Urban rail transit passenger service quality evaluation based on the KANO–Entropy–TOPSIS model: the China case

    Wencheng Huang Affiliation
    ; Yue Zhang Affiliation
    ; Yifei Xu Affiliation
    ; Rui Zhang Affiliation
    ; Minhao Xu Affiliation
    ; Yang Wang Affiliation


In order to evaluate the URTPSQ (Urban Rail Transit Passenger Service Quality) comprehensively, find the shortage of URTPSQ, find out the difference between the actual service situation and the passenger’s expectation and demand,and provide passengers with better travel services, a passenger-oriented KANO–Entropy–TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method is proposed and applied in this paper. Firstly, a KANO model is applied to select the service quality indicators from the 24 URTPSQ evaluation sub-indicators, according to the selection results, the KANO service quality indicators of URTPSQ are constructed. Then the sensitivity of the KANO service quality indicators based on the KANO model are calculated and ranked, the PS (Passenger Satisfaction) of each KANO service quality indicator by using the Entropy–TOPSIS method is calculated and ranked. Based on the difference between the sensitivity degree rank and the satisfaction degree rank of each KANO service quality indicator, determine the service quality KANO indicators of the URTPSQ that need to be improved significantly. A case study is conducted by taking the Chengdu subway system in China as a background. The results show that the Chengdu subway operation enterprises should pay attention to the must-be demand first, then the one-dimensional demand, finally the attractive demand. The three indicators, including transfer on the same floor in the station, service quality of staffs of urban rail transit enterprises,and cleanness in the station and passenger coach, need to be improved urgently. For the managers and operators of urban rail transit system, the passengers’ must-be demand should be satisfied first if the KANO model is applied to evaluate the service. The indicators with highest sensitivity degree and lowest TOPSIS value should be improved based on the KANO–Entropy–TOPSIS model.

First published online 14 December 2021

Keyword : urban rail transit, passenger service quality, KANO–Entropy–TOPSIS, sensitivity degree, satisfaction degree, passenger-oriented

How to Cite
Huang, W., Zhang, Y., Xu, Y., Zhang, R., Xu, M., & Wang, Y. (2022). Urban rail transit passenger service quality evaluation based on the KANO–Entropy–TOPSIS model: the China case. Transport, 37(2), 98–109.
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Jun 7, 2022
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Awasthi, A.; Chauhan, S. S.; Omrani, H.; Panahi, A. 2011. A hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating transportation service quality, Computers & Industrial Engineering 61(3): 637–646.

Aydin, N. 2017. A fuzzy-based multi-dimensional and multi-period service quality evaluation outline for rail transit systems, Transport Policy 55: 87–98.

Aydin, N.; Celik, E.; Taskin Gumus, A. 2015. A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul, Transportation Research Part A: Policy and Practice 77: 61–81.

Baum-Snow, N.; Kahn, M. E. 2005. Effects of Urban Rail Transit Expansions: Evidence from Sixteen Cities, 1970–2000, Brookings-Wharton Papers on Urban Affairs 2005: 147–206.

Brons, M.; Givoni, M.; Rietveld, P. 2009. Access to railway stations and its potential in increasing rail use, Transportation Research Part A: Policy and Practice 43(2): 136–149.

CTA URTPC. 2011. Metro Operational Performance Evaluation System: MOPES 2.0. 2011-GZ-001. China Transportation Association (CTA), Urban Rail Transit Professional Committee (URTPC). (in Chinese).

De Ona, J.; De Ona, R.; Eboli, L.; Mazzulla, G. 2015a. Heterogeneity in perceptions of service quality among groups of railway passengers, International Journal of Sustainable Transportation 9(8): 612–626.

De Ona, R.; Machado, J. L.; De Ona, J. 2015b. Perceived service quality, customer satisfaction, and behavioral intentions: structural equation model for the metro of Seville, Spain, Transportation Research Record: Journal of the Transportation Research Board 2538: 76–85.

De Ona, J.; De Ona, R.; Eboli, L.; Mazzulla, G. 2016. Index numbers for monitoring transit service quality, Transportation Research Part A: Policy and Practice 84: 18–30.

Diana, M. 2012. Measuring the satisfaction of multimodal travelers for local transit services in different urban contexts, Transportation Research Part A: Policy and Practice 46(1): 1–11.

Eboli, L.; Fu, Y.; Mazzulla, G. 2016. Multilevel comprehensive evaluation of the railway service quality, Procedia Engineering 137: 21–30.

Feng, X.; Li, K.; Ding, C.; Hua, W. 2019. Bayesian network modeling explorations of strategies on reducing perceived transfer time for urban rail transit service improvement in different seasons, Cities 95: 102474.

