Trafik Birimlerinin Lokasyonlara Optimal Dağılımı İçin Bir Matematiksel Model Önerisi: CRITIC Tabanlı Eğri Tahminleri (CBCE)

Abstract

Trafik denetim birimlerinin varlığı, trafik kazalarının azaltılmasında önemli bir faktör olarak kabul edilmektedir. Bu nedenle, trafik birimlerinin optimal dağılımı, kaynakların verimli kullanımı, kazaların azaltılması ve yol güvenliğinin artırılması açısından kritik bir öneme sahiptir. Bu çalışmada, bölgeler arasında trafik birimlerinin optimal dağılımını sağlamak için CRITIC Tabanlı Eğri Tahmini (CBCE) adı verilen matematiksel bir yöntem önerilmiştir. Yöntemin uygulanabilirliğini sağlamak amacıyla, trafik kazalarını etkileyen faktörler (bağımsız değişkenler) trafik uzmanlarının görüşleri ve literatür taraması doğrultusunda belirlenmiştir. Çalışmada bölgelerin trafik ile ilgili sayısal değerleri hipotetik örneklemlere dayanmaktadır. Bu faktörlere ait nicel değerler iki bölge için atanmış ve bir karar matrisi oluşturulmuştur. Belirlenen faktörlerin ağırlıkları CRITIC yöntemi kullanılarak hesaplanmış ve bu faktörlerin trafik kazaları üzerindeki etkileri Eğri Tahmini yöntemi ile analiz edilmiştir. Bu analizlerin sonucunda, bölgeler için optimal trafik birimi tahsis oranları belirlenmiştir. Ampirik bulgular, önerilen yöntemin duyarlılık analizlerinde ideal sonuçlar verdiğini, karşılaştırmalı analizlerde kararlı ve güvenilir olduğunu ve simülasyon analizlerinde sağlam ve tutarlı çıktılar sunduğunu göstermektedir. Çalışmada önerilen yöntemin hem kamu hem de özel sektör tarafından trafik stratejilerinin geliştirilmesi açısından faydalı olacağı düşünülmektedir.

Keywords

• Trafik birimleri
• CRITIC
• CRITIC Tabanlı Eğri Tahmini
• Optimal Dağılım Oranı

A Mathematical Model Proposal for The Optimal Distribution of Traffic Units to Locations: CRITIC-Based Curve Estimations (CBCE)

Abstract

The presence of traffic enforcement units is widely recognized as a critical factor in reducing traffic accidents. Therefore, the optimal allocation of these units holds substantial importance for ensuring the efficient use of resources, minimizing accident rates, and enhancing overall road safety. In this study, a mathematical method called the CRITIC-Based Curve Estimation (CBCE) approach is proposed to achieve the optimal distribution of traffic units across regions. To ensure the applicability of the proposed model, the factors influencing traffic accidents (independent variables) were identified based on both expert opinions and an extensive review of the relevant literature. The numerical data representing regional traffic characteristics were constructed using hypothetical samples. Quantitative values for these factors were assigned for two regions, and a decision matrix was developed accordingly. The weights of the identified factors were calculated using the CRITIC method, while their effects on traffic accidents were analyzed through the Curve Estimation technique. As a result of these analyses, optimal allocation ratios for traffic enforcement units across regions were determined. Empirical findings indicate that the proposed method yields ideal results in sensitivity analyses, demonstrates stability and reliability in comparative evaluations, and produces robust and consistent outputs in simulation-based tests. The proposed approach is expected to provide valuable insights for both the public and private sectors in developing effective traffic management and safety strategies.

Keywords

• Traffic units
• CRITIC
• CRITIC-based Curve Estimation
• Optimal Distribution Ratio

