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Reducing Perceived Urban Rail Transfer Time with Ordinal Logistic Regressions
Xuesong Feng, Feng, Xuesong , , Weixin Hua, Hua, Weixin , , Xuepeng Qian, Qian, Xuepeng ,
Keywords:perceived transfer time, perceived transfer waiting time, ordinal logistic regression model, urban rail transit service improvement


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.


Sun Y, Yu X, Bie R, Song H. Discovering time-dependent shortest path on traffic graph for drivers towards green driving. Journal of Network and Computer Applications. 2017;83: 204-212. Available from: doi:10.1016/j.jnca.2015.10.018

Şimşekoğlua Ö, Nordfjærnb T, Rundmobc T. The role of attitudes, transport priorities, and car use habit for travel mode use and intentions to use public transportation in an urban Norwegian public. Transport Policy. 2015;42: 113-120. Available from: doi:10.1016/j.tranpol.2015.05.019

Wu W, Li PK, Zhang Y. Modelling and simulation of vehicle speed guidance in connected vehicle environment. International Journal of Simulation Modelling. 2015;14(1): 145-157. Available from: doi:10.2507/IJSIMM14(1)CO3

Ernstberger A, Joeris A, Daigl M, Kiss M, Angerpointnera K, Nerlich M, Schmucker U. Decrease of morbidity in road traffic accidents in a high income country - An analysis of 24,405 accidents in a 21 year period. Injury. 2015;46(Supplement 4): S135-S143. Available from: doi:10.1016/S0020-1383(15)30033-4

World Health Organisation. Global Status Report on Road Safety: Supporting Decade of Action. Geneva: WHO Press; 2013. Available from:

Birago D, Mensah SO, Sharma S. Level of service delivery of public transport and mode choice in Accra, Ghana. Transportation Research Part F: Traffic Psychology and Behaviour. 2017;46(Part B): 284-300. Available from: doi:10.1016/j.trf.2016.09.033

Liu S, Gong D. Modelling and simulation on recycling of electric vehicle batteries - u sing agent approach. International Journal of Simulation Modelling. 2014;13(1): 79-92. Available from: doi:10.2507/IJSIMM13(1)CO1

Chowdhury S, Hadas Y, Gonzalez VA, Schot B. Public transport users’ and policy makers’ perceptions of integrated public transport systems. Transport Policy. 2018;61: 75-83. Available from: doi:10.1016/j.tranpol.2017.10.001

Cools M, Fabbro Y, Bellemans T. Free public transport: A socio-cognitive analysis. Transportation Research Part A: Policy and Practice. 2016;86: 96-107. Available from: doi:10.1016/j.tra.2016.02.010

Bryniarska Z, Zakowska L. Multi-criteria evaluation of public transport interchanges. Transportation Research Procedia. 2017;24: 25-32. Available from: doi:10.1016/j.trpro.2017.05.063

Hernandez S, Monzon A, de Oña R. Urban transport interchanges: A methodology for evaluating perceived quality. Transportation Research Part A: Policy and Practice. 2016;84: 31-43. Available from: doi:10.1016/j.tra.2015.08.008

Hernandez S, Monzon A. Key factors for defining an efficient urban transport interchange: Users’ perceptions. Cities. 2016;50: 158-167. Available from: doi:0.1016/j.cities.2015.09.009

Chowdhury S, Ceder A, Schwalger B. The effects of travel time and cost savings on commuters’ decision to travel on public transport routes involving transfers. Journal of Transport Geography. 2015;43: 151-159. Available from: doi:10.1016/j.jtrangeo.2015.01.009

Silva JA, Bazrafshan H. User satisfaction of intermodal transfer facilities in Lisbon, Portugal: Analysis with structural equations modelling. Transportation Research Record: Journal of the Transportation Research Board. 2013;2350: 102-110. Available from: doi:10.3141/2350-12

Iseki H, Taylor BD. Style versus service? An analysis of user perceptions of transit stops and stations. Journal of Public Transportation. 2010;13: 23-48. Available from: doi:10.5038/2375-0901.13.3.2

Bak M, Borkowski P, Pawlowska B. Types of solutions improving passenger transport interconnectivity. Transport Problems. 2012;7(1): 27-36. Available from:

Guo Z, Wilson NHM. Assessment of the transfer penalty for transit trips: Geographic information system-

based disaggregate modeling approach. Transportation Research Record: Journal of the Transportation Research Board. 2004;1872: 10-18. Available from: doi:10.3141/1872-02

Aguiléra V, Allio S, Benezech V, Combes F, Milion C. Using cell phone data to measure quality of service and passenger flows of Paris transit system. Transportation Research Part C: Emerging Technologies. 2014;43(Part 2): 198-211. Available from: doi:10.1016/j.trc.2013.11.007

Feng X, Wang X, Zhang H. Passenger transfer efficiency optimization modelling research with simulations. International Journal of Simulation Modelling. 2014;13(2): 210-218. Available from: doi:10.2507/IJSIMM13(2)CO7

Varotto SF, Glerum A, Stathopoulos A, Bierlaire M, Longo G. Mitigating the impact of errors in travel time reporting on mode choice modelling. Journal of Transport Geography. 2017;62: 236-246. Available from: doi:10.1016/j.jtrangeo.2017.05.016

Kelly P, Krenn P, Titze S, Stopher P, Foster C. Quantifying the difference between self-reported and global positioning systems-measured journey durations: A systematic review. Transport Reviews. 2013;33(4): 443-459. Available from: doi:10.1080/01441647.2013.815288

Delclòs-Alió X, Marquet O, Miralles-Guasch C. Keeping track of time: A Smartphone-based analysis of travel time perception in a suburban environment. Travel Behaviour and Society. 2017;9: 1-9. Available from: doi:10.1016/j.tbs.2017.07.001

González RM, Martínez-Budría E, Díaz-Hernández JJ, Esquivel A. Explanatory factors of distorted perceptions of travel time in tram. Transportation Research Part F: Traffic Psychology and Behaviour. 2015;30: 107-114. Available from: doi:10.1016/j.trf.2015.02.006

Fan Y, Guthrie A, Levinson D. Waiting time perceptions at transit stops and stations: Effects of basic amenities, gender, and security. Transportation Research Part A: Policy and Practice. 2016;88: 251-264. Available from: doi:10.1016/j.tra.2016.04.012

Kremelberg D. Practical Statistics: A Quick and Easy Guide to SPSS, STATA, and Other Statistical Software. Thousand Oaks: Sage Publications, Inc.; 2010.

Borooah VK. Logit and Probit: Ordered and Multinomial Models. Thousand Oaks: Sage Publications, Inc.; 2002.

Van Exel N, Rietveld P. Perceptions of public transport travel time and their effect on choice-sets among car drivers. Journal of Transport and Land Use. 2010;2(3): 75-86. Available from: doi:10.5198/jtlu.v2i3.15

Chambers RL, Steel DG, Wang S, Welsh AH. Maximum Likelihood Estimation for Sample Surveys. Boca Reton: CRC Press; 2012.

Copyright (c) 2023 Xuesong Feng, Feng, Xuesong , , Weixin Hua, Hua, Weixin , , Xuepeng Qian, Qian, Xuepeng ,

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