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 :
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