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Evaluation of public transportation systems for sustainable cities using an integrated fuzzy multi-criteria group decision-making model

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Abstract

In this era of increasing demand for mobility and rapid urban growth, there is a pressing need for a public transit system that is safe, fast, reliable, well-connected, and sustainable. Furthermore, it is essential to reduce the external costs associated with urban transportation, including environmental pollution, noise, congestion, and accidents, to foster sustainable cities. Choosing the right urban transportation system can meet this goal, but it is not an accessible business for decision-makers in the face of several conflicting criteria and ambiguities in the evaluation process. To cope with this, the current paper suggests a multi-criteria group decision-making (MCGDM) framework consisting of fuzzy BWM (Best–Worst method) and fuzzy MAIRCIA (Multi-Attribute Ideal-Real Comparative Analysis) techniques. This extended MCGDM approach has been applied to evaluate six urban transport systems, namely, Trams, Light Rail Trams, Metro (Subway), Bus Rapid Transport, Commuter Trains, and Public Buses based on 11 selection criteria which we have determined after consultation with highly experienced professionals. The fuzzy BWM technique is employed to identify the weights of the criteria. The fuzzy MAIRCA technique is utilized for ranking the alternatives using the calculated weights of the criteria. The proposed approach's validation has been examined with an extensive robustness check. The study is conducted from a general perspective, i.e., not restricted to a particular city. However, with the identified selection criteria, the proposed decision-making procedure can be repeated for a specific city considering any specific requirements, constraints, or limitations of that city.

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References

  • Achillas, Ch., Vlachokostas, Ch., Moussiopoulos, N., & Banias, G. (2011). Prioritize strategies to confront environmental deterioration in urban areas: Multicriteria assessment of public opinion and experts’ views. Cities, 28(5), 414–423.

    Article  Google Scholar 

  • Alkharabsheh, A., Moslem, S., Oubahman, L., & Duleba, S. (2021). An integrated approach of multi-criteria decision-making and grey theory for evaluating urban public transportation systems. Sustainability, 13(5), 2740.

    Article  Google Scholar 

  • Awasthi, A., Chauhan, S. S., & Goyal, S. K. (2011a). A multi-criteria decision-making approach for location planning for urban distribution centers under uncertainty. Mathematical and Computer Modelling, 53(1–2), 98–109.

    Article  Google Scholar 

  • Awasthi, A., Chauhan, S., & Omrani, H. (2011b). Application of fuzzy TOPSIS in evaluating sustainable transportation systems. Expert Systems with Applications, 38(10), 12270–12280.

    Article  Google Scholar 

  • Ayadi, H., Hamani, N., Kermad, L., & Benaissa, M. (2021). Novel fuzzy composite indicators for locating a logistics platform under sustainability perspectives. Sustainability, 13(7), 3891.

    Article  Google Scholar 

  • Banae Costa, C. A., Corte, J. M., & Vansnick, J. C. (2016). On the Mathematical Foundations of MACBETH. In S. Greco, M. Ehrgott, & J. Figueira (Eds.), Multiple criteria decision analysis. International series in operations research & management science. New York: Springer.

    Google Scholar 

  • Barfod, M. B., Salling, K. B., & Leleur, S. (2011). Composite decision support by combining cost-benefit and multi-criteria decision analysis. Decision Support Systems, 51(1), 167–175.

    Article  Google Scholar 

  • Boral, S., Howard, I., Chaturvedi, S. K., McKee, K., & Naikan, V. N. A. (2020). An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA. Engineering Failure Analysis, 108, 104195.

    Article  Google Scholar 

  • Bozanic, D., Tešic, D., & Kocic, J. (2019). Multi-criteria FUCOM-Fuzzy MABAC model for the selection of location for construction of single-span bailey bridge. Decision Making: Applications in Management and Engineering, 2, 132–146.

    Google Scholar 

  • Brauers, W., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35, 445–469.

    Google Scholar 

  • Browne, D., & Ryan, L. (2011). Comparative analysis of evaluation techniques for transport policies. Environmental Impact Assessment Review, 31(3), 226–233.

    Article  Google Scholar 

  • Brucker, K., Verbeke, A., & Macharis, C. (2004). The applicability multicriteria-analysis to the evaluation of intelligent transport systems (ITS). Economic impacts of intelligent transportation systems: Innovations and case studies. Research in Transportation Economics, 8, 151–179.

