2022
Yuan, Yalong; Yang, Min; Feng, Tao; Ma, Yafeng; Ren, Yifeng; Ruan, Xinpei
Heterogeneity in the transfer time of air-rail intermodal passengers based on ticket booking data Journal Article
In: Transportation Research Part A: Policy and Practice, vol. 165, pp. 533-552, 2022, ISSN: 0965-8564.
Abstract | Links | BibTeX | Tags: Air-rail integrated service (ARIS), Air-rail passenger, Passenger heterogeneity, Ticket booking data, Transfer time
@article{YUAN2022533,
title = {Heterogeneity in the transfer time of air-rail intermodal passengers based on ticket booking data},
author = {Yalong Yuan and Min Yang and Tao Feng and Yafeng Ma and Yifeng Ren and Xinpei Ruan},
url = {https://www.sciencedirect.com/science/article/pii/S0965856422002580},
doi = {https://doi.org/10.1016/j.tra.2022.09.022},
issn = {0965-8564},
year = {2022},
date = {2022-01-01},
journal = {Transportation Research Part A: Policy and Practice},
volume = {165},
pages = {533-552},
abstract = {Transfer constitutes the weakest link in air-rail integrated services (ARISs). Air-rail passengers face problems such as long transfer times, complex transfer processes and poor transfer experiences, which seriously reduces ARIS competitiveness. The transfer time, as the most direct and objective measurement of transfer behavior, is important in ARIS planning, construction and operation. This paper aims to analyze the influence of transfer time on the use of ARIS using ticket booking data. The air-rail intermodal passengers are classified into three groups according to the social demographical attributes and trip characteristics using a latent class clustering model, namely passengers departing on weekdays for medium- or long-distance trips, passengers departing on weekdays for medium- or short-distance trips and passengers departing during weekends for medium- or long-distance trips. After that, a generalized ordered logistic regression (GOL) model is employed to identify the key factors influencing the transfer behavior of these three passenger groups. The results indicate that ticket-related attributes have the greatest impact on passenger transfer behavior, followed by transfer-related attributes, and operation-related attributes have the least impact. The effects of operation- and transfer-related attributes on transfer behavior among the different passenger groups are similar, but the ticket price, travel distance and travel time impose distinct effects on transfer behavior in the different passenger groups. Finally, some relevant recommendations to improve the ARIS are discussed.},
keywords = {Air-rail integrated service (ARIS), Air-rail passenger, Passenger heterogeneity, Ticket booking data, Transfer time},
pubstate = {published},
tppubtype = {article}
}
Transfer constitutes the weakest link in air-rail integrated services (ARISs). Air-rail passengers face problems such as long transfer times, complex transfer processes and poor transfer experiences, which seriously reduces ARIS competitiveness. The transfer time, as the most direct and objective measurement of transfer behavior, is important in ARIS planning, construction and operation. This paper aims to analyze the influence of transfer time on the use of ARIS using ticket booking data. The air-rail intermodal passengers are classified into three groups according to the social demographical attributes and trip characteristics using a latent class clustering model, namely passengers departing on weekdays for medium- or long-distance trips, passengers departing on weekdays for medium- or short-distance trips and passengers departing during weekends for medium- or long-distance trips. After that, a generalized ordered logistic regression (GOL) model is employed to identify the key factors influencing the transfer behavior of these three passenger groups. The results indicate that ticket-related attributes have the greatest impact on passenger transfer behavior, followed by transfer-related attributes, and operation-related attributes have the least impact. The effects of operation- and transfer-related attributes on transfer behavior among the different passenger groups are similar, but the ticket price, travel distance and travel time impose distinct effects on transfer behavior in the different passenger groups. Finally, some relevant recommendations to improve the ARIS are discussed.