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Application of BP network for travel behavior analysis: complexity recognition of trip chaining
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NETL
The article develops a BP network for trip chaining pattern recognition based on the data obtained from Beijing Resident Trip Survey。 First a set of socioeconomic and demographic factors related to traveller information which potentially influence trip-chaining patterns are pretreated through principle components analysis， therefore seven variables 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， a logistic regression model is also introduced to make a comparison， and the conclusions indicate BP network performs much better; actually the generalization capability of the former is much better too。
BP neural networkprinciple components analysistravel behavior analysistrip chaininglogistic regression model
Zhao dan、Shao chunfu、Zhu nuo、Liu yinhong
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MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology,Beijing Jiaotong University,Beijing, China
Changsha(CN)
2010 International Conference on Intelligent Computation Technology and Automation
NETL
