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Application of wavelet neural networks for trip chaining recognition
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NETL
The article develops a wavelet neural network for trip chaining pattern recognition。 Based on the data obtained from Beijing Resident Trip Survey， a set of socioeconomic and demographic factors related to the of traveller situation which potentially influence trip-chaining patterns are selected as input variables of neural network， and a categorical trip chaining pattern (simple and complex trip chaining) are used as output variables。 In order to quantify prediction accuracy， two performance measures are applied to evaluate it。 Besides， BP neural network and a logistic regression model are also introduced to make a comparison， and the conclusions indicate wavelet neural network performs much better in convergence rate and prediction accuracy; actually its generalization capability is much better too。
BP neural networklogistic regression modeltravel behavior analysistrip chainingwavelet neural network
Dan Zhao、Chunfu Shao
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MOE Key Lab. for Urban Transp. Complex Syst. Theor. & Technol., Beijing Jiaotong Univ., Beijing, China
Yantai(CN)
2010 Sixth International Conference on Natural Computation
NETL
