BWM ve CoCoSo Yöntemleri ile Kentlerin Ulaşım Performanslarının Karşılaştırmalı Analizi

Yıl 2022, Cilt: 13 Sayı: 36, 824 - 856, 02.08.2022

Ömer Faruk Görçün , Hande Küçükönder

https://doi.org/10.31198/idealkent.1028556

Cited By: 4

Öz

Kentsel ulaşım sisteminin performansının ölçülmesi, mevcut ulaşım sistemlerinin iyileştirilmesi ve geliştirilmesi için çok kritik bir konudur. Bununla birlikte, bir toplu taşıma sistemi için performans analizi yapmak sürece ilişkin çok sayıda çelişkili kriter ve karmaşık durumların varlığı gözetildiğinde karar vericiler ve uygulayıcılar için kolay bir iş değildir. Öte yandan, mevcut literatürde toplu taşıma sistemini değerlendirmek için yaygın olarak kabul edilen belirlenmiş bir kriter seti bulunmamaktadır. Dolayısıyla bu durum değerlendirme ve analiz süreçlerini çok daha zor bir hale getirmektedir. Bu çalışmada kentsel raylı ulaşım sistemlerinin performanslarını değerlendirmek üzere hibrit bir karar verme modeli önerilmektedir. Önerilen model, Best and Worst Method (BWM) ve Combined Compromise Solution (CoCoSo) tekniklerinin entegrasyonuna dayanmaktadır. BWM tekniği ile karar vericilerin öznel değerlendirmelerindeki en iyi ve en kötü tercihleri öne çıkarılarak kriter ağırlıkları belirlenmekte, CoCoSo tekniği ile karar alternatifleri performans düzeylerine göre sıralanmaktadır. Bu model, Avrupa’da metro hatlarına sahip 30 kentin raylı ulaşım performanslarını dokuz kriter ile değerlendirmek için uygulanmıştır. Çalışma sonucunda en yüksek performans düzeyine sahip olan ilk sıradaki kentin Saint Petersburg olduğu belirlenmiştir. Ayrıca yapılan duyarlılık analizi sonucunda önerilen modelin güvenilir ve tutarlı sonuçlar sergilediği, bu tür performans değerlendirme süreçlerinde uygun bir karar desteği sağlayabileceği tespit edilmiştir.

Anahtar Kelimeler

Ulaşım Sistemleri, CoCoSo, Performans analizi, BWM

Destekleyen Kurum

Yok

Proje Numarası

Yok

Comparative Performance Analysis for the Cities with the BWM and the CoCoSo Techniques

Öz

Measuring the performances of the urban transport systems is a critical issue in improving and developing the existing transport systems. In the meantime, making performance analysis for the public transport system is not easy for practitioners and decision-makers, as there are many conflicting criteria and very complicated situations in the evaluation process. In addition, there are no commonly accepted criteria set in the existing literature to assess the public transport systems. Hence, this situation makes it difficult to evaluate and analyze processes much more. The proposed model based on the integration of the Best and Worst Method (BWM) and Combined Compromise Solution (CoCoSo) techniques. While the criteria weights are identified by highlighting the worst and the best criterion with the BWM, decision alternatives are ranked with the CoCoSo technique. This model was implemented to evaluated urban rail systems performances of 30 European cities having metro rail systems. At the end of the study, it has been determined that the first ranked city having highest performance is Saint Petersburg. Besides, as a result of the sensitivity analysis, the proposed model provides reliable and consistent results and it has been observed that it can provide a proper decision support for these kinds of evaluation processes.

