DocumentCode
3026047
Title
Expressway Emergency Resources Demand Forecasting Based on Neural Network
Author
Liu Jin
Author_Institution
Dept. of Traffic Eng., Hong Kong-Zhuhai-Macao Bridge Authority, Zhuhai, China
fYear
2013
fDate
29-30 June 2013
Firstpage
595
Lastpage
598
Abstract
Expressway traffic accidents seriously threaten the personal property security. And emergency resources are the basis and premise of accident rescue. Thus the emergency resource demand prediction of expressway is of great significance. The influence factors of emergency resource demand is analyzed in this paper, and neural network programming is carried out on the highway emergency resource demand. Finally, combining trained of neural network and case analysis, it achieves emergency resource demand projections for the new case of the emergency center. The results show that the BP neural network can form the inherent law of highway emergency resource demand after training, self-learning and self-adaptation, and the results can meet the prediction error precision. So the results of neural network prediction can provide scientific allocation of expressway emergency resource with reasonable reference.
Keywords
backpropagation; emergency management; neural nets; road accidents; road safety; road traffic; traffic engineering computing; BP neural network; accident rescue; case analysis; emergency center; emergency resource demand prediction; expressway emergency resource demand forecasting; expressway traffic accident; highway emergency resource demand; neural network prediction; neural network programming; personal property security; self-adaptation; self-learning; Accidents; Biological neural networks; Hazards; Resource management; Roads; demand forecasting; emergency resources; expressway; neural network; traffic engineering;
fLanguage
English
Publisher
ieee
Conference_Titel
Digital Manufacturing and Automation (ICDMA), 2013 Fourth International Conference on
Conference_Location
Qingdao
Type
conf
DOI
10.1109/ICDMA.2013.140
Filename
6598061
Link To Document