A Comparison of Cross-nested Logit Model and BP Neural Network to Estimate Residential Location and Commute Mode Choice in Beijing
The objective of this paper is to compare the merits of back propagation neural network(BPNN) with those of cross-nested iogit(CNL) model to estimate the simultaneously joint choice of residential location and commute mode choice during the process of employment suburbanization. Back propagation neural network and discrete choice model specified as cross-nested Iogit have been respectively employed to investigate the joint choice for different types of employment destination scenarios, that is, under center(CBD),urban and suburban workplace patterns in Beijing. The predictive capability of these two models has been compared in terms of models accuracy. Results demonstrate that on the whole the BPNN have a higher accuracy for this joint choice model and is more suitable for prediction.
Cross-nested Logit BPNN Residential Location Choice Commute Mode Choice Spatial Correlation
Li Xia Shao Chunfu Yang Liya Ma Zhuanglin
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology Beijing Jiaotong U School of Public Administration, Renmin University of China, Beijing 100872,China
国际会议
长沙
英文
36-39
2010-03-13（万方平台首次上网日期，不代表论文的发表时间）