References

1. Abdallah, F. D., & Gouda, H. F. (2024). Comparison of curve estimation regression methods in predicting protein amount from total milk yield in Holstein dairy cattle Assiut Veterinary Medical Journal, 70(181), 156-165.
2. Abdullah, A. G., Nurhikam, Y., Hakim, D. L., Sugito, N. T., & Zakaria, D. (2024). CRITIC-TOPSIS Method: Design of hybrid renewable energy systems based on multi-criteria decision-making. International Journal of Electrical and Computer Engineering Systems, 15(8), 705-718.
3. Abuzinadah, N., Aljrees, T., Chen, X., Umer, M., Aboulola, I. O., Tahir, S., . . . Ashraf, I. (2024). Improving Traffic Accident Severity Prediction Using Convoluted Features and Decision-Level Fusion of Models. Transportation Research Record: Journal of the Transportation Research Board, 2678(8), s. 731-744. https://doi.org/10.1177/03611981231220.
4. Adler, N., Hakkert, A. S., Raviv, T., & Sher, M. (2014). The Traffic Police Location and Schedule Assignment Problem. Journal of Decision Decision Making Journal, 21(5-6), 315-333. https://doi.org/10.1002/mcda.1522.
5. Anjum, M., Simic, V., Min, H., & Kraiem, N. (2024). Transformative Pathways to Metaverse Integration in Intelligent Transportation Systems Using Pythagorean Fuzzy CRITIC-AROMAN Method. IEEE, 15, 119383-119404. https://doi.org/10.1109/ACCESS.2024.3449417.
6. Arshad, M. W., Setiawansyah, M., Suryono, R. R., & Rahmanto, Y. (2024). Combination of CRITIC Weighting Method and Multi-Atributive Ideal-Real Comparative Analysis in Staff Admissions. Journal of Computer Science and Information Technology, 4(2), 77-86.
7. Arslan, R. (2020). CRITIC Yöntemi . H. Bircan içinde, Çok Kriterli Karar Verme Problemlerinde Kriter Ağırlıklandırma Yöntemleri (s. 117-134). Ankara: Nobel Akademik Yayıncılık.
8. Ayçin, E. (2019). Çok Kriterli Karar Verme . Ankara: Nobel Yayın.