Anahtar Kelimeler

Transport Systems, CoCoSo, Performance Analysis, BWM

Proje Numarası

Yok

Kaynakça

• Referans1 Ahern, A., & Anandarajah, G. (2007). Railway projects prioritisation for investment: Application of goal programming. Transport Policy, 14(1), 70–80.
• Referans2 Ambrasaite, I., Barfod, M., & Salling, K. (2011). MCDA and risk analysis in transport infrastructure appraisals: The rail baltica case. Procedia Social and Behavioral Sciences, 20, 944–953.
• Referans3 Amoroso S., Salvo G. & Zito P. (2011). Sustainable urban public transport. A comparison between European and north African cities. Managing Sustainability? Proceedings of the 12th Management International Conference, 2011 Portorož, Slovenia, 23–26 November 2011.
• Referans4 Awasthi, A., & Chauhan, S. S. (2011a). Using AHP and dempster shafer theory for evaluating sustainable transport solutions. Environmental Modelling & Software, 26, 787–796.
• Referans5 Awasthi, A., Chauhan, S., & Omrani, H. (2011b). Application of fuzzy TOPSIS in evaluating sustainable transportation systems. Expert Systems with Applications, 38(10), 12270–12280.
• Referans6 Aydin, N., Celik, E. & Gumus, A.T. (2015). A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul. Transport Research A Policy Practice, 77:61–81.
• Referans7 Barfod, M. B. (2012). An MCDA approach for the selection of bike projects based on structuring and appraising activities. European Journal of Operational Research, 218(3), 810–818.
• Referans7 Basbas, S., Pitsiava-Latinopoulou, M., & Zacharaki, E. (2009). Motorized road transport: Economic and environmental costs—a policy assessment framework. International Journal of Sustainable Development and Planning, 4(4), 309–321.
• Referans8 Beukes, E. A., Vanderschuren, M. J. W. A., & Zuidgeest, M. H. P. (2011). Context sensitive multimodal road planning: A case study in Cape Town, South Africa. Journal of Transport Geography, 19, 452–460.
• Referans9 Beuthe, M., Eeckhoudt, L., & Scannella, G. (2000). A practical multicriteria methodology for assessing risky public investments. Socio-Economic Planning Sciences, 34(2), 121–139.
• Referans10 Bielli, M. (1992). A DSS approach to urban traffic management. European Journal of Operational Research, 61(1–2), 106–113.
• Referans11 Bilgiç, S., Torğul, B., & Paksoy, T. (2021). Sürdürülebilir Enerji Yönetimi İçin BWM Yöntemi İle Yenilenebilir Enerji Kaynaklarının Değerlendirilmesi, Verimlilik Dergisi, Sayı: 2, 95-110.
• Referans12 Bouwman, M. E., & Moll, H. C. (2002). Environmental analyses of land transportation systems in The Netherlands. Transportation Research Part D, 7(5), 331–345.
• Referans13 Brey, J. J., Contreras, I., Carazo, A. F., Brey, R., Hernández-Díaz, A. G., & Castro, A. (2007). Evaluation of automobiles with alternative fuels utilizing multicriteria techniques. Journal of Power Sources, 169(1), 213–219.
• Referans14 Brucker, K., Verbeke, A., & Macharis, C. (2004). The applicability multicriteria-analysis to the evaluation of intelligent transport dystems (ITS). Economic impacts of intelligent transportation systems: Innovations and case studies. Research in Transportation Economics, 8, 151–179.
• Referans15 Caliskan, N. (2006). A decision support approach for the evaluation of transport investment alternatives. European Journal of Operational Research, 175(3), 1696–1704.
• Referans16 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.
• Referans17 Cyril, A., Mulangi, R. H. & Varghese, G. (2019). Performance Optimization of Public Transport Using Integrated AHP–GP Methodology. Urban Rail Transit 5 (2), 133–144.
• Referans18 Çakır, E,& Can, M. (2019). Best-Worst Yöntemine Dayalı ARAS Yöntemi ile Dış Kaynak Kullanım Tercihinin Belirlenmesi: Turizm Sektöründe Bir Uygulama. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 23 (3) , 1273-1300.
• Referans19 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.
• Referans20 Çanakçıoğlu, M. & Görçün, Ö. F. (2019). Evaluation of public transport systems in aspects of external costs by using an integrated MCDM model. International Congress of Energy, Economy, and Security, Istanbul, 2019.