    Article  Google Scholar 

  • Caliskan, N. (2006). A decision support approach for the evaluation of transport investment alternatives. European Journal of Operational Research, 175(3), 1696–1704.

    Article  Google Scholar 

  • Celik, E., Bilisik, O. N., Erdogan, M., Gumus, A. T., & Baracli, H. (2013). An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul. Transportation Research Part e: Logistics and Transportation Review, 58, 28–51.

    Article  Google Scholar 

  • Chang, Y., Wey, W., & Tseng, H. (2009). Using ANP priorities with goal programming for revitalization strategies in historic transport: A case study of the Alishan Forest Railway. Expert Systems with Applications, 36(4), 8682–8690.

    Article  Google Scholar 

  • Data Bridge (2021). Global Smart Cities Market – Industry Trends and Forecast to 2027, https://www.databridgemarketresearch.com/reports/global-smart-cities-market, Access date: 04.08.2021.

  • European Commission (2021). The future of cities, https://urban.jrc.ec.europa.eu/thefutureofcities. Access date: 03.10.2021.

  • Ecer F. (2020). Çok Kriterli Karar Verme Geçmişten Günümüze Kapsamlı Bir Yaklaşım, Yayın Yeri:Seçkin Yayıncılık, Basım sayısı:1, ISBN:978-975-02-6017-9.

  • Ecer, F. (2022). Multi-criteria decision making for green supplier selection using interval type-2 fuzzy AHP: A case study of a home appliance manufacturer. Operational Research, 22, 199–233.

    Article  Google Scholar 

  • Ecer, F., & Pamucar, D. (2020). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multicriteria model. Journal of Cleaner Production, 266, 121981.

    Article  Google Scholar 

  • Fülöp, J. (2005). Introduction to Decision Making Methods. Laboratory of Operations Research and Decision Systems: Computer and Automation Institute, Hungarian Academy of Sciences, 1, 1–16.

    Google Scholar 

  • Garg, C. P., & Kashav, V. (2019). Evaluating value creating factors in greening the transportation of Global Maritime Supply Chains (GMSCs) of containerized freight. Transportation Research Part d: Transport and Environment, 73, 162–186.

    Article  Google Scholar 

  • Garg, C. P., & Sharma, A. (2020). Sustainable outsourcing partner selection and evaluation using an integrated BWM–VIKOR framework. Environment, Development and Sustainability, 22, 1529–1557.

    Article  Google Scholar 

  • Gul, M., & Ak, M. F. (2020). Assessment of occupational risks from human health and environmental perspectives: A new integrated approach and its application using fuzzy BWM and fuzzy MAIRCA. Stochastic Environmental Research and Risk Assessment, 34, 1231–1262.

    Article  Google Scholar 

  • Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems., 121, 23–31.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Hull, A. (2008). Policy integration: What will it take to achieve more sustainable transport solutions in cities. Transport Policy, 15(2), 94–103.

    Article  Google Scholar 

  • Iniestra, J., & Garda, J. (2009). Multicriteria decisions on interdependent infrastructure transportation projects using an evolutionary-based framework. Journal of Applied Soft Computing, 9(2), 512–526.

    Article  Google Scholar 

  • Ivanovic, I., Grujicic, D., Macura, D., Jovic, J., & Bojovic, N. (2013). One approach for road transport project selection. Transport Policy, 25, 22–29.

    Article  Google Scholar 

  • Jabbari, M., Sheikh, S., Rabiee, M., & Oztekin, A. (2022). A collaborative decision support system for multi-criteria automatic clustering. Decision Support Systems, 153, 113671.

    Article  Google Scholar 

  • Jones, S., Tefe, M., & Appiah-Opoku, S. (2013). Proposed framework for sustainability screening of urban transport projects in developing countries: A case study of Accra, Ghana. Transportation Research Part a: Policy and Practice, 49, 21–34.

    Google Scholar 

  • Kalifa, M., Özdemir, A., Özkan, A., & Banar, M. (2022). Application of Multi-Criteria Decision analysis including sustainable indicators for prioritization of public transport system. Integrated Environmental Assessment and Management, 18(1), 25–38.

    Article  Google Scholar 

  • Kavran, Z., Stefancic, G., & Presecki, A. (2007). Multicriteria analysis and public transport management. WIT Transactions on the Built Environment, 96, 85–90.

