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Mathematical Problems in EngineeringVolume 2013 2013, Article ID 452735, 8 pages

Research Article

School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA

Received 18 January 2013; Accepted 11 March 2013

Academic Editor: Reza Jazar

Copyright © 2013 Jian Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study reviews the impact of public transit network layout TNL on resident mode choice. The review of TNL as a factor uses variables divided into three groups: a variable set without considering the TNL, one considering TNL from the zone level, and one considering TNL from the individual level. Using Baoding’s travel survey data, a Multinomial Logit MNL model is used, and the parameter estimation result shows that TNL has significant effect on resident mode choice. Based on parameter estimation, the factors affecting mode choice are further screened. The screened variable set is regarded as the input data to the BP neural network’s training and forecasting. Both forecasting results indicate that introducing TNL can improve the performance of mode choice forecasting.

Autor: Jian Gao, Peng Zhao, Chengxiang Zhuge, Hui Zhang, and Edward D. McCormack



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