The model assessing the impact of price and provided services on the quality of the trip by train: MCDM approach

Simple item page
dc.contributor.authorSivilevičius, Henrikas
dc.contributor.authorMaskeliūnaitė, Lijana
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2019-06-15T15:50:59Z
dc.date.available2019-06-15T15:50:59Z
dc.description.abstractLong-distance transportation of passengers is usually performed by air and rail transport. The time and cost of the trip, as well as quality and variety of services and other economic criteria, strongly influence the choice of one of the competing transport facilities. The quality of the trip by train is determined by the criteria of four groups associated with the vehicle elements and the state of the railway line, organization of the trip by train, its technology, the price of the trip and the provided services (PTPS) and safety of the trip by train. The paper presents a model of the major component of the comprehensive quality index (CQI), characterizing a trip by an international train from such aspects as the PTPS. Based on using the analytic hierarchy process (AHP), the weights of these criteria groups and the weights of the criteria of each group are determined. A survey of the respondents of three categories, including passengers, service staff of the train and the administration staff of the joint-stock company, is conducted to know their opinions about various aspects of the trip described by the considered criteria. The model is based on the average weight and the weights of six criteria of the group, describing the PTPS, which are multiplied by the calculated variables. This allows the quality of the trip by train, depending only on these criteria, to be expressed in a single number. The numerical example shows that the suggested model yields reliable data and can be used in practice for evaluating the quality of trips by various international trains.en
dc.formattext
dc.identifier.doi10.15240/tul/001/2019-2-004
dc.identifier.eissn2336-5604
dc.identifier.issn1212-3609
dc.identifier.urihttps://dspace.tul.cz/handle/15240/152595
dc.language.isoen
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisher.abbreviationTUL
dc.relation.isbasedonAghdaie, M. H., Hashemkhani Zolfani, S., & Zavadskas, E. K. (2013). Decision making in machine tool selection: an integrated approach with SWARA and CORPAS-G methods. Inzinerine Ekonomika – Engineering Economics, 24(1), 5-17. https://dx.doi.org/10.s+SS/j01.ee.24.1.2822.
dc.relation.isbasedonAlbaladejo, I., & González-Martínez, M. (2018). A nonlinear dynamic model for international tourism demand on the Spanish Mediterranean coasts. E&M Ekonomie a Management, 21(4), 65-78. https://doi.org/10.15240/tul/001/2018-4-005.
dc.relation.isbasedonAllen, J., Eboli, L., Mazzulla, G., & Ortúzar, J. D. (2018). Effect of critical incidents on public transport satisfaction and loyalty: an Ordinal Probit SEM-MIMIC approach. Transportation. https://doi.org/10.1007/s11116-018-9921-4.
dc.relation.isbasedonAvdovskiy, A. A., Badaev, A. S., Belov, K. A., et al. (2013). Organizatsiya zheleznodorozhnykh passazhirskikh perevozok. Moskva: Akademiya.
dc.relation.isbasedonBajćetić, S., Tica, S., Živanović, P., Milovanović, B., & Dorojević, A. (2018). Analysis of public transport users’ satisfaction using quality function deployment: Belgrade case study. Transport, 33(3), 609-618. https://doi.org/10.3846/transport.2018.1570.
dc.relation.isbasedonBatley, R., Dargay, J., & Wardman, M. (2011). The impact of lateness and reliability on passenger rail demand. Transportation Research Part E: Logistics and Transportation Review, 47(1), 61-72. https://doi.org/10.1016/j.tre.2010.07.004.
dc.relation.isbasedonBelov, I. V., Tereshina, N. P., Galaburda, V. G., et al. (2001). Ekonomika zheleznodorozhnogo transporta. Moskva: Marshrut.
dc.relation.isbasedonBörjesson, M. (2014). Forecasting demand for high speed rail. Transportation Research Part A: Policy and Practice, 70, 81-92. https://dx.doi.org/10.1016/j.tra.2014.10.010.