• Referans21 Çiftçi, H. N., & Yıldırım, B. F. (2020). BİST enerji sektöründe faaliyet gösteren işletmelerin finansal performanslarının incelenmesi: Gri Sayılara dayalı Zaman Kesiti örneği. Muhasebe Bilim Dünyası Dergisi, 22(3), 384-404.
• Referans22 Demir, G., & Bircan, H. (2020). Kriter Ağırlıklandırma yöntemlerinden BWM ve FUCOM Yöntemlerinin Karşılaştırılması ve Bir Uygulama. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(2), 170–185.
• Referans23 Deveci, M., Pamucar, D. and Gokasar, I. (2021). Fuzzy Power Heronian function based CoCoSo method for the advantage prioritization of autonomous vehicles in real-time traffic management. Sustainable Cities and Society, 69, 102846.
• Referans24 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.
• Referans25 Ecer, F., & Pamucar, D. (2020). Sustainable Supplier Selection: A Novel Integrated Fuzzy Best Worst Method (F-BWM) and Fuzzy CoCoSo with Bonferroni (CoCoSo’B) Multi-Criteria Model. Journal of Cleaner Production, 266, 1-18.
• Referans26 Ekbatani, M.K. & Cats, O. (2015). Multi-criteria appraisal of multi-modal urban public transport systems, 18th Euro working group on transportation, Delft, The Netherlands, 1-11.
• Referans27 Ellis, J. B., Deutsch, J.-C., Mouchel, J.-M., Scholes, L., & Revitt, M. D. (2004). Multicriteria decision approaches to support sustainable drainage options for the treatment of highway and urban runoff. Science of the Total Environment, 334–335, 251–260.
• Referans28 Emberger, G., Pfaffenbichler, P., Jaensirisak, S., & Timms, P. (2008). “Ideal” decision-making processes for transport planning: A comparison between Europe and South East Asia. Transport Policy, 15(6), 341–349.
• Referans29 Estache, A., Guasch, J.-L., Iimi, A., & Trujillo, L. (2009). Multidimensionality and renegotiation: Evidence from transport-sector public–private-partnership transactions in Latin America. Review of Industrial Organization, 35(1–2), 41–71.
• Referans30 Ferrari, P. (2003). A method for choosing from among alternative transportation projects. European Journal of Operational Research, 150(1), 194–203.
• Referans31 Fioravanti, R.D., Amâncio, M.A., Galves, M.L. (2007). Alternatives to reduce congestion and improve the road system using a multicriteria decision analysis: A case study in the city of Campinas, Brazil. In: Brebbia C.A. (Ed.), “Urban transport XIII: Urban transport and the environment in the twentyfirst Century”, WIT Transactions on the Built Environment, 96, 63–73.
• Referans32 Giuliano, G. A. (1985). Multicriteria method for transportation investment planning. Transportation Research Part A: General, 19(1), 29–41.
• Referans33 Görçün, Ö. F. (2019). İstanbul Kentinde Silivri - Sabiha Gökçen Havalimanı Arası Ulaşım Alternatiflerinin AHP TOPSIS Yöntemleri ile Analizi. İdealkent, 10 (27), 800-823.
• Referans34 Görçün, Ö. F. (2021). Evaluation of the selection of proper metro and tram vehicle for urban transportation by using a novel integrated MCDM approach. Science Progress, 104 (1), 1-18.
• Referans35 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.
• Referans36 He, J., & Hung, W. (2012). Perception of policy-makers on policy-making criteria: The case of vehicle emissions control. Science of the Total Environment, 417–418, 21–31.
• Referans37 Hickman, R., Saxena, S., Banister, D., & Ashiru, O. (2012). Examining transport futures with scenario analysis and MCA. Transportation Research Part A: Policy and Practice, 46(3), 560–575.
• Referans38 Iniestra, J., & García, J. (2009). Multicriteria decisions on interdependent infrastructure transportation projects using an evolutionary-based framework. Journal of Applied Soft Computing, 9(2), 512–526.
• Referans39 Ivanovic´, I., Grujicˇic´, D., Macura, D., Jovic´, J., & Bojovic´, N. (2013). One approach for road transport project selection. Transport Policy, 25, 22–29.
• Referans40 Kavran, Z., Štefancˇic´, G., & Presecˇki, A. (2007). Multicriteria analysis and public transport management. WIT Transactions on the Built Environment, 96, 85–90.
