Bayesian network modeling explorations of strategies on reducing perceived transfer time for urban rail transit service improvement in different seasons

Cities ◽  
2019 ◽  
Vol 95 ◽  
pp. 102474
Author(s):  
Xuesong Feng ◽  
Kemeng Li ◽  
Chuanchen Ding ◽  
Weixin Hua
Keyword(s):  
Bayesian Network ◽  
Network Modeling ◽  
Urban Rail Transit ◽  
Service Improvement ◽  
Rail Transit ◽  
Transfer Time ◽  
Transit Service ◽  
Urban Rail ◽  
Bayesian Network Modeling ◽  
Different Seasons

Analysis of Urban Rail Transit Transfer Time during the Point Transfer and Channel Transfer

CICTP 2016 ◽  
2016 ◽  
Author(s):  
Gang Ren ◽  
Jia Lu ◽  
Hang Jiang ◽  
Lin-li Xing ◽  
Li-li Lu
Keyword(s):  
Urban Rail Transit ◽  
Rail Transit ◽  
Transfer Time ◽  
Urban Rail

scholarly journals Reducing Perceived Urban Rail Transfer Time with Ordinal Logistic Regressions

2019 ◽  
Vol 31 (5) ◽  
pp. 593-602
Author(s):  
Xuesong Feng ◽  
Weixin Hua ◽  
Xuepeng Qian
Keyword(s):  
Logistic Regression ◽  
Waiting Time ◽  
Ordinal Logistic Regression ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transfer Time ◽  
Urban Rail ◽  
Logistic Regressions

In order to improve the transfers inside an Urban Rail Transit (URT) station between different rail transit lines, this research newly develops two Ordinal Logistic Regression (OLR) models to explore effective ways for saving the Perceived Transfer Time (PTT) of URT passengers, taking into account the difficulty of improving the transfer infrastructure. It is validated that the new OLR models are able to rationally explain probabilistically the correlations between PTT and its determinants. Moreover, the modelling analyses in this work have found that PTT will be effectively decreased if the severe transfer walking congestion is released to be acceptable. Furthermore, the congestion on the platform should be completely eliminated for the evident reduction of PTT. In addition, decreasing the actual transfer waiting time of the URT passengers to less than 5 minutes will obviously decrease PTT.

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