demand prediction and traffic data modeling to support pro-active control. This study found that the approach of combining advanced neural networks and conventional error correction is promising for improved ITS applications

Keywords

data analysis; error correction; forecasting theory; neural nets; road traffic; traffic control; traffic engineering computing; data analysis; demand prediction; error correction; freeway traffic; hybrid neural network; intelligent transportation system; road traffic control; traffic management systems; Biological neural networks; Communication system traffic control; Control systems; Data analysis; Error correction; Intelligent transportation systems; Neural networks; Numerical analysis; Real time systems; Traffic control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.833517

Filename

833517