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Çevresel Sürdürülebilirlik Perspektifinden Şehir İçi Akıllı Ulaşım Sistemlerinin TOPSIS Analizi ile Değerlendirilmesi

Year 2025, Volume: 8 Issue: 1, 1 - 14, 30.04.2025
https://doi.org/10.38002/tuad.1496774

Abstract

Bu çalışma, çevresel sürdürülebilirlik perspektifinden akıllı ulaşım sistemlerinin performanslarını değerlendirmek amacıyla TOPSIS (İdeal Çözüme Benzerlik ile Tercih Sıralama Tekniği) yöntemini kullanmıştır. Elektrikli otobüs sistemi (EOS), paylaşımlı elektrikli skuter sistemi (PESS), otonom araç paylaşım sistemi (OAPS) ve akıllı bisiklet paylaşım sistemi (ABPS) olmak üzere dört farklı ulaşım sistemi analiz edilmiştir. Analiz; karbon emisyonlarının azaltılması, enerji verimliliği, kaynak kullanımı, hava kalitesine etki ve yenilenebilir enerji kullanımı kriterleri doğrultusunda gerçekleştirilmiştir. Analiz sonuçlarına göre, EOS en yüksek göreceli yakınlık değeriyle en iyi performansı sergilemiştir. EOS, karbon emisyonlarının azaltılması ve enerji verimliliği konularında üstün performans göstermekte ve fosil yakıt kullanımını azaltarak kaynak kullanımını optimize etmektedir. ABPS ikinci sırada yer alarak çevre dostu bir alternatif olarak öne çıkmaktadır. OAPS üçüncü sırada yer almakta olup, enerji verimliliği ve kaynak kullanımı açısından diğer sistemlere göre daha düşük performans sergilemektedir. PESS ise en düşük performansı göstermiştir. Bulgular, şehir plancıları ve politika yapıcılar için önemli bilgiler sunmakta olup, akıllı ulaşım sistemlerinin çevresel sürdürülebilirlik hedefleri doğrultusunda en uygun seçeneklerin belirlenmesine katkı sağlamaktadır.

Ethical Statement

İlgili çalışmada insan veya hayvan katılımcılardan veri toplanmadığı için etik kurul izni gerekmemektedir.

References

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Evaluation of Urban Intelligent Transportation Systems from an Environmental Sustainability Perspective Using the TOPSIS Method

Year 2025, Volume: 8 Issue: 1, 1 - 14, 30.04.2025
https://doi.org/10.38002/tuad.1496774

Abstract

This study uses the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to evaluate the performance of intelligent transportation systems from an environmental sustainability perspective. Four different transportation systems were analyzed: electric bus system (EBS), shared electric scooter system (SESS), autonomous vehicle sharing system (AVSS) and intelligent bicycle sharing system (IBSS). The analysis was carried out in line with the criteria of carbon emission reduction, energy efficiency, resource utilization, impact on air quality and use of renewable energy. According to the analysis results, the EBS performed the best with the highest relative proximity value. EBS shows superior performance in terms of carbon emission reduction and energy efficiency, and optimizes resource use by reducing fossil fuel use. IBSS ranked second and stands out as an environmentally friendly alternative. The AVSS ranks third and performs lower than the other systems in terms of energy efficiency and resource utilization. The SESS performed the lowest. The findings provide important information for urban planners and policy makers and contribute to the identification of the most appropriate options in line with the environmental sustainability goals of intelligent transportation systems.

Ethical Statement

İlgili çalışmada insan veya hayvan katılımcılardan veri toplanmadığı için etik kurul izni gerekmemektedir.