    Article  Google Scholar 

  • Kayapinar Kaya, S. (2020). Evaluation of the effect of COVID-19 on countries’ sustainable development level: A comparative MCDM framework. Operational Research in Engineering Sciences: Theory and Applications, 3(3), 101–122.

    Google Scholar 

  • Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of Evaluation Based on Distance from Average Solution (EDAS). Informatica, 26, 435–451.

    Article  Google Scholar 

  • Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation & Economic Cybernetics Studies & Research, 50(3), 25–44.

    Google Scholar 

  • Kundu, P., Kar, S., & Maiti, M. (2014). A fuzzy MCDM method and an application to solid transportation problem with mode preference. Soft Computing, 18(9), 1853–1864.

    Article  Google Scholar 

  • Kuo, M.-S., & Liang, G.-S. (2012). A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers. Applied Soft Computing, 12(1), 476–485.

    Article  Google Scholar 

  • Labbouz, S., Roy, B., & Diab, Y. (2008). Implementing a public transport line: Multi-criteria decision-making methods that facilitate concertation. Operational Research, 8(1), 5–31.

    Article  Google Scholar 

  • Li, Y.-T., Huang, B., & Lee, D.-H. (2011). Multimodal, multicriteria dynamic route choice: A GIS-microscopic traffic simulation approach. Annals of GIS, 17(3), 173–187.

    Article  Google Scholar 

  • Liu, K. F. R., & Lai, J.-H. (2009). Decision-support for environmental impact assessment: A hybrid approach using fuzzy logic and fuzzy analytic network process. Expert Systems with Applications, 36(3), 5119–5136.

    Article  Google Scholar 

  • Macharis, C., Verbeke, A., & De Brucker, K. (2004). The strategic evaluation of new technologies through multicriteria analysis: The advisors’ case. Research in Transportation Economics, 8, 443–462.

    Article  Google Scholar 

  • Macharis, C., De Witte, A., & Turcksin, L. (2010). The multi-actor multi-criteria analysis (MAMCA) application in the Flemish long-term decision-making process on mobility and logistics. Transport Policy, 17(5), 303–311.

    Article  Google Scholar 

  • Market Research Future (2021). Public Transport Market, https://www.marketresearchfuture.com, Access date: 04.11.2021.

  • Mateus, R., Ferreira, J. A., & Carreira, J. (2008). Multicriteria decision analysis (MCDA): Central Porto high-speed railway station. European Journal of Operational Research, 187(1), 1–18.

    Article  Google Scholar 

  • Mei, M., & Chen, Z. (2021). Evaluation and selection of sustainable hydrogen production technology with hybrid uncertain sustainability indicators based on rough-fuzzy BWM-DEA. Renewable Energy, 165. Part, 1, 716–730.

    Google Scholar 

  • Mohajeri, N., & Amin, G. R. (2010). Railway station site selection using analytical hierarchy process and data envelopment analysis. Computers & Industrial Engineering, 59(1), 107–114.

    Article  Google Scholar 

  • Nassereddine, M., & Eskandari, H. (2017). An integrated MCDM approach to evaluate public transportation systems in Tehran. Transportation Research Part a: Policy and Practice, 106, 427–439.

    Google Scholar 

  • Pamučar, D. Vasin, L. & Lukovac, L. (2014). Selection of railway level crossings for investing in security equipment using hybrid DEMATEL-MARICA model. XVI International Scientific-expert Conference on Railway, Railcon, 89–92.

  • Pérez, C. J., Carrillo, M. H., & Montoya-Torres, J. R. (2015). Multi-criteria approaches for urban passenger transport systems: A literature review. Annals of Operations Research, 226, 69–87.

    Article  Google Scholar 

  • Prakash, C., & Barua, M. K. (2015). Integration of AHP-TOPSIS method for prioritizing the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment. Journal of Manufacturing System, 37, 599–615.

    Article  Google Scholar 

  • Prakash, C., & Barua, M. K. (2016). A combined MCDM approach for evaluation and selection of third-party reverse logistics partner for Indian electronics industry. Sustainable Production and Consumption, 7, 66–78.

    Article  Google Scholar 

  • Rawat, A., & Garg, C. P. (2021). Assessment of the barriers of natural gas market development and implementation: A case of developing country. Energy Policy, 152, 112195.

    Article  Google Scholar 

  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.

