Application of BP network for travel behavior analysis: complexity recognition of trip chaining
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 network principle components analysis travel behavior analysis trip chaining logistic regression model
Zhao dan Shao chunfu Zhu nuo Liu yinhong
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology,Beijing Jiaotong University,Beijing, China
国际会议
长沙
英文
738-741
2010-05-11（万方平台首次上网日期，不代表论文的发表时间）