Sürdürülebilir Kent Hareketliliği Performans Değerlendirmesi İçin Yeni Bir Çok Kriterli Karar Verme Yaklaşımı: MEREC-RAWEC Uygulaması

Öz

Dünya genelinde her geçen yıl artan kent nüfusu, kentsel hareketliliği giderek daha karmaşık hale getirmektedir. Kent hareketliliği, çok sayıda faktörle ilişkili karmaşık bir yapıya sahip olup toplumun yaşam kalitesi üzerinde önemli bir etkiye sahiptir. Bu çalışmanın temel amacı, kent hareketliliğine yalnızca ulaşım açısından değil aynı zamanda sürdürülebilirlik çerçevesinden bakarak, kentlerin performansını objektif biçimde belirlenmiş kriter ağırlıklarıyla ölçmek ve bu kentleri sıralamaktır. Bu bağlamda, MEREC-RAWEC yaklaşımı önerilmiştir. ISO 37120 standartlarının öngördüğü sürdürülebilirlik boyutlarıyla uyumlu altı kriter ve yedi alternatif üzerinden yapılan analizlerde Sürdürülebilir Hareketlilik Hazırlık Endeksi verileri kullanılmış ve MEREC yöntemiyle sürdürülebilir kent hareketliliği açısından en önemli kriterin 0,2848 ağırlık değeriyle Finans, en az önemli kriterin ise 0,0700 ağırlık değeriyle Ulaşım arzı olduğu belirlenmiştir. RAWEC yöntemiyle yapılan sıralama sonuçları ise en yüksek performansa sahip kentin Buenos Aires, en düşük performansa sahip kentin ise Cape Town olduğu belirlenmiştir. Yöntemin kriter ağırlıklarındaki değişime hassasiyetini incelemek için dört senaryo altında duyarlılık analizi yapılmış ve sonuçların genel olarak tutarlı olduğu ortaya konmuştur. Sonuçların güvenilirliği test etmek amacıyla MABAC, EDAS, CODAS ve MAIRCA yöntemleri ile karşılaştırmalı analiz gerçekleştirilmiştir. MABAC ve MAIRCA ile aynı sonuçların, EDAS ve CODAS ile ise %89 düzeyinde korelasyonla uyumlu sonuçların elde edildiği ortaya konmuş ve yöntemin bulguları doğrulanmıştır. Son olarak, yaşanabilir ve sürdürülebilir kentlerin oluşturulması yönünde kent yönetimlerine ve karar vericilere yönelik politika önerileri sunulmuştur.

Anahtar Kelimeler

• Sürdürülebilirlik
• Kent hareketliliği
• MEREC
• RAWEC
• Performans değerlendirme

A Novel Multi Criteria Decision Making Approach for Sustainable Urban Mobility Performance Evaluation: An Application of MEREC-RAWEC

Öz

The continuous growth of urban populations worldwide has made urban mobility increasingly complex. As a multifaceted phenomenon shaped by diverse factors, urban mobility plays a critical role in shaping the quality of life. This study aims to evaluate urban mobility not only from a transportation perspective but also within a broader sustainability framework by assessing city performance through objectively determined criterion weights and ranking cities accordingly. For this purpose, the MEREC-RAWEC approach is proposed. The analysis was conducted using six criteria and seven alternatives aligned with the sustainability dimensions outlined in the ISO 37120 standard, drawing on data from the Sustainable Mobility Readiness Index. The MEREC results indicate that Finance (0.2848) is the most important criterion in sustainable urban mobility, while Transport Supply (0.0700) is the least significant. Using these weights, the RAWEC method ranked Buenos Aires as the highest-performing city, whereas Cape Town ranked the lowest. Sensitivity analysis under four scenarios showed that the rankings were largely stable with respect to variations in weights. To ensure robustness, the results were compared with those of the MABAC, EDAS, CODAS, and MAIRCA methods. The findings revealed full consistency with MABAC and MAIRCA and a strong correlation (89%) with EDAS and CODAS, confirming the reliability of the approach. Finally, the study offers policy recommendations for city administrations and decision-makers to foster the development of more livable and sustainable urban environments.

