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《Journal of Traffic and Transportation Engineering》 2014-05
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Fusion method of road section average speed based on BP neural network and D-S evidence theory

LI Rui-min;MA Wei;Institute of Transportation Engineering,Tsinghua University;Department of Civil Engineering,Tsinghua University;  
In order to estimate road section average speed accurately,a fusion method of road section average speed based on BP neural network and D-S evidence theory was proposed.The values of probability density function were estimated by trained BP neural network,and the data were fused by D-S evidence theory.The self-learning ability of BP neural network and the reasoning ability of D-S evidence theory were combined in the fusion method.The framework and model of the fusion method were presented,and each process of the method was analyzed.The fusion method was verified by using floating car data(FCD),microwave detector data,and license plate recognition data from Beijing-Xizang Expressway.The robustness of the fusion method was verified in the case that microwave detector failed to work.Analysis result indicates that the mean absolute percentage errors of fusion data are 7.90%,20.72% better than that of FCD and microwave detector data respectively.When microwave detector fail to work,the fusion accuracy reduces,but the errors of fusion data is still smaller than that of FCD,and the fusion method is proved to be robustness.2tabs,6figs,26 refs.
【Fund】: 国家自然科学基金项目(71361130015)
【CateGory Index】: U491.1
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