Garrett, T. A. 2004. Light Rail Transit in America: Policy Issues and Prospects for Economic Development. Research Department, Federal Reserve Bank of St. Louis, St. Louis, MO, US.

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.

He, L.; Song, W.; Wu, Z.; Xu, Z.; Zheng, M.; Ming, X. 2017. Quantification and integration of an improved Kano model into QFD based on multi-population adaptive genetic algorithm, Computers & Industrial Engineering 114: 183–194.

Huang, W.; Shuai, B. 2017. Using improved entropy-cloud model to select high-speed railway express freight train service sites, Mathematical Problems in Engineering 2017: 7824835.

Huang, W.; Shuai, B.; Sun, Y.; Li, M.; Pang, L. 2018a. Evaluation of risk in railway dangerous goods transportation system by integrated entropy–TOPSIS-coupling coordination method, China Safety Science Journal 28(2): 134–138. (in Chinese).

Huang, W.; Shuai, B.; Sun, Y.; Wang, Y.; Antwi, E. 2018b. Using entropy–TOPSIS method to evaluate urban rail transit system operation performance: the China case, Transportation Research Part A: Policy and Practice 111: 292–303.

Huang, W.; Shuai, B.; Wang, L.; Antwi, E. 2017. Railway container station reselection approach and application: based on entropy-cloud model, Mathematical Problems in Engineering 2017: 8701081.

Huang, W.; Shuai, B.; Zuo, J.; Wang, L.; Mao, J. 2016. Corrected entropy based operation performance evaluation about urban rail transportation non-networks system, Journal of Transportation Systems Engineering and Information Technology 16(6): 115–121. (in Chinese).

Kang, L.; Wu, J.; Sun, H.; Zhu, X.; Gao, Z. 2015. A case study on the coordination of last trains for the Beijing subway network, Transportation Research Part B: Methodological 72: 112–127.

Kano, N.; Seraku, N.; Takahashi, F.; Tsuji, S.-C. 1984. Attractive quality and must-be quality, The Journal of the Japanese Society for Quality Control 14(2): 147–156. (in Japanese).

Kuo, T. 2017. A modified TOPSIS with a different ranking index, European Journal of Operational Research 260(1): 152–160.

Kwong, C. K.; Chen, Y.; Chan, K. Y. 2011. A methodology of integrating marketing with engineering for defining design specifications of new products, Journal of Engineering Design 22(3): 201–213.

Litman, T. 2007. Evaluating rail transit benefits: a comment, Transport Policy 14(1): 94–97.

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.

Nedeliakova, E.; Sekulova, J.; Nedeliak, I.; Ľoch, M. 2014. Methodics of identification level of service quality in railway transport, Procedia – Social and Behavioral Sciences 110: 320–329.

Nelson, P.; Baglino, A.; Harrington, W.; Safirova, E.; Lipman, A. 2007. Transit in Washington, DC: current benefits and optimal level of provision, Journal of Urban Economics 62(2): 231–251.

SC PRC. 2012. Guiding Opinions on Priority Urban Development of Public Transport by the State Council of the People’s Republic of China. State Council of the People’s Republic of China (SC PRC). (in Chinese). Available from Internet:

Semchugova, E.; Zyryanov, V.; Negrov, N.; Nikitina, A. 2017. Models of estimation of application of passenger service quality parameters, Transportation Research Procedia 20: 584–590.

Shannon, C. E. 2001. A mathematical theory of communication, ACM SIGMOBILE Mobile Computing and Communications Review 5(1): 3–55.

Shannon, C. E.; Weaver, W. 1971. The Mathematical Theory of Communication. 16th Edition. The University of Illinois Press. 144 p.

Shen, W.; Xiao, W.; Wang, X. 2016. Passenger satisfaction evaluation model for urban rail transit: a structural equation modeling based on partial least squares, Transport Policy 46: 20–31.

Štefancova, V.; Nedeliakova, E.; Lopez-Escolano, C. 2017. Connection of dynamic quality modeling and total service management in railway transport operation, Procedia Engineering 192: 834–839.

Sun, H.; Wu, J.; Wu, L.; Yan, X.; Gao, Z. 2016. Estimating the influence of common disruptions on urban rail transit networks, Transportation Research Part A: Policy and Practice 94: 62–75.

Vuk, G. 2005. Transport impacts of the Copenhagen metro, Journal of Transport Geography 13(3) 223–233.

Walczak, D.; Rutkowska, A. 2017. Project rankings for participatory budget based on the fuzzy TOPSIS method, European Journal of Operational Research 260(2): 706–714.

Wang, C.-H. 2013. Incorporating customer satisfaction into the decision-making process of product configuration: a fuzzy Kano perspective, International Journal of Production Research 51(22): 6651–6662.

Zeleny, M. (Ed.). 1976. Multiple Criteria Decision Making Kyoto 1975. Springer. 350 p.