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Fuzzy analysis of comfort along travel chains

    Lajos Kisgyörgy Affiliation
    ; János Tóth Affiliation

Abstract

The competitiveness of a travel chain largely depends on the travel conditions along the sequence of journeys within the chain. This paper shows a method to analyse and to optimize the service quality along a travel chain. Travel comfort is a very important qualitative feature of the public transportation service, where travel comfort is used in a broader sense to describe ride quality and transfer quality including mobility, information, safety, security, and naturally comfort aspects. The analysis of travel comfort in the literature regards public transportation services. Several synthetic indices, which consider user judgment about service aspects, were developed to describe travel comfort, and comprehensive analyses have been published. However, to describe the competitiveness of the public transport the focus from the individual services should be moved toward the integrated service of the travel chain from the beginning to the end. The characteristics of travel comfort along the travel chain should be described and the location and rate of necessary interventions should be identified. In this paper we analyse the travel comfort features of travel chains. This paper proposes a method, which describes the travel comfort characteristics with synthetic indices based on the individual comfort indices of travel components, and uses a fuzzy approach to give an overall analysis of comfort conditions along the travel chain. The proposed method helps to identify the quality fluctuation and the weak points of a travel chain and makes the attractiveness of alternative travel chains comparable. An illustrative case study was carried out for one of the major transportation corridor of Budapest (Hungary), to exemplify the approach, where the validity of the method was tested as well. The results confirmed the usefulness and applicability of the methodology; by its application very valuable insights can be gained regarding the location and type of the necessary interventions. The results of our research are helpful to evaluate the actual service level of sustainable alternatives of individual car usage and to promote modal shift towards sustainable transportation modes.

Keyword : comfort analysis, travel chain, comfort index, fuzzy rules, public transportation, competitiveness

How to Cite
Kisgyörgy, L., & Tóth, J. (2020). Fuzzy analysis of comfort along travel chains. Transport, 35(2), 203-212. https://doi.org/10.3846/transport.2020.12634
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May 11, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

BKK. 2018. Maps. Budapesti Közlekedési Központ (BKK) – Centre for Budapest Transport, Hungary. Available from Internet: https://bkk.hu/en/maps

Cascetta, E.; Cartenì, A. 2014. The hedonic value of railways terminals. A quantitative analysis of the impact of stations quality on travellers behaviour, Transportation Research Part A: Policy and Practice 61: 41–52. https://doi.org/10.1016/j.tra.2013.12.008

Cherry, T.; Townsend, C. 2012. Assessment of potential improvements to metro–bus transfers in Bangkok, Thailand, Transportation Research Record: Journal of the Transportation Research Board 2276: 116–122. https://doi.org/10.3141/2276-14

Cox, E. 1998. The Fuzzy Systems Handbook: a Practitioner’s Guide to Building, Using, and Maintaining Fuzzy Systems. AP Professional. 716 p.

De Abreu e Silva, J.; Bazrafshan, H. 2013. User satisfaction of intermodal transfer facilities in Lisbon, Portugal, Transportation Research Record: Journal of the Transportation Research Board 2350: 102–110. https://doi.org/10.3141/2350-12

De Oña, J.; De Oña, R.; Eboli, L.; Mazzulla, G. 2013. Perceived service quality in bus transit service: a structural equation approach, Transport Policy 29: 219–226. https://doi.org/10.1016/j.tranpol.2013.07.001

De Oña, R.; De Oña, J. 2015. Analysis of transit quality of service through segmentation and classification tree techniques, Transportmetrica A: Transport Science 11(5): 365–387. https://doi.org/10.1080/23249935.2014.1003111

De Oña, R.; Eboli, L.; Mazzulla, G. 2014. Key factors affecting rail service quality in the Northern Italy: a decision tree approach, Transport 29(1): 75–83. https://doi.org/10.3846/16484142.2014.898216

Dell’Olio, L.; Ibeas, A.; Cecín, P.; Dell’Olio, F. 2011. Willingness to pay for improving service quality in a multimodal area, Transportation Research Part C: Emerging Technologies 19(6): 1060–1070. https://doi.org/10.1016/j.trc.2011.06.004

Duleba, S.; Shimazaki, Y.; Mishina, T. 2013. An analysis on the connections of factors in a public transport system by AHP-ISM, Transport 28(4): 404–412. https://doi.org/10.3846/16484142.2013.867282

Eboli, L.; Mazzulla, G. 2011. A methodology for evaluating transit service quality based on subjective and objective measures from the passenger’s point of view, Transport Policy 18(1): 172–181. https://doi.org/10.1016/j.tranpol.2010.07.007