9. Baghdadi, Z. D., & Pani, S. C. (2012). Accuracy of population-specific Demirjian curves in the estimation of dental age of Saudi children. International Journal of Paediatric Dentistry, 22, 125-131. https://doi.org/10.1111/j.1365-263X.2011.01179.x.
10. Beenstock, M., Gafni, D., & Goldin, E. (2001). The effect of traffic policing on road safety in Israel. Accident Analysis & Prevention, 33(1), 73-80. https://doi.org/10.1016/S0001-4575(00)00017-8.
11. Berhanu, Y., Alemayehu, E., & Schröder, D. (2023). Examining Car Accident Prediction Techniques and Road Traffic Congestion: A Comparative Analysis of Road Safety and Prevention of World Challenges in Low-Income and High-Income Countries. Journal of Advanced Transportation, 2023, 1-18. https://doi.org/10.1155/2023/6643412.
12. Bradford, B., Hohl, K., & MacQueen, S. (2015). Obeying the Rules of the Road: Procedural Justice, Social Identity, and Normative Compliance. Journal of Contemporary Criminal Justice, 31(2), 171-191. https://doi.org/10.1177/1043986214568833.
13. Bucsuházy, K., Matuchová, E., Mikulec, R., Moravcová, P., Kostíková, M., & Mikulec, R. (2020). Human factors contributing to the road traffic accident occurrence. Transportation Research Procedia, 45, 555–561. https://doi.org/10.1016/j.trpro.2020.03.057.
14. Castro, P., Ibitoye, R., Ellmers, T., Kaski, D., Arshad, Q., & Bronstein, A. M. (2024). Towards an explanation for ‘unexplained’ dizziness in older people. Age and Ageing, 53, 1-8. https://doi.org/10.1093/ageing/afae137.
15. Chhillar, N., Arora, S., & Chawla, P. (2024). Measuring Digital Financial Literacy: Scale Development and Validation. Thailand and The World Economy, 42(1), 110-145.
16. Corp., I. (2013). IBM SPSS Statistics for Windows: SPSS Tutorial-Curve Estimation. New York: IBM Corp.
17. Das, U., & Behera, B. (2024). Geospatial assessment of ecological vulnerability of fragile Eastern Duars Forest integrating GIS-based AHP, CRITIC and AHP-TOPSIS models. Geomatics, Natural Hazards and Risk, 15(1), 1-25. https://doi.org/10.1080/19475705.2024.2330529.
18. Dausan, A. F., & Barican, L. B. (2024). Traffic Management and Accident Investigation with Defensive Driving. Wiseman's Books Trading, Inc: Manila.
19. Dekker, S. W. (2002). Reconstructing human contributions to accidents: The new view on error and performance. Journal of Safety Research, 33(1), 371-385. https://doi.org/10.1016/S0022-4375(02)00032-4.
20. Diakoulaki, D., Mavrotas, G., & Papayannakis , L. (1995). Determining Objective Weights in Multiple Criteria Problems: The Critic Method. Computers & Operations Research, 22(7), 763-770.
21. Dong, G., Jia, H., Dong, X., & Xia, H. (2020). Analysis of Influencing Factors in Traffic Accidents of Road Passenger Transport Vehicles. IOP Conf. Series: Earth and Environmental Science, 587, 1-7. https://doi.org/10.1088/1755-1315/587/1/012011.
22. Ecer, F. (2020). Çok Kriterli Karar Verme. Ankara: Seçkin Yayıncılık.
23. Efromovich, S. (1999). Nonparametric Curve Estimation: Methods, Theory, and Applications. New York: Springer.
24. Elvik, R., Pasnin, L. T., & Nævestad, T.-O. (2022). Effects on accidents of police checks of drivers of heavy goods vehicles in Norway. Transportation Research Interdisciplinary Perspectives, 14, 1-5. https://doi.org/10.1016/j.trip.2022.100606.
25. Ergin, H., & Güler, H. (2024). The Impact of Traffic Fines on Traffic Accidents in Türkİye. 2nd International Congress of Finance and Tax. Konya.
26. Eygü, H. (2018). Investigation of Factors Affecting Traffic Accidents by Structural Equation Modelling. Electronic Journal of Social Sciences, 17(66), 838-851. https://doi.org/10.17755/esosder.391299.
27. Garson, G. D. (2012). Curve Fitting & Nonlinear Regression. Statistical Associates Publishers.
28. Gorzelanczyk, P., & Piątkowski, P. (2024). Evaluation of factors affecting the number of traffic accidents. Technical Sciences, 27(27), 87–103. https://doi.org/10.31648/ts.10043.
29. Gupta, A. (2017). Fundamentals of Traffic Engineering Design. New Delhi: Random Publications.
30. Hall, A., Lovejoy-Williams, K., Lewis, N., Jorge, A. G., & Bourne, P. A. (2024). International Journal of Public Finance, Law & Taxation, 8(1), 36-78.
31. Hossain, M. E., & Zaman, M. U. (2021). Analyzing the Factors Influencing Road Traffic Accident Severity: A Case Study of Khulna City. Plan Plus, 11, 143-164. https://doi.org/10.54470/planplus.v11i1.10.
32. Hu, L., Bao, X., Wu, H., & Wu, W. (2020). A Study on Correlation of Traffic Accident Tendency with Driver Characters Using In-Depth Traffic Accident Data. Journal of Advanced Transportation, 2020, 1-8. https://doi.org/10.1155/2020/9084245.
33. Jafri, M., Shaikh, A., & Shaikh, M. (2011). Estimation Of The Production Of Matiari Sugar Mill Sindh Using SPSS Through Cobb-Douglas Model. Sindh University Research Journal (Science Series), 43(2), 127-132.
34. Jasvi, M. A., Ullah, Z., & Kamran, M. (2024). Role of Traffic Police Officer in Road Safety Management. International Research Journal of Arabic and Islamic Studies, 4(1), 101-108.
35. Jomnonkwao, S., Uttra, S., & Ratanavaraha, V. (2020). Sustainability, 12, 1-17. https://doi.org/10.3390/su12010395.
36. Karagöz, Y. (2016). SPSS ve AMOS Uygulamalı Nitel-Nicel Karma Bilimsel Araştırma Yöntemler ve Yayın Etiği (1-638 b.). Ankara, Türkiye: Nobel Akademik Yayıncılık.
37. Karagöz, Y. (2017). SPSS ve AMOS 23 Uygulamalı İstatistiksel Analizler (1 b.). Ankara, Türkiye: Nobel Akademik Yayıncılık.
38. Karagöz, Y. (2019). SPSS - AMOS - META Uygulamalı İstatistiksel Analizler. Ankara, Türkiye: Nobel Akademik Yayıncılık..
39. 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(525), 1-21. https://doi.org/10.3390/sym13040