DocumentCode
3204687
Title
Traffic Incident Duration Prediction Based on Artificial Neural Network
Author
Guan, Liping ; Liu, Weiming ; Yin, Xiangyuan ; Zhang, Luping
Volume
3
fYear
2010
fDate
11-12 May 2010
Firstpage
1076
Lastpage
1079
Abstract
The prediction of traffic incident duration is an important foundation of advanced incident management system and driver information system. In this paper, actual traffic incident data was used to study the prediction problem of traffic incident duration by the method of neural network. 660 sets of actual traffic incident data from a freeway management center were used to train a neural network model, and 170 sets of incident data in the same data collection, which are different from training data, were used to test the prediction effect of the model. The test result shows that the correlation of the prediction values and the actual values is 0.8535, which indicates that the prediction result of the neural network model can basically represent actual incident duration.
Keywords
correlation methods; neural nets; transportation; artificial neural network; data collection; driver information system; freeway management center; traffic incident duration prediction; Artificial neural networks; Decision trees; Disaster management; Intelligent networks; Intelligent transportation systems; Neural networks; Predictive models; Telecommunication traffic; Testing; Traffic control; incident duration; incident management; intelligent transportation system; neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
Conference_Location
Changsha
Print_ISBN
978-1-4244-7279-6
Electronic_ISBN
978-1-4244-7280-2
Type
conf
DOI
10.1109/ICICTA.2010.418
Filename
5523317
Link To Document