• Referans41 Khasnabis, S., Alsaidi, E., Liu, L., & Ellis, R. D. (2002). Comparative study of two techniques of transit performance assessment: AHP and GAT. Journal of Transportation Engineering, 128(6), 499–508.
• Referans42 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.
• Referans43 Labbouz, S., Roy, B., & Diab, Y. (2008). Implementing a public transport line: Multi-criteria decision-making methods that facilitate concertation. Operational Research International Journal, 8(1), 5–31.
• Referans44 Lai, H., Liao, H., Wen, Z., Zavadskas, E.K. & Al-Barakati, A. (2020). An improved CoCoSo method with a maximum variance optimization model for cloud service provider selection. Economics of Engineering Decisions, 31, 411–424.
• Referans45 Levine, J., & Underwood, S. (1996). A multiattribute analysis of goals for intelligent transportation system planning. Transportation Research Part C: Emerging Technologies, 4(2), 97–111.
• Referans46 Macharis, C., De Witte, A., & Turcksin, L. (2010). The multi-actor multi-criteria analysis (MAMCA) applica- tion in the Flemish long-term decision-making process on mobility and logistics. Transport Policy, 17(5), 303–311.
• Referans47 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.
• Referans48 Milakis, D., & Athanasopoulos, K. (2014). What about people in cycle network planning? Applying participa- tive multicriteria GIS analysis in the case of the Athens metropolitan cycle network. Journal of Transport Geography, 35, 120–129.
• Referans49 Moslem, S. (2020). Analyzing public involvement in urban transport decision making by MCDM methodology. Budapest University of Technology and Economics Faculty of Transportation Engineering and Vehicle Engineering, Department of Transport Technology and Economics, Thesis for Degree of Doctor of Philosophy.
• Referans50 Mufazzal,S. Muzakkir,S.M. (2018) A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals, Computers & Industrial Engineering,Volume 119,2018,Pages 427-438,ISSN 0360-8352,https://doi.org/10.1016/j.cie.2018.03.045.
• Referans51 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.
• Referans52 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.
• Referans53 Özdağoğlu, A., Ulutaş, A. and Keleş, M. K. (2020). The ranking of Turkish universities with COCOSO and MARCOS. Economics Business and Organization Research, 374-392.
• Referans54 Panou, K. D., & Sofianos, A. I. (2002). A fuzzy multicriteria evaluation system for the assessment of tunnels vis-à-vis surface roads: The WPMA case—part II. Tunnelling and Underground Space Technology, 17(2), 209–219.
• Referans55 Peng, X., Zhang, X. & Luo, Z. (2020). Pythagorean fuzzy MCDM method based on CoCoSo and CRITIC with score function for 5G industry evaluation. Artificial Intelligence Review, 53, 3813–3847.
• Referans56 Poh, K. L., & Ang, B. W. (1999). Transportation fuels and policy for Singapore: An AHP planning approach. Computers & Industrial Engineering, 37(3), 507–525.
• Referans57 Pressl, B., Mader, C. & Wieser, M. (2010). User-specific web-based route planning. In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A. (Eds.), Computers helping people with special needs, lecture notes in computer science, 6179, 280–287.
• Referans58 Raju,K.S., & Kumar, D.N. (1999). Multicriterion decision making in irrigation planning, Agric. Syst. 62 (1999) 117–129, http://dx.doi.org/10.1016/S0308-521X(99) 00060-8
• Referans59 Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
• Referans60 Rodrigues da Silva, A. N., da Silva Costa, M., & Macedo, M. H. (2008). Multiple views of sustainable urban mobility: The case of Brazil. Transport Policy, 15(6), 350–360.
• Referans61 Sayers, T. M., Jessop, A. T., & Hills, P. J. (2003). Multi-criteria evaluation of transport options - flexible, transparent and user-friendly? Transport Policy, 10(2), 95–105.
• Referans62 Scarpellini, S., Valero, A., Llera, E., & Aranda, A. (2013). Multicriteria analysis for the assessment of energy innovations in the transport sector. Energy, 57, 160–168.
• Referans63 Stević, Ž., Pamučar, D., Puška, A. & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231.
