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Discrete Dynamics in Nature and Society
Volume 2015, Article ID 136010, 7 pages
http://dx.doi.org/10.1155/2015/136010
Research Article

Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit

1School of Traffic and Transportation, Beijing Jiaotong University, Haidian District, Beijing 100044, China
2Guangzhou Metro Corporation, Guangzhou 510310, China

Received 9 August 2014; Revised 21 October 2014; Accepted 23 October 2014

Academic Editor: Binggen Zhang

Copyright © 2015 Chang-jun Cai 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

For urban rail transit, the spatial distribution of passenger flow in holiday usually differs from weekdays. Holiday destination choice behavior analysis is the key to analyze passengers’ destination choice preference and then obtain the OD (origin-destination) distribution of passenger flow. This paper aims to propose a holiday destination choice model based on AFC (automatic fare collection) data of urban rail transit system, which is highly expected to provide theoretic support to holiday travel demand analysis for urban rail transit. First, based on Guangzhou Metro AFC data collected on New Year’s day, the characteristics of holiday destination choice behavior for urban rail transit passengers is analyzed. Second, holiday destination choice models based on MNL (Multinomial Logit) structure are established for each New Year’s days respectively, which takes into account some novel explanatory variables (such as attractiveness of destination). Then, the proposed models are calibrated with AFC data from Guangzhou Metro using WESML (weighted exogenous sample maximum likelihood) estimation and compared with the base models in which attractiveness of destination is not considered. The results show that the values are improved by 0.060, 0.045, and 0.040 for January 1, January 2, and January 3, respectively, with the consideration of destination attractiveness.