Anahtar Kelimeler

• Sustainability
• Urban mobility
• MEREC
• RAWEC
• Performance evaluation

Kaynakça

1. Abdelaal, R. M. S., Makki, A. A., Al-Madi, E. M., & Qhadi, A. M. (2024). Prioritizing strategic objectives and projects in higher education institutions: A new hybrid fuzzy MEREC-G-TOPSIS approach. IEEE Access, 12, 89735–89753. https://doi.org/10.1109/ACCESS.2024.3419701
2. Ağaoğlu, M. N., Korkmaz, F., & Alakara, E. H. (2021). Sürdürülebilir ulaşım ve bisiklet yollarının planlanması: Sivas Cumhuriyet Üniversitesi yerleşkesi örneği. Gaziosmanpaşa Bilim Araştırmaları Dergisi, 10(2), 140–155.
3. Akbulut, O. Y., & Aydın, Y. (2024). A hybrid multidimensional performance measurement model using the MSD-MPSI-RAWEC model for Turkish banks. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 11(3), 1157–1183. https://doi.org/10.30798/makuiibf.1464469
4. Akpınar, M. E. (2025). Evaluating resilience and sustainability in global supply chains: Multi-criteria decision-making approach for post-pandemic challenges. Logforum, 21(1), 63–72. https://doi.org/10.17270/J.LOG.001141
5. Akram, M., Zahid, S., & Al-Kenani, A. N. (2024). Multi-criteria group decision-making for evaluating efficient and smart mobility sharing systems using Pythagorean fuzzy rough numbers. Granular Computing, 9(2), 50. https://doi.org/10.1007/s41066-024-00466-6
6. Andrade, N. A., & Sawicka, H. (2025). Methodology for park-and-ride facility location using the catchment area method and a multiple criteria decision-aiding approach. Logforum, 21(2), 197–209. https://doi.org/10.17270/J.LOG.001185
7. Aquilué Junyent, I., Martí Casanovas, M., Roukouni, A., Moreno Sanz, J., Roca Blanch, E., & Correia, G. H. A. (2024). Planning shared mobility hubs in European cities: A methodological framework using MCDA and GIS applied to Barcelona. Sustainable Cities and Society, 106, 105377. https://doi.org/10.1016/j.scs.2024.105377
8. Aydin, N., Seker, S., & Özkan, B. (2022). Planning location of mobility hub for sustainable urban mobility. Sustainable Cities and Society, 81, 103843. https://doi.org/10.1016/j.scs.2022.103843

