Title :
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
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;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.833517
