Urban rail transit passenger service quality evaluation based on the KANO–Entropy–TOPSIS model: the China case
Abstract
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
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Aydin, N. 2017. A fuzzy-based multi-dimensional and multi-period service quality evaluation outline for rail transit systems, Transport Policy 55: 87–98. https://doi.org/10.1016/j.tranpol.2017.02.001
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. https://doi.org/10.1016/j.tra.2015.03.029
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. https://doi.org/10.1353/urb.2006.0001
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. https://doi.org/10.1016/j.tra.2008.08.002
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. https://doi.org/10.1080/15568318.2013.849318
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. https://doi.org/10.3141/2538-09
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. https://doi.org/10.1016/j.tra.2015.05.018
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. https://doi.org/10.1016/j.tra.2011.09.018
Eboli, L.; Fu, Y.; Mazzulla, G. 2016. Multilevel comprehensive evaluation of the railway service quality, Procedia Engineering 137: 21–30. https://doi.org/10.1016/j.proeng.2016.01.230
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. https://doi.org/10.1016/j.cities.2019.102474
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. https://doi.org/10.1016/j.tra.2013.01.041
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. https://doi.org/10.1016/j.cie.2017.10.009
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. https://doi.org/10.1155/2017/7824835
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). https://doi.org/10.16265/j.cnki.issn1003-3033.2018.02.023
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. https://doi.org/10.1016/j.tra.2018.03.025
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. https://doi.org/10.1155/2017/8701081
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. https://doi.org/10.1016/j.trb.2014.09.003
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. https://doi.org/10.1016/j.ejor.2016.11.052
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. https://doi.org/10.1080/09544820903173180
Litman, T. 2007. Evaluating rail transit benefits: a comment, Transport Policy 14(1): 94–97. https://doi.org/10.1016/j.tranpol.2006.09.003
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
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. https://doi.org/10.1016/j.sbspro.2013.12.876
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. https://doi.org/10.1016/j.jue.2007.02.001
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: http://english.www.gov.cn
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. https://doi.org/10.1016/j.trpro.2017.01.094
Shannon, C. E. 2001. A mathematical theory of communication, ACM SIGMOBILE Mobile Computing and Communications Review 5(1): 3–55. https://doi.org/10.1145/584091.584093
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. https://doi.org/10.1016/j.tranpol.2015.10.006
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. https://doi.org/10.1016/j.proeng.2017.06.144
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. https://doi.org/10.1016/j.tra.2016.09.006
Vuk, G. 2005. Transport impacts of the Copenhagen metro, Journal of Transport Geography 13(3) 223–233. https://doi.org/10.1016/j.jtrangeo.2004.10.005
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. https://doi.org/10.1016/j.ejor.2016.12.044
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. https://doi.org/10.1080/00207543.2013.825742
Zeleny, M. (Ed.). 1976. Multiple Criteria Decision Making Kyoto 1975. Springer. 350 p. https://doi.org/10.1007/978-3-642-45486-8