dc.relation.isbasedonBureika, G., Bekintis, G., Liudvinavičius, L., & Vaičiūnas, G. (2013). Applying analytic hierarchy process to assess traffic safety risk of railway infrastructure. Maintenance and Reliability = Eksploatacja I Niezawodność, 15(4), 376-383.
dc.relation.isbasedonChen, S., Leng, Y., Mao, B., & Liu, S. (2014). Integrated weight-based multi-criteria evaluation on transfer in large transport terminals: A case study of the Beijing South railway station. Transportation Research Part A: Policy and Practice, 66, 13-26. https://dx.doi.org/10.1016/j.tra.2014.04.015.
dc.relation.isbasedonCheng, M.-Y., Su, C.-W., Tsai, M.-H., & Lin, K.-S. (2012). Data preprocessing for artificial neural network applications in prioritizing railroad projects – a practical experience in Taiwan. Journal of Civil Engineering and Management, 18(4), 483-494. https://doi.org/10.3846/13923730.2012.2699914.
dc.relation.isbasedonChristogiannis, E., & Pyrgidis, C. (2014). Investigation of the impact of traffic composition on the economic profitability of a new railway corridor. Proceedings of the Institution of Mechanical Engineers. Part F: Journal of Rail and Rapid Transit, 228(4), 389-401. https://doi.org/10.1177/0954409713478590.
dc.relation.isbasedonDe 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/10.3846/16484142.2014.894938.
dc.relation.isbasedonDe Oña, J., De Oña, R., Eboli, L., & Mazzulla, G. (2015). Heterogeneity in perceptions of service quality among groups of railway passengers, in the northern Italy: a decision tree approach. International Journal of Sustainable Transportation, 9, 612-626. https://doi.org/10.1080/15568318.2013.849318.
dc.relation.isbasedonDe Oña, J., De Oña, R., Eboli, L., Forciniti, C., & Mazzulla, G. (2016). Transit passengers’ behavioural intentions: the influence of service quality and customer satisfaction. Transportmetrica A: Transport Science, 12(5), 385-412. https://dx.doi.org/10.1080/23249935.2016.1146365.
dc.relation.isbasedonDlamini, N. G. (2011). The environmental impacts of various rail passenger ticketing opinions in Japan. Transportation Research Part D: Transport and Environment, 16(5), 365-371. https://doi.org/10.1016/j.trd.2011.02.002.
dc.relation.isbasedonEboli, L., & Mazzulla, G. (2015). Relationships between rail passengers’ satisfaction and service quality: a framework for identifying the key service factors. Public Transport, 7(2), 185-201. https://doi.org/10.1007/s12469-014-0096-x.
dc.relation.isbasedonEboli, 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.
dc.relation.isbasedonGelumbickas, G., & Vaičiūnas, G. (2011). Analysis of passenger rolling stock faults and its statistics in Lithuania. Transport, 26(3), 315-319. https://doi.org/10.3846/16484142.2011.618190.
dc.relation.isbasedonGudienė, N., Banaitis, A., Podvezko, V., & Banaitienė, N. (2014). Identification and evaluation of the critical success factors for construction projects in Lithuania: AHP approach. Journal of Civil Engineering and Management, 20(3), 350-359. https://doi.org/10.3846/13923730.2014.914082.
dc.relation.isbasedonGuo, S., Yu, L., Chen, X., & Zhang, Y. (2011). Modelling waiting time for passengers transferring from rail to buses. Transportation Planning and Technology, 34(8), 795-809. https://doi.org/10.1080/03081060.2011.613589.
dc.relation.isbasedonGuo, Z., & Wilson, N. H. M. (2011). Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground. Transportation research Part A: Policy and Practice, 45(2), 91-104. https://doi.org/10.1016/j.tra.2010.11.002.