Eboli, L.; Mazzulla, G. 2009. A new customer satisfaction index for evaluating transit service quality, Journal of Public Transportation 12(3): 21–37. https://doi.org/10.5038/2375-0901.12.3.2

Eboli, L.; Mazzulla, G. 2015. Relationships between rail passengers’ satisfaction and service quality: a framework for identifying key service factors, Public Transport 7(2): 185–201. https://doi.org/10.1007/s12469-014-0096-x

Eboli, L.; Mazzulla, G. 2007. Service quality attributes affecting customer satisfaction for bus transit, Journal of Public Transportation 10(3): 21–34. https://doi.org/10.5038/2375-0901.10.3.2

Friman, M.; Fellesson, M. 2009. Service supply and customer satisfaction in public transportation: the quality paradox, Journal of Public Transportation 12(4): 57–69. https://doi.org/10.5038/2375-0901.12.4.4

Guihaire, V.; Hao, J.-K. 2008. Transit network design and scheduling: a global review, Transportation Research Part A: Policy and Practice 42(10): 1251–1273. https://doi.org/10.1016/j.tra.2008.03.011

Hassan, M. N.; Hawas, Y. E.; Ahmed, K. 2013. A multi-dimensional framework for evaluating the transit service performance, Transportation Research Part A: Policy and Practice 50: 47–61. https://doi.org/10.1016/j.tra.2013.01.041

Hernandez, S.; Monzon, A.; De Oña, R. 2016. Urban transport interchanges: a methodology for evaluating perceived quality, Transportation Research Part A: Policy and Practice 84: 31–43. https://doi.org/10.1016/j.tra.2015.08.008

Iseki, H.; Taylor, B. D. 2010. Style versus service? An analysis of user perceptions of transit stops and stations, Journal of Public Transportation 13(3): 23–48. https://doi.org/10.5038/2375-0901.13.3.2

Kisgyörgy, L.; Vasvári, G. 2014. Travel chain based urban mobility, in Second International Conference on Traffic and Transport Engineering (ICTTE), 27–28 November 2014, Belgrade, Serbia, 69–75.

López-Lambas, M. E.; Monzon, A. 2010. Private funding and management for public interchanges in Madrid, Research in Transportation Economics 29(1): 323–328. https://doi.org/10.1016/j.retrec.2010.07.041

Markovits-Somogyi, R. 2011. Modification of a DEA-AHP based method for ranking the decision making units, in 9th International Conference on Data Envelopment Analysis (DEA2011), 25–27 August 2011, Thessaloniki, Greece.

Mukherjee, K. 2014. Analytic hierarchy process and technique for order preference by similarity to ideal solution: a bibliometric analysis ‘from’ past, present and future of AHP and TOPSIS, International Journal of Intelligent Engineering Informatics 2(2/3): 96–117. https://doi.org/10.1504/IJIEI.2014.066210

Nathanail, E. 2008. Measuring the quality of service for passengers on the Hellenic railways, Transportation Research Part A: Policy and Practice 42(1): 48–66. https://doi.org/10.1016/j.tra.2007.06.006

Outwater, M.; Sana, B.; Ferdous, N.; Woodford, B.; Lobb, J.; Schmitt, D.; Roux, J.; Bhat, C.; Sidharthan, R.; Sidharthan, R.; Hess, S. 2014. Characteristics of Premium Transit Services that Affect Choice of Mode. Transit Cooperative Research Program (TCRP) Report 166. Transportation Research Board (TRB), Washington, DC, US. 394 p. https://doi.org/10.17226/22401

Strandemar, K. 2005. On Objective Measures for Ride Comfort Evaluation. Royal Institute of Technology (KTH), Stockholm, Sweden. 99 p. Available from Internet: http://kth.diva-portal.org/smash/get/diva2:14199/FULL-TEXT01.pdf

TRB. 2003. A Guidebook for Developing a Transit Performance-Measurement System. Transit Cooperative Research Program (TCRP) Report 88. Transportation Research Board (TRB), Washington, DC, US. 383 p. Available from Internet: http://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_rpt_88.pdf

TRB. 2013. Transit Capacity and Quality of Service Manual. Transit Cooperative Research Program (TCRP) Report 165. Transportation Research Board (TRB), Washington, DC, US. 805 p. https://doi.org/10.17226/24766

Tsalis, P.; Naniopoulos, A. 2012. Accessibility management at municipal level for people with restricted mobility: the case of Thessaloniki, Procedia – Social and Behavioral Sciences 48: 2597–2606. https://doi.org/10.1016/j.sbspro.2012.06.1230

Tyrinopoulos, Y.; Antoniou, C. 2008. Public transit user satisfaction: variability and policy implications, Transport Policy 15(4): 260–272. https://doi.org/10.1016/j.tranpol.2008.06.002