9. Baba Slimane, N. E. H., & Baouni, T. (2021). Interaction between the transport network and the territory of Algiers (complex system): In search of indicators. Journal of Applied Engineering Science, 11(2), 77–92. https://doi.org/10.2478/jaes-2021-0011
10. Bebber, S., Libardi, B., De Atayde Moschen, S., Correa da Silva, M. B., Cristina Fachinelli, A., & Nogueira, M. L. (2021). Sustainable mobility scale: A contribution for sustainability assessment systems in urban mobility. Clean Engineering and Technology, 5, 100271. https://doi.org/10.1016/j.clet.2021.100271
11. Brand, C., Götschi, T., Dons, E., Gerike, R., Anaya-Boig, E., Avila-Palencia, I., ... Nieuwenhuijsen, M. J. (2021). The climate change mitigation impacts of active travel: Evidence from a longitudinal panel study in seven European cities. Global Environmental Change, 67, 102224. https://doi.org/10.1016/j.gloenvcha.2021.102224
12. Ceballos, B., Lamata, M. T., & Pelta, D. A. (2016). A comparative analysis of multi-criteria decision-making methods. Progress in Artificial Intelligence, 5, 315–322.
13. Chatterjee, K., & Kar, S. (2018). Supplier selection in telecom supply chain management: A fuzzy-Rasch based COPRAS-G method. Technological and Economic Development of Economy, 24(2), 765–791. https://doi.org/10.3846/20294913.2017.1295289
14. Commission of the European Communities. (2009). Action plan on urban mobility (COM(2009) 490 final). Brussels. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2009:0490:FIN:EN:PDF (Accessed June 18, 2025)
15. Çelebi Demirarslan, P., Sönmez Çakır, F., & Akansel, I. (2024). Ranking the quality of life indexes by years in Asian countries using multi-criteria decision-making methods. Asia-Pacific Journal of Regional Science, 8(3), 911–942. https://doi.org/10.1007/s41685-024-00350-w
16. da Silva, A. N. R., Tan, F. M., & de Sousa, P. B. (2024). Key sustainable mobility indicators for university campuses. Environmental and Sustainability Indicators, 22, 100371. https://doi.org/10.1016/j.indic.2024.100371
17. Damidavičius, J., Burinskienė, M., & Antuchevičienė, J. (2020). Assessing sustainable mobility measures applying multicriteria decision-making methods. Sustainability, 12(15), 6067. https://doi.org/10.3390/su12156067
18. Demir, G., & Chatterjee, P. (2025). A fuzzy multi-criteria decision-making approach to personalised treatment in neuroscience. Brain: Broad Research in Artificial Intelligence and Neuroscience, 16(2), 311. https://doi.org/10.70594/brain/16.2/23
19. Distefano, N., & Leonardi, S. (2023). Fostering urban walking: Strategies focused on pedestrian satisfaction. Sustainability, 15(24), 16649. https://doi.org/10.3390/su152416649
20. Durmuş, Z. (2025). Assessment of renewable energy performance in Turkey using a novel integrated MSD-CRITIC-RAWEC model. Journal of Operational and Strategic Analytics, 3(1), 49–64. https://doi.org/10.56578/josa030105
21. Dündar, S. (2025). Performance evaluation of IPARD-II rural development programs with integrated DIBR-RAWEC methods. Pamukkale University Journal of Engineering Sciences, 31(3), 339–350. https://doi.org/10.5505/pajes.2024.43996
22. Dündar, S., & Karadağ, İ. (2025). Selection of a facility location for a cosmetics company by integrated F-LBWA and I-RAWEC methods. International Journal of Fuzzy Systems, 1–20. https://doi.org/10.1007/s40815-024-01960-4
23. Ertuğrul, M., & Özdarak, E. (2025). Measuring airline performance: An integrated balanced scorecard-based MEREC-CoCoSo model. Sustainability, 17(13), 5826. https://doi.org/10.3390/su17135826
24. Esangbedo, M. O., & Tang, M. (2023). Evaluation of enterprise decarbonization scheme based on grey-MEREC-MAIRCA hybrid MCDM method. Systems, 11(8), 397. https://doi.org/10.3390/systems11080397
25. European Commission. (2013). Together towards competitive and resource-efficient urban mobility (COM(2013) 913 final). Brussels. https://eur-lex.europa.eu/resource.html?uri=cellar:82155e82-67ca-11e3-a7e4-01aa75ed71a1.0011.02/DOC_3&format=PDF (Accessed June 18, 2025)
26. European Commission. (2019). The European Green Deal (COM(2019) 640 final). Brussels. https://eur-lex.europa.eu/resource.html?uri=cellar:b828d165-1c22-11ea-8c1f-01aa75ed71a1.0002.02/DOC_1&format=PDF (Accessed June 26, 2025)
27. Galanakis, K., Heinz, H., & Marggraf, C. (2024). Place-based sustainable urban mobility: A conceptual framework to spark local designs. Regional Studies, 58(12), 2419–2434. https://doi.org/10.1080/00343404.2024.2406290
28. Gamal, A., Abdel-Basset, M., Hezam, I. M., Sallam, K. M., & Hameed, I. A. (2023). An interactive multi-criteria decision-making approach for autonomous vehicles and distributed resources based on logistic systems: Challenges for a sustainable future. Sustainability, 15(17), 12844. https://doi.org/10.3390/su151712844