dc.relation.isbasedonHadiuzzman, M., Farazi, N. P., Hossain, S., & Malik, D. M. G. (2017). An exploratory analysis of observed and latent variables affecting intercity train service quality in developing countries. Transportation. https://doi.org./10.1007/s11116-017-9843-6.
dc.relation.isbasedonHansen, I. A. (2010). Railway network timetabling and dynamic traffic management. International Journal of Civil Engineering, 8(1), 19-32.
dc.relation.isbasedonHashemkhani Zolfani, S., & Antucheviciene, J. (2012). Team member selecting based on AHP and TOPSIS Grey. Inzinerine Ekonomika – Engineering Economics, 23(4), 425-434. https://doi.org/10.5755/j01ee.23.4.2725.
dc.relation.isbasedonHashemkhani Zolfani, S., & Saparauskas, J. (2013). New application of SWARA method in prioritizing sustainability assessment indicators of energy system. Inzinerine Ekonomika – Engineering Economics, 24(5), 408-414. https://doi.org/10.5755/j01.ee.24.5.4526.
dc.relation.isbasedonHashemkhani Zolfani, S., Rezaeiniya, N., Pourhossein, M., & Zavadskas, K. E. (2012). Decision making on advertisement strategy selection based on life cycle of products by applying FAHP and TOPSIS GREY: growth stage perspective; a case about food industry in Iran. Inzinerine Ekonomika – Engineering Economics, 23(5), 471-484. https://dx.doi.org/10.5755/j01.ee.23.5.3134.
dc.relation.isbasedonHu, S.-R., & Liu, C.-T. (2014). Optimizing headways for mass rapid transit services. Journal of Transportation Engineering, 140(11). https://doi.org/10.1061/(ASCE)TE.1943-5436.0000703.
dc.relation.isbasedonJafarian, E., & Rezvani, M. A. (2012). Application of fuzzy fault tree analysis for evaluation of railway safety risks: an evaluation of root causes for passenger train derailment. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 226(1), 14-25. https://doi.org/10.1177/095440971403678.
dc.relation.isbasedonJánošikova, L., Kavicka, A., & Bazant, M. (2014). Optimal operation scheduling and platform track assignment in a passenger railway station. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 228(3), 271-284. https://doi.org/10.1177/0954409712472275.
dc.relation.isbasedonKandt, J., Rode, P., Hoffman, C., Graff, A., & Smith, D. (2015). Gauging interventions for sustainable travel: A comparative study of travel attitudes in Berlin and London. Transportation Research Part A: Policy and Practice, 80, 35-48. https://doi.org/10.1016/j.tra.2015.07.008.
dc.relation.isbasedonKaya, I., & Kahraman, C. (2014). A comparison of fuzzy multicriteria decision making methods for intelligent building assessment. Journal of Civil Engineering and Management, 20(1), 59-69. https://doi.org/10.3846/13923730.2013.801906.
dc.relation.isbasedonKendall, M., & Gibbons, J. D. (1990). Rank Correlation Methods (5th ed.). London: Oxford University Press.
dc.relation.isbasedonKirschstein, T., & Meisel, F. (2015). GHG-emission models for assessing the eco-friendliness of road and rail freight transports. Transportation Research Part B: Methodological, 73, 13-33. https://doi.org/10.1016/j.trb.2014.12.004.
dc.relation.isbasedonLee, A. H. I., Kang, H.-Y., & Lin, C.-Y. (2014). A performance evaluation model using FAHP/DEA and the Malmquist Productivity Index to assess the photovoltaics industry in Taiwan. Journal of Testing and Evaluation, 42(1), 211-228. https://doi.org/10.1520/JTE20130008.
dc.relation.isbasedonMaskeliūnaitė, L., & Sivilevičius, H. (2012). Expert evaluation of criteria describing the quality of travelling by international passenger train: technological, economic and safety perspectives. Technological and Economic Development of Economy, 18(3), 544-566. https://doi.org/10.3846/20294913.2012.710178.
