Prethodno priopćenje
https://doi.org/10.7307/ptt.v31i5.3161
Reducing Perceived Urban Rail Transfer Time with Ordinal Logistic Regressions
Xuesong Feng
; Beijing Jiaotong University, School of Traffic and Transportation, Beijing, P.R. China
Weixin Hua
; Beijing Jiaotong University, School of Traffic and Transportation, Beijing, P.R. China
Xuepeng Qian
; Ritsumeikan Asia Pacific University, College of Asia Pacific Studies, Beppu, Japan
Sažetak
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.
Ključne riječi
Hrčak ID:
227931
URI
Datum izdavanja:
18.10.2019.
Posjeta: 747 *