    Article  Google Scholar 

  • Sabaei, D., Erkoyuncu, J., & Roy, R. (2015). Understanding the life cycle implications of manufacturing A review of multi-criteria decision-making methods for enhanced maintenance delivery. Procedia CIRP, 37, 30–35.

    Article  Google Scholar 

  • Simić, V., Ivanović, I., Đorić, V., & Torkayesh, A. E. (2022). Adapting urban transport planning to the COVID-19 pandemic: An integrated fermatean fuzzy model. Sustainable Cities and Society, 79, 103669.

    Article  Google Scholar 

  • Stanković, M., Stević, Ž, Das, D. K., Subotić, M., & Pamučar, D. (2020). A new fuzzy MARCOS method for road traffic risk analysis. Mathematics, 8(3), 457.

    Article  Google Scholar 

  • Trentesaux, D., Schon, W., Lussier, B., Dahyot, R., Ouedraogo, A., Arenas, D., Lefebvre, S. & Cheritel, H. (2018). The Autonomous Train. in 2018 13th Annual Conference on System of Systems Engineering (SoSE), 514–520.

  • Tsamboulas, D. A. (2007). A tool for prioritizing multinational transport infrastructure investments. Transport Policy, 14(1), 11–26.

    Article  Google Scholar 

  • Tudela, A., Akiki, N., & Cisternas, R. (2006). Comparing the output of cost benefit and multi-criteria analysis: An application to urban transport investments. Transportation Research Part a: Policy and Practice, 40(5), 414–423.

    Google Scholar 

  • Turcksin, L., Bernardini, A., & Macharis, C. (2011). A combined AHP-PROMETHEE approach for selecting the most appropriate policy scenario to stimulate a clean vehicle fleet. Procedia - Social and Behavioral Sciences, 20, 954–965.

    Article  Google Scholar 

  • Tzeng, G., Lin, C., & Opricovic, S. (2005). Multi-criteria analysis of alternative-fuel buses for public transportation. Energy Policy, 33(11), 1373–1383.

    Article  Google Scholar 

  • Vahdani, B., Zandieh, M., & Tavakkoli-Moghaddam, R. (2011). Two novel FMCDM methods for alternative-fuel buses selection. Applied Mathematical Modelling, 35(3), 1396–1412.

    Article  Google Scholar 

  • Yang, M., Wang, W., Chen, X., & Li, W. (2007). Mode Choice for the mass rapid transit system based on combined method of DEA and AHP. Journal of Highway and Transportation Research and Development, 2, 89–94.

    CAS  Google Scholar 

  • Yedla, S., & Shrestha, R. M. (2003). Multi-criteria approach for the selection of alternative options for environmentally sustainable transport system in Delhi. Transportation Research Part a: Policy and Practice, 37(8), 717–729.

    Google Scholar 

  • Yu, J., Liu, Y., Chang, G.-L., Ma, W., & Yang, X. (2011). Locating urban transit hubs: Multicriteria model and case study in China. Journal of Transportation Engineering, 137, 944–952.

    Article  Google Scholar 

  • Zapolskytė, S., Burinskienė, M., & Trépanier, M. (2020). Evaluation criteria of smart city mobility system using MCDM method. The Baltic Journal of Road and Bridge Engineering, 15(4), 196–224.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Engineering, XIM University, Bhubaneswar, India

    Pradip Kundu

  2. Department of International Logistics & Trade, Kadir Has University, Cibali Av. Kadir Has St. Fatih, Istanbul, 34083, Turkey

    Ömer Faruk Görçün

  3. Department of Operations Management, Indian Institute of Management Rohtak, Rohtak, Haryana, 124010, India

    Chandra Prakash Garg

  4. Department of Numerical Methods, Faculty of Economics and Administrative Sciences, Bartın University, Bartın, Turkey

    Hande Küçükönder

  5. Department of Accounting and Finance at Kadir Has University, Kadir Has University, Cibali Av. Kadir Has St. Fatih, Istanbul, 34083, Turkey

    Mustafa Çanakçıoğlu

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  1. Pradip Kundu
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Kundu, P., Görçün, Ö.F., Garg, C.P. et al. Evaluation of public transportation systems for sustainable cities using an integrated fuzzy multi-criteria group decision-making model. Environ Dev Sustain (2023). https://doi.org/10.1007/s10668-023-03776-y

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