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BWM ve CoCoSo Yöntemleri ile Kentlerin Ulaşım Performanslarının Karşılaştırmalı Analizi

Yıl 2022, , 824 - 856, 02.08.2022
https://doi.org/10.31198/idealkent.1028556

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.

Destekleyen Kurum

Yok

Proje Numarası

Yok

Kaynakça

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

Yıl 2022, , 824 - 856, 02.08.2022
https://doi.org/10.31198/idealkent.1028556

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.

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.
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Toplam 86 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Ömer Faruk Görçün 0000-0003-3850-6755

Hande Küçükönder 0000-0002-0853-8185

Proje Numarası Yok
Yayımlanma Tarihi 2 Ağustos 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Görçün, Ö. F., & Küçükönder, H. (2022). BWM ve CoCoSo Yöntemleri ile Kentlerin Ulaşım Performanslarının Karşılaştırmalı Analizi. İDEALKENT, 13(36), 824-856. https://doi.org/10.31198/idealkent.1028556