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Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

PUBLISHED
20.12.2023
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Copyright (c) 2024 Adnan ABDULVAHİTOĞLU, Danışment VURAL, Aslı ABDULVAHİTOĞLU

Optimising Traffic Safety – Locating Traffic Gendarmes Based on Multi-Criteria Decision Making

Authors:Adnan ABDULVAHİTOĞLU, Danışment VURAL, Aslı ABDULVAHİTOĞLU

Abstract

Turkey’s expanding population and growing economy have resulted in a significant increase in automobile ownership, leading to a rise in traffic volume and, subsequently, an increase in the number of accidents. The increase in the number of deaths and injuries caused by traffic accidents has motivated authorities and automobile manufacturers to work together to mitigate the impact of traffic accidents. Therefore, the demand for better roads, modern technologies and higher-quality driver training is becoming increasingly urgent for traffic safety. Due to the scale of harm to the country’s economy and society caused by the material and moral losses, resulting from traffic accidents, traffic safety and management is one of the most important government initiatives. One of the responsible units of traffic safety and management in Turkey is the traffic gendarme. This study reviewed the categories of traffic accidents, the number of accidents, and the road network that occurred in a province's gendarme responsibility area in Turkey and linked them with the number of traffic gendarmes in that province. Thus, the study utilised mixed integer programming based on multi-criteria decision-making methods to identify the areas where these traffic gendarmes will be deployed according to established principles.

Keywords:traffic, traffic safety, traffic gendarmes, multi-criteria decision making, CRITIC, mixed integer programming