• Referans64 Qureshi, I. A. & Lu, H. (2007). Urban transport and sustainable transport strategies: A case study of Karachi, Pakistan. Tsinghua Science and Technology, 12(3), 309-317.
• Referans65 Thomopoulos, N., & Grant-Muller, S. (2013). Incorporating equity as part of the wider impacts in transport infrastructure assessment: An application of the SUMINI approach. Transportation, 40(2), 315–345.
• Referans66 Topal, A. (2021), Çok Kriterli Karar Verme analizi ile elektrik üretim şirketlerinin finansal performans analizi: entropi tabanlı Cocoso yöntemi, Business & Management Studies: An International Journal, 9(2), ss. 532-546.
• Referans67 Tsamboulas, D. A. (2007). A tool for prioritizing multinational transport infrastructure investments. Transport Policy, 14(1), 11–26.
• Referans68 Tsamboulas, D., & Mikroudis, G. (2000). EFECT - evaluation framework of environmental impacts and costs of transport initiatives. Transportation Research Part D: Transport and Environment, 5(4), 283–303.
• Referans69 Tsamboulas, D., Yiotis, G. S., & Panou, K. D. (1999). Use of multicriteria methods for assessment of transport projects. Journal of Transportation Engineering, 125(5), 407–414.
• Referans70 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.
• Referans71 Turcksin, L., Bernardini, A., & Macharis, C. (2011a). 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.
• Referans72 Turcksin, L., Macharis, C., Lebeau, K., Boureima, F., Van Mierlo, J., Bram, S., et al. (2011b). A multi- actor multi-criteria framework to assess the stakeholder support for different biofuel options: The case of Belgium. Energy Policy, 39(1), 200–214.
• Referans73 Tzeng, G., Lin, C., & Opricovic, S. (2005). Multi-criteria analysis of alternative-fuel buses for public trans- portation. Energy Policy, 33(11), 1373–1383.
• Referans74 Ulutaş, A., Karakuş, C. B. and Topal, A. (2020). Location selection for logistics center with fuzzy SWARA and CoCoSo methods. Journal of Intelligent & Fuzzy Systems, 38(4), 4693-4709.
• Referans75 Ülengin, F., & Topcu, Y. I. (2000). 41-Knowledge-based decision support systems techniques and their application in transportation planning systems. Knowledge-Based Systems, 4, 1403–1429.
• Referans76 Vahdani, B., Zandieh, M., & Tavakkoli-Moghaddam, R. (2011). Two novel FMCDM methods for alternative- fuel buses selection. Applied Mathematical Modelling, 35(3), 1396–1412.
• Referans77 Vitosoglu, Y., Ozden, R., Yaliniz, P. & Bilgic, S. (2014). Comparison of light rail systems in Turkey with the method of comparative standard determination. Transportation Research Procedia, 3, 670-679.
• Referans78 Wen, Z., Liao, H., Kazimieras Zavadskas, E. & Al-Barakati, A. (2019). Selection third-party logistics service providers in supply chain finance by a hesitant fuzzy linguistic combined compromise solution method. Economic research - Ekonomska istraživanja, 32(1), 4033-4058.
• Referans79 Wey, W.-M., & Wu, K.-Y. (2007). Using ANP priorities with goal programming in resource allocation in transportation. Mathematical and Computer Modelling, 46(7–8), 985–1000.
• Referans80 Yazdani, M., Zarate, P., Zavadskas, E., & Turskis, Z. (2018). A Combined Compromise Solution (CoCoSo) Method for Multi-Criteria Decision-Making Problems. Management Decision, 1-19.
• Referans81 Yazdani, M., Zarate, P., Zavadskas, E. K. & Turskis, Z. (2019). ‘A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems’, Management Decision, 57(9), pp. 2501-2519.
• Referans82 Yedla, A., & 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.
• Referans83 Yeh, C.-H., Deng, H., & Chang, Y.-H. (2000). Fuzzy multicriteria analysis for performance evaluation of bus companies. European Journal of Operational Research, 126(3), 459–473.
• Referans84 Zavadskas, E.K., Turskis, Z., Antucheviciene, J., Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika. 122(6), 3-6.
• Referans85 Zuidgeest, M., Brussel, M., & van Maarseveen, M. (2013). Quantifying the contextual influences on road design. Computer-Aided Civil and Infrastructure Engineering, 28(5), 344–358.