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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 292608, 11 pages
http://dx.doi.org/10.1155/2014/292608
Research Article

An Empirical Study of Parameter Estimation for Stated Preference Experimental Design

1School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
2Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou No. 2, Nanjing 210096, China
3Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
4School of Transportation, Southeast University, No. 2 Si Pai Lou, Nanjing, Jiangsu 210096, China

Received 12 July 2014; Accepted 15 August 2014; Published 31 August 2014

Academic Editor: Huimin Niu

Copyright © 2014 Fei Yang 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.

Abstract

The stated preference experimental design can affect the reliability of the parameters estimation in discrete choice model. Some scholars have proposed some new experimental designs, such as D-efficient, Bayesian D-efficient. But insufficient empirical research has been conducted on the effectiveness of these new designs and there has been little comparative analysis of the new designs against the traditional designs. In this paper, a new metro connecting Chengdu and its satellite cities is taken as the research subject to demonstrate the validity of the D-efficient and Bayesian D-efficient design. Comparisons between these new designs and orthogonal design were made by the fit of model and standard deviation of parameters estimation; then the best model result is obtained to analyze the travel choice behavior. The results indicate that Bayesian D-efficient design works better than D-efficient design. Some of the variables can affect significantly the choice behavior of people, including the waiting time and arrival time. The D-efficient and Bayesian D-efficient design for MNL can acquire reliability result in ML model, but the ML model cannot develop the theory advantages of these two designs. Finally, the metro can handle over 40% passengers flow if the metro will be operated in the future.