References

  1. [1] WHO. https://www.who.int/health-topics/road-safety [Accessed 17th Nov. 2022].
  2. [2] Sungur İ, Akdur R, Piyal B. Analysis of traffic accidents in Turkey [Türkiye’deki trafik kazalarının analizi]. Ankara Medical Journal. 2014;14(3):114-124. DOI: 10.17098/amj.65427.
  3. [3] Şenel B, Şenel M. Risk analysis: Fault tree analysis application on traffic accidents occurred in Türkiye [Risk analizi: türkiye’de gerçekleşen trafik kazaları üzerine hata ağacı analizi uygulaması]. Anadolu University Journal of Social Sciences [Anadolu Üniversitesi Sosyal Bilimler Dergisi]. 2013;13(3):65-84.
  4. [4] Arıkan ÖE. Traffic risk index of cities in Türkiye [Türkiye’deki illerin trafik risk endeksi]. Pamukkale University Journal of Engineering Sciences [Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi]. 2016;22(6):405-412. https://dergipark.org.tr/tr/pub/pajes/issue/26682/286307 [Accessed 17th Nov. 2022].
  5. [5] Traffic Statistics. Traffic Department of the General Directorate of Security. http://www.trafik.gov.tr/istatistikler37 [Accessed 27th Mar. 2023].
  6. [6] Road Traffic Accident Statistics. Turkish Statistical Institute. https://data.tuik.gov.tr/ [Accessed 27th Mar. 2023].
  7. [7] Türkiye Transportation and Communications Strategy 2011-2023. Ministry of Transport. https://www.sp.gov.tr/upload/xSPTemelBelge/files/Turkiye_Ulasim_veIletisim_Stratejisi.pdf. [Accessed 11th Nov. 2022].
  8. [8] Gökdağ M, Atalay A. Traffic education and effective on the traffic accidents [Trafik eğitiminin trafik kazaları üzerindeki etkisi]. Erzincan University Journal of Science and Technology [EÜFBED - Fen Bilimleri Enstitüsü Dergisi]. 2015;8(2):272-283. DOI: 10.18185/eufbed.45311.
  9. [9] Özpınar A, Kazasker E, Öz Ö. Intelligent traffic control and management system with RFID electronic vehicle identification [Akıllı trafik denetimi ve yönetimi için RFID ile elektronik plaka uygulaması]. XII. Academic Informatics Conference, 10-12 Feb. 2010, Muğla. https://ab.org.tr/ab10/kitap/_AB10_ikincicilt.pdf [Accessed 27th Mar. 2023].
  10. [10] Çinicioğlu EN, Atalay M, Yorulmaz H. Bayesian network model for analysis of traffic accidents [Trafik kazaları analizi için bayes ağları modeli]. Journal of Information Technologies. 2013;6(2):41-52. https://dergipark.org.tr/en/pub/gazibtd/issue/6628/88011.
  11. [11] Bayata HF, Hattatoğlu F. Neural networks and multivariate statistical methods in traffic accident modeling [Yapay sinir ağları ve çok değişkenli istatistik yöntemlerle trafik kaza modellemesi]. Erzincan University Journal of Science and Technology [EÜFBED - Fen Bilimleri Enstitüsü Dergisi]. 2010;3(2):207-219. https://dergipark.org.tr/tr/download/article-file/68394 [Accessed 27th Mar. 2023].
  12. [12] Yılmaz İ, et al. Geographical information systems aided traffic accident analysis system case study: City of Afyonkarahisar. Accident Analysis & Prevention 2008;40(1):174-181. DOI: 10.1016/j.aap.2007.05.004.
  13. [13] Abdulvahitoğlu A, Macit İ, Koyuncu M. Selecting the facility location of the gendarmerie station with an AHP-TOPSIS based mathematical model and analysis using GAS/GIS; A case study in a city [Jandarma Karakolu Kuruluş Yerinin AHP-TOPSIS Tabanlı Bir Matematiksel Model ile Seçimi ve CAS/CBS ile Analizi; Bir İlimizde Uygulama]. The Journal of Security Sciences [Güvenlik Bilimleri Dergisi]. 2021;10(2):305-338. DOI: 10.28956/gbd.1028022.
  14. [14] Abdulvahitoglu A, Kılıç M. A new approach for selecting the most suitable oilseed for biodiesel production; The integrated AHP-TOPSIS method. Ain Shams Engineering Journal. 2022;13(3):101604. DOI: 10.1016/j.asej.2021.10.002.
  15. [15] Abdulvahitoğlu A, Abdulvahitoğlu A, Kılıç M. Assessment of electric vehicle batteries via integrated SWARA-TOPSIS approach [Elektrikli araç bataryalarının bütünleşik SWARA-TOPSIS metodu ile değerlendirilmesi]. Cukurova University Journal of the Faculty of Engineering [Çukurova Üniversitesi Mühendislik Fakültesi Dergisi]. 2022;37(4):1061-1076. https://dergipark.org.tr/en/download/article-file/2878300.
  16. [16] Juodagalvienė B, Turskis Z, Šaparauskas J, Endriukaitytė A. Integrated multi-criteria evaluation of house’s plan shape based on the EDAS and SWARA methods. Engineering Structures and Technologies. 2017;9(3):117-125. DOI: 10.3846/2029882X.2017.1347528.
  17. [17] Karabašević D, Stanujkić D, Urošević S, Maksimović M. An approach to personnel selection based on SWARA and WASPAS methods. Journal of Economics, Management and Informatics. 2016;7(1):1-11. DOI: 10.5937/bizinfo1601001K.
