Open AccessDevelopment of a transfer‐cost‐based logit assignment model for the Beijing rail transit network using automated fare collection data
Author(s) -
Si Bingfeng,
Zhong Ming,
Liu Jianfeng,
Gao Ziyou,
Wu Jianjun
Publication year - 2013
Publication title -
journal of advanced transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 46
eISSN - 2042-3195
pISSN - 0197-6729
DOI - 10.1002/atr.1203
Subject(s) - beijing , transfer (computing) , transfer station , computer science , revenue , flow network , mixed logit , logit , public transport , travel behavior , operations research , transit (satellite) , transport engineering , function (biology) , logistic regression , mathematical optimization , engineering , mathematics , economics , machine learning , parallel computing , china , accounting , evolutionary biology , political science , law , biology
SUMMARY Literature review indicates that little is known about traveler behavior, such as transfer and route choices, in large transit systems because of the number of alternative routes involved and lack of empirical data. Even though many transit route assignment models have been developed and ample automated fare collection data have been collected, nearly no study has quantified how accurate resulting flow assignments are, especially for transfer flows. However, as a multi‐stakeholder system, it is essential to estimate passenger flows over the Beijing rail transit network for revenue sharing and daily management/operation purpose. In this paper, major factors (including total travel time and transfer cost) that influence passenger flow pattern in the Beijing rail transit network are considered in a logit‐based network flow assignment model. Specifically, a full transfer cost function, including transfer walking time, vehicle waiting time, and a penalty to additional transfers, is proposed to better simulate passengers' transfer behaviors. A generalized cost function for urban rail transit network is presented, and the corresponding route choice behavior of travelers is analyzed. An improved logit‐based model is then presented for solving this network flow assignment problem. The depth‐first method is used to search for “effective paths” among all O–D pairs. The average errors of estimated transfer flows from the proposed assignment model, which is proven to be more realistic in searching a set of effective paths, are below 20%. The results indicate that the models being developed in this study are capable of reasonably reproducing passengers' transfer and route choices and thus helpful for understanding the transfer behaviors of passengers of large rail transit networks. Copyright © 2012 John Wiley & Sons, Ltd.