dc.relation.isbasedonMaskeliūnaitė, L., & Sivilevičius, H. (2014). The model for evaluating the criteria describing the quality of the trip by international train. Technological and Economic Development of Economy, 20(3), 484-506. https://dx.doi.org/10.3846/20294913.2014.949333.
dc.relation.isbasedonMiranda, S., Tavares, P., & Queiró, R. (2018). Perceived service quality and customer satisfaction: A fuzzy set QCA approach in the railway sector. Journal of Business Research, 89, 371-377. https://doi.org/10.1016/j.jbusres.2017.12.040.
dc.relation.isbasedonMiroshnichenko, O. F., & Oginskaya, A. E. (2013). Novye metodicheskie podkhody pri opredelenii ekonomicheskoy effektivnosti passazhirskogo poezda dal′nego sledovaniya v usloviyakh funktsionirovaniya OAO ,,Federal’noy passazhirskoy kompanii“. Vestnik VNIIZHT, 5, 3-7.
dc.relation.isbasedonNoorzaei, J., Pour, P. M., Jaafar, M. S., Fong, Y. C., & Thanoon, W. A.-M. (2012). Numerical simulation of railway track supporting system using finite-infinite and thin layer elements under impulsive loads. Journal of Civil Engineering and Management, 18(2), 245-252. https://doi.org/10.3846/13923730.2012.671286.
dc.relation.isbasedonNowell, R., & Sutton, A. (2011). The benefits and development of windows with improved passenger containment. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 225(3), 247-257. https://doi.org/10.1243/09544097JRRT388.
dc.relation.isbasedonPaha, J., Rompf, D., & Warneeke, C. (2013). Customer choice patterns in passenger rail competition. Transportation Research Part A: Policy and Practice, 50, 209-227. https://doi.org/10.1016/j.tra.2013.01.037.
dc.relation.isbasedonPersson, R. (2010). Tilting trains: benefits and motion sickness. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 224(6), 513-522. https://doi.org/10.1243/09544097JRRT282.
dc.relation.isbasedonPodvezko, V., & Sivilevičius, H. (2013). The use of AHP and rank correlation methods for determining the significance of the interaction between the elements of a transport system having a strong influence on traffic safety. Transport, 28(4), 389-403. https://doi.org/10.3846/16484142.2013.866980.
dc.relation.isbasedonSaaty, T. L. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill.
dc.relation.isbasedonSeelhorst, M., & Liu, Y. (2015). Latent air travel preferences: understanding the role of frequent flyer programs on itinerary choice. Transportation Research Part A: Policy and Practice, 80, 49-61. https://dx.doi.org/10.1016/j.tra.2015.07.007.
dc.relation.isbasedonSivilevičius, H. (2011). Application of expert evaluation method to determine the importance of operating asphalt mixing plant quality criteria and rank correlation. The Baltic Journal of Road and Bridge Engineering, 6(1), 48-58. https://doi.org/10.3846/bjrbe.2011.07.
dc.relation.isbasedonSivilevičius, H., & Maskeliūnaitė, L. (2014). The numerical example for evaluating the criteria describing the quality of the trip by international train. E&M Ekonomie a Management, 17(2), 73-86. https://dx.doi.org.10.15240/tul/001/2014-2-006/1648.
dc.relation.isbasedonSivilevičius, H., Maskeliūnaitė, L., Petkevičienė, B., & Petkevičius, K. (2012). The model of evaluating the criteria, describing the quality of organization and technology of travel by international train. Transport, 27(3), 307-319. https://dx.doi.org/10.38464142.2012.724448.
dc.relation.isbasedonSivilevičius, H., & Maskeliūnaitė, L. (2018). Multiple criteria evaluation and the inverse hierarchy model for justifying the choise of rail transport mode. Promet – Traffic & Transportation, 30(1), 57-69. https://doi.org/10.7307/ptt.v30i1.2417.