  18. [18] Yücenur GN, İpekçi A. SWARA/WASPAS methods for a marine current energy plant location selection problem. Renewable Energy. 2021;163:1287-1298. DOI: 10.1016/j.renene.2020.08.131.
  19. [19] Tadić S, Krstić M, Kovač M, Brnjac N. Evaluation of smart city logistics solutions. Promet – Traffic&Transportation. 2022;34(5):725-738. DOI: 10.7307/ptt.v34i5.4122.
  20. [20] Zandi I, Pahlavani P. Spatial modeling and prioritization of potential areas for determining location of hospitals by a GIS-based multi-criteria decision-making analyses: A case study of the 5th district of Tehran. Town and Country Planning. 2021;13(1):247-280. DOI: 10.22059/JTCP.2021.313899.670175.
  21. [21] Wing S, et al. GRP and CRITIC method for probabilistic uncertain linguistic MAGDM and its application to site selection of hospital constructions. Soft Computing. 2022;26:237-251. DOI: 10.1007/s00500-021-06429-2.
  22. [22] Jati H, Wardani R. Visibility ranking of university E-learning websites-CRITIC method approach. Journal of Physics: Conference Series. 2021;1737(1):012030. DOI: 10.1088/1742-6596/1737/1/012030.
  23. [23] Uzun G, Kabak M. Determining the search and rescue prioritization of coast guard surface vessels by using analytic network process [Sahil güvenlik yüzer unsurlarının arama ve kurtarma önceliklerinin tanımlanmasında analitik ağ süreci kullanımı]. Journal of the Faculty of Engineering and Architecture of Gazi University [Gazi Üniversitesi Mimarlık ve Mühendislik Fakültesi Dergisi]. 2019;34(2):819-833. DOI: 10.17341/gazimmfd.460482.
  24. [24] Razi N, Karataş M. A Multi-Objective model for locating search and rescue boats. European Journal of Operation Research. 2016;254:279-273. DOI: 10.1016/j.ejor.2016.03.026.
  25. [25] Wang S, et al. Multi-flight rerouting optimisation based on typical flight paths under convective weather in the terminal area. Promet – Traffic&Transportation. 2022;34(6):907-926. DOI: 10.7307/ptt.v34i6.4195.
  26. [26] Karavidic Z, Projovic D. A Multi-Criteria Decision-Making model in the security forces operations based on rough sets. Decision Making: Applications in Management and Engineering. 2018;1(1):97-120. DOI: 10.31181/dmame180197k.
  27. [27] Wang Y, Li B, Zhao Z, Tang K. Optimal routing and charging of electric logistics vehicles based on long-distance transportation and dynamic transportation system. Promet – Traffic&Transportation. 2023;35(2):230-242. DOI: 10.7307/ptt.v35i2.54.
  28. [28] Köse E, Düzenli B, Çakmak S, Vural D. Medicine distribution problem between pharmacy warehouse and pharmacies. Sādhanā. 2022;47(171). DOI: 10.1007/s12046-022-01938-8.
  29. [29] Hakimi SL. Optimum location of switching centers and the absolute centers and medians of a graph. Operations Research. 1964;12(3):450-459. DOI: 10.1287/opre.12.3.450.
  30. [30] Diakoulaki D, Mavrotas G, Papayannakis L. Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research. 1995;22(7):763-770. DOI: 10.1016/0305-0548(94)00059-H.
  31. [31] Gao R, Nam HO, Ko WI, Jang H. National options for a sustainable nuclear energy system: MCDM evaluation using an improved integrated weighting approach. Energies. 2017;10(12):1-24. DOI: 10.3390/en10122017.
  32. [32] Işık Ö. Evaluation of financial performance of non-life insurance sector in Turkey by critic based TOPSIS and MULTIMOORA [Türkiye’de hayat dışı sigorta sektörünün finansal performansının CRITIC tabanlı TOPSIS ve MULTIMOORA yöntemiyle değerlendirilmesi]. Business & Management Studies: An International Journal. 2019;7(1):542-562. DOI: 10.15295/bmij.v7i1.1090.
  33. [33] Akgül Y. Analysis of financial performance of commercial banks traded in Istanbul stock exchange with the integrated CRITIC-CoCoSo model [Borsa istanbul’da işlem gören ticari bankaların finansal performansının bütünleşik CRITIC-CoCoSo modeliyle analizi]. Journal of Economics and Financial Researches. 2021;3(2):71-90. https://dergipark.org.tr/en/pub/jefr/issue/68029/1032234 [Accessed 27th Mar. 2023].
  34. [34] Ayçin E. Using CRITIC and MAIRCA methods in personnel selection process [Personel seçim sürecinde CRITIC ve MAIRCA yöntemlerinin kullanılması]. The Business Journal. 2020;1(1):1-12. https://dergipark.org.tr/en/download/article-file/1077980 [Accessed 27th Mar. 2023].
  35. [35] Klose A, Drexl A. Facility location models for distribution system design. European Journal of Operational Research. 2005;162(1):4-29. DOI: 10.1016/j.ejor.2003.10.031.
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