References

  • Akande, N. O., Arulogun, O. T., Ganiyu, R. A. ve Adeyemo, I. A. (2018). Improving the quality of service in public road transportation using real time travel information system. World Review of Intermodal Transportation Research, 7(1), 57-79. https://doi.org/10.1504/WRITR.2018.089529
  • Aslan, H. M., Yıldız, M. S. ve Uysal, H. T. (2015). Application of fuzzy TOPSIS method on location selection of disaster stations: A location analysis in Düzce. Journal of Politics, Economics and Management Research, 3(2), 111-128.
  • Auer, A., Feese, S., Lockwood, S. ve Hamilton, B. A. (2016). History of intelligent transportation systems. United States Department of Transportation. https://rosap.ntl.bts.gov/view/dot/30826
  • Barth, M. J., Wu, G. ve Boriboonsomsin, K. (2015). Intelligent transportation systems and greenhouse gas reductions. Current Sustainable/Renewable Energy Reports, 2(3), 90-97. https://doi.org/10.1007/s40518-015-0032-y
  • Behzadian, M., Otaghsara, S. K., Yazdani, M. ve Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051-13069. https://doi.org/10.1016/j.eswa.2012.05.056
  • Bhaskar, A. S., Khan, A. ve Patre, S. R. (2021). Application potential of fuzzy embedded TOPSIS approach to solve MCDM based problems. Intelligent manufacturing içinde (ss. 99-121), Springer. https://doi.org/10.1007/978-3-030-50312-3_5
  • Brakewood, C. ve Watkins, K. (2019). A literature review of the passenger benefits of real-time transit information. Transport Reviews, 39(3), 327-356. https://doi.org/10.1080/01441647.2018.1472147
  • Brennand, C. A., Filho, G. P. R., Maia, G., Cunha, F., Guidoni, D. L. ve Villas, L. A. (2019). Towards a fog-enabled intelligent transportation system to reduce traffic jam. Sensors, 19(18), 3916, 1-29. https://doi.org/10.3390/s19183916
  • Browne, M., Allen, J. ve Leonardi, J. (2012). Evaluating the use of an urban consolidation centre and electric vehicles in central London. IATSS Research, 35(1), 1-6. https://doi.org/10.1016/j.iatssr.2011.06.002
  • Camodeca, R. ve Almici, A. (2021). Digital transformation and convergence toward the 2030 agenda’s sustainability development goals: Evidence from Italian listed firms. Sustainability, 13(21), 11831-11849. https://doi.org/10.3390/su132111831
  • Carter, C. R. ve Rogers, D. S. (2008). A framework of sustainable supply chain management: Moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360-387. https://doi.org/10.1108/09600030810882816
  • Chandrappa, S., Guruprasad, M. S., Kumar, H. N., Raju, K. ve Kumar, D. S. (2023). An IoT-based automotive and intelligent toll gate using RFID. SN Computer Science, 4(2), 154, 1-15. https://doi.org/10.1007/s42979-022-01569-0
  • Chen, S. J. ve Hwang, C. L. (1992). Fuzzy multiple attribute decision making: Methods and applications. Springer-Verlag.
  • Chen, F., Yang, B., Zhang, W., Ma, J., Lv, J. ve Yang, Y. (2017). Enhanced recycling network for spent e-bicycle batteries: A case study in Xuzhou, China. Waste Management, 60, 660-665. https://doi.org/10.1016/j.wasman.2016.09.027
  • Cheng, Z., Pang, M. S. ve Pavlou, P. A. (2020). Mitigating traffic congestion: The role of intelligent transportation systems. Information Systems Research, 31(3), 653-674. https://doi.org/10.1287/isre.2019.0894
  • Cho, S., Shrestha, B., Salah, B., Ullah, I. ve Salem, N. M. (2022). A proposed waiting time algorithm for a prediction and prevention system of traffic accidents using smart sensors. Electronics, 11(11), 1765, 1-19. https://doi.org/10.3390/electronics11111765
  • Crawford, D. (2021). Benefits of ITS. Road Network Operations Intelligent Transport Systems. https://rno-its.piarc.org/en/its-basics/benefits-its
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There are 73 citations in total.

Details

Primary Language Turkish
Subjects Transport Planning
Journal Section Research Article
Authors

Rukiye Gizem Öztaş Karlı 0000-0003-0999-418X

Publication Date April 30, 2025
Submission Date June 7, 2024
Acceptance Date November 5, 2024
Published in Issue Year 2025 Volume: 8 Issue: 1

Cite

APA Öztaş Karlı, R. G. (2025). Çevresel Sürdürülebilirlik Perspektifinden Şehir İçi Akıllı Ulaşım Sistemlerinin TOPSIS Analizi ile Değerlendirilmesi. Trafik Ve Ulaşım Araştırmaları Dergisi, 8(1), 1-14. https://doi.org/10.38002/tuad.1496774

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