dc.relation.isbasedonŠoltés, V., & Gavurová, B. (2014). The functionality comparison of the health care by the Analytical Hierarchy process method. E&M Ekononie a Management, 17(3), 100-117. https://doi.org/10.15240/tul/001/2014-3-009.
dc.relation.isbasedonVaičiūnas, G., & Bureika, G. (2014). Approach modelling of constant interfailure process of renewal multi-unit fleet. Eksploatacja i Niezavodność-Maintenance and Reliability, 16(3), 415-421.
dc.relation.isbasedonVidal, L.-A., Marle, F., & Bacquet, J.-C. (2011). Using a Delphi process and the Analytic Hierarchy Process (AHP) to evaluate the complexity of projects. Expert Systems with Applications, 38(5), 5388-5405. https://doi.org/10.1016/j.eswa.2010.10.016.
dc.relation.isbasedonVitić-Ćetković, A., & Bauk, S. (2014). E-services and positioning of passenger ports in the context of cruise tourism promotion. Promet – Traffic&Transportation, 26(1), 83-93.
dc.relation.isbasedonWang, W.-C., Yu, W.-d., Yang, I-T., Lin, C.-C., Lee, M.-T., & Cheng, Y.-Y. (2013). Applying the AHP to support the best-value contractor selection-lessons learned from two case studies in Taiwan. Journal of Civil Engineering and Management, 19(1), 24-36. https://doi.org/10.3846/13923730.2012.734851.
dc.relation.isbasedonWoo, C. K., Cheng, Y. S., Li, R., Chiu, A., Ho, S. T., & Horowitz, I. (2015). Can Hong Kong price-manage its cross-harbor-tunnel congestion? Transportation Research Part A: Policy and Practice, 82, 94-109. https://doi.org/10.1016/j.tra.2015.09.002.
dc.relation.isbasedonYaghini, M., Nikoo, N., & Ahadi, H. R. (2014). An integer programming model for analysing impacts of different train types on railway line capacity. Transport, 29(1), 28-35. https://doi.org/10.3846/16484142.2014.894938.
dc.relation.isbasedonZalar, D., Užpalytė-Vitkūnienė, R., Rebolj, D., & Lep, M. (2018). Amethodological framework for measuring the level of convenience of transport ticketing systems. Transport, 33(4), 1005-1016. https://doi.org/10.3846/16484142.2017.1300783.
dc.relation.isbasedonZavadskas, E. K., & Podvezko, V. (2016). Integrated determination of objective criteria weights in MCDM. International Journal of Information Technology & Decision Making, 15(2), 267-283. https://doi.org/10.1142/S0219622016500036.
dc.relation.isbasedonZhao, J., & Deng, W. (2013). Fuzzy multiobjective decision support model for urban rail transit projects in China. Transport, 28(3), 224-235. https://doi.org/10.3846/16484142.2013.829119
dc.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.relation.isrefereedtrue
dc.rightsCC BY-NC
dc.subjecttransportationen
dc.subjectrailway trip qualityen
dc.subjectinternational trainen
dc.subjectpassenger servicesen
dc.subjectMCDM methodsen
dc.subjectpractical applicationen
dc.subject.classificationCO2
dc.subject.classificationR4
dc.subject.classificationL62
dc.subject.classificationL92
dc.titleThe model assessing the impact of price and provided services on the quality of the trip by train: MCDM approachen
dc.typeArticleen
local.accessopen
local.citation.epage67
local.citation.spage51
local.facultyFaculty of Economics
local.filenameEM_2_2019_04
local.fulltextyes
local.relation.abbreviationE+Mcs
local.relation.abbreviationE&Men
local.relation.issue2
local.relation.volume22
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
EM_2_2019_04.pdf
Size:
1.59 MB
Format:
Adobe Portable Document Format
Description:
článek
Download
Collections
Číslo 2