29. Garau, C., Masala, F., & Pinna, F. (2016). Cagliari and smart urban mobility: Analysis and comparison. Cities, 56, 35–46. https://doi.org/10.1016/j.cities.2016.02.012
30. Hajduk, S. (2022). Multi-criteria analysis in the decision-making approach for the linear ordering of urban transport based on TOPSIS technique. Energies, 15(1), 274. https://doi.org/10.3390/en15010274
31. Herrera-Acevedo, D. D., & Sierra-Porta, D. (2025). Network structure and urban mobility sustainability: A topological analysis of cities from the urban mobility readiness index. Sustainable Cities and Society, 119, 106076. https://doi.org/10.1016/j.scs.2024.106076
32. Jain, D., & Tiwari, G. (2017). Sustainable mobility indicators for Indian cities: Selection methodology and application. Ecological Indicators, 79, 310–322. https://doi.org/10.1016/j.ecolind.2017.03.059
33. Jasti, P. C., & Vinayaka Ram, V. (2019). Integrated and sustainable benchmarking of metro rail system using analytic hierarchy process and fuzzy logic: A case study of Mumbai. Urban Rail Transit, 5(3), 155–171. https://doi.org/10.1007/s40864-019-00107-1
34. Katrancı, A., Kundakcı, N., & Arman, K. (2026). Fuzzy SIWEC and fuzzy RAWEC methods for sustainable waste disposal technology selection. Spectrum of Operations Research, 3(1), 87–102. https://doi.org/10.31181/sor31202633
35. 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(3), 435–451. https://doi.org/10.15388/Informatica.2015.57
36. 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 and Economic Cybernetics Studies and Research, 50(3), 25–44.
37. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525. https://doi.org/10.3390/sym13040525
38. Korkmazyürek, B., & Polat, E. (2019). Kentsel ulaşımda esnek, akıllı ve yeni bir planlama yaklaşımı: Sürdürülebilir kentsel hareketlilik planları. Kent Akademisi, 12(2), 225–240. https://doi.org/10.35674/kent.537224
39. Kramar, U., Dragan, D., & Topolšek, D. (2019). The holistic approach to urban mobility planning with a modified focus group, SWOT, and fuzzy analytical hierarchical process. Sustainability, 11(23), 6599. https://doi.org/10.3390/su11236599
40. Kundu, P., Görçün, Ö. F., Garg, C. P., Küçükönder, H., & Çanakçıoğlu, M. (2023). Evaluation of public transportation systems for sustainable cities using an integrated fuzzy multi-criteria group decision-making model. Environment, Development and Sustainability, 26(11), 27655–27684. https://doi.org/10.1007/s10668-023-03776-y
41. Melkonyan, A., Gruchmann, T., Lohmar, F., & Bleischwitz, R. (2022). Decision support for sustainable urban mobility: A case study of the Rhine-Ruhr area. Sustainable Cities and Society, 80, 103806. https://doi.org/10.1016/j.scs.2022.103806
42. Moradi, M., Delavar, M. R., & Moshiri, B. (2017). A GIS-based multi-criteria analysis model for earthquake vulnerability assessment using Choquet integral and game theory. Natural Hazards, 87, 1377–1398. https://doi.org/10.1007/s11069-017-2822-6
43. Morfoulaki, M., & Papathanasiou, J. (2021). Use of PROMETHEE MCDA method for ranking alternative measures of sustainable urban mobility planning. Mathematics, 9(6), 602. https://doi.org/10.3390/math9060602
44. Moslem, S. (2024). A novel parsimonious spherical fuzzy analytic hierarchy process for sustainable urban transport solutions. Engineering Applications of Artificial Intelligence, 128, 107447. https://doi.org/10.1016/j.engappai.2023.107447
45. Mutavdžija, M., Kovačić, M., & Buntak, K. (2024). Moving towards sustainable mobility: A comparative analysis of smart urban mobility in Croatian cities. Sustainability, 16(5), 2004. https://doi.org/10.3390/su16052004
46. Narayanamoorthy, S., Parthasarathy, T. N., Pragathi, S., Shanmugam, P., Baleanu, D., Ahmadian, A., & Kang, D. (2022). The novel augmented Fermatean MCDM perspectives for identifying the optimal renewable energy power plant location. Sustainable Energy Technologies and Assessments, 53, 102488. https://doi.org/10.1016/j.seta.2022.102488
47. Olteanu (Burcă), A. L., Ionașcu, A. E., Cosma, S., Barbu, C. A., Popa, A., Cioroiu, C. G., & Goswami, S. S. (2024). Prioritizing the European investment sectors using a fuzzy-MEREC-AROMAN decision-making model. Sustainability, 16(17), 7790. https://doi.org/10.3390/su16177790
48. Ozdemir, S., Demirel, N., Zaralı, F., & Çelik, T. (2024). Multi-criteria assessment framework for evaluation of Green Deal performance. Environmental Science and Pollution Research, 31(3), 4686–4704. https://doi.org/10.1007/s11356-023-31370-2
49. Önder, H. G., & Akdemir, F. (2022). Sürdürülebilir ulaşım altyapısının pandemi döneminde yeniden kurgulanması: Mikromobilite trendleri ve Türkiye. İdealkent, 13(36), 748–770. https://doi.org/10.31198/idealkent.1039996
50. Özekenci, E. K. (2025). A multi-criteria framework for economic decision support in urban sustainability: Comparative insights from European cities. International Journal of Economic Sciences, 14(1), 162–181. https://doi.org/10.31181/ijes1412025188
51. Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016–3028. https://doi.org/10.1016/j.eswa.2014.11.057
52. Pamučar, D., Vasin, L., & Lukovac, V. (2014). Selection of railway level crossings for investing in security equipment using hybrid DEMATEL-MARIC model. In XVI International Scientific-expert Conference on Railways (pp. 89–92). Niš, Serbia. https://doi.org/10.13140/2.1.2707.6807
53. Pelit, İ., & Avşar, İ. İ. (2025). Turkiye’s carbon emission profile: A global analysis with the MEREC-PROMETHEE hybrid method. Sustainability, 17(14), 6527. https://doi.org/10.3390/su17146527
54. Puška, A., Nedeljković, M., Dudić, B., Štilić, A., & Mittelman, A. (2024). Improving agricultural sustainability in Bosnia and Herzegovina through renewable energy integration. Economies, 12(8), 195. https://doi.org/10.3390/ECONOMIES12080195
55. Puška, A., Štilić, A., Pamučar, D., Božanić, D., & Nedeljković, M. (2024). Introducing a novel multi-criteria ranking of alternatives with weights of criterion (RAWEC) model. MethodsX, 12, 102628. https://doi.org/10.1016/j.mex.2024.102628
56. PwC, & MaasLab. (2024). The sustainable mobility readiness index. https://www.pwc.com/m1/en/publications/sustainable-mobility-readiness-index.html (Accessed May 30, 2025)
57. PwC. (2024). Sustainable mobility readiness index. https://www.pwc.com/m1/en/publications/sustainable-mobility-readiness-index.html (Accessed September 8, 2025)
58. Radović, D., Stević, Ž., Pamučar, D., Zavadskas, E. K., Badi, I., Antuchevičiene, J., & Turskis, Z. (2018). Measuring performance in transportation companies in developing countries: A novel rough ARAS model. Symmetry, 10(10), 434. https://doi.org/10.3390/sym10100434
59. Sałabun, W., Palczewski, K., & Wątróbski, J. (2019). Multicriteria approach to sustainable transport evaluation under incomplete knowledge: Electric bikes case study. Sustainability, 11(12), 3314. https://doi.org/10.3390/su11123314
60. Santos, T., Cardoso, M., Vieira da Silva, M. A., & Fernandes, V. A. (2024). Assessing mobility resilience and vulnerability under challenging transportation fare policies: Rio de Janeiro case study. Urbe – Revista Brasileira de Gestão Urbana, 16, e20230236. https://doi.org/10.1590/2175-3369.016.e20230236
61. Senne, C. M., Lima, J. P., & Favaretto, F. (2021). An index for the sustainability of integrated urban transport and logistics: The case study of São Paulo. Sustainability, 13(21), 12116. https://doi.org/10.3390/su132112116
62. Shamsi, M., Zakerinejad, M., & Zareifard, M. R. (2025). Optimal, reliable, and sustainable technology selection for mining overburden waste utilization using green and climate-smart mining (GCSM). Journal of Environmental Chemical Engineering, 13(3), 116118. https://doi.org/10.1016/j.jece.2025.116118
63. Su, J., Liu, H., Chen, Y., Zhang, N., & Li, J. (2025). A novel multi-criteria decision making method to evaluate green innovation ecosystem resilience. Engineering Applications of Artificial Intelligence, 139, 109528. https://doi.org/10.1016/j.engappai.2024.109528
64. Suna Gider, K., & Koç, C. (2022). Sürdürülebilir ulaşım sistemi kapsamında bisiklet yollarının değerlendirilmesi: Diyarbakır örneği. Gaziosmanpaşa Bilim Araştırmaları Dergisi, 11(3), 122–136.
65. Tursun, A. (2025). Sürdürülebilir ulaşım sistemlerinde sistem dinamiği yaklaşımı ve karmaşık etkileşimlerin incelenmesi. Kent Akademisi, 18(3), 1195–1208. https://doi.org/10.35674/kent.1539983
66. United Nations Human Settlements Programme (UN-Habitat). (2025). UN-Habitat Annual Report 2024. Nairobi, Kenya. www.unhabitat.org/annual-report-2024 (Accessed July 11, 2025)
67. World Bank Group. (2025). Urban population (% of total population). https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS (Accessed July 22, 2025)
68. Yildirim, B., Ayyildiz, E., & Aydin, N. (2024). Optimal location selection for electric vehicle car-sharing stations using Fermatean fuzzy decision-making methodology. Journal of Cleaner Production, 485, 144400. https://doi.org/10.1016/j.jclepro.2024.144400
69. Yurdakul, M., & İç, Y. T. (2009). Application of correlation test to criteria selection for multi criteria decision making (MCDM) models. International Journal of Advanced Manufacturing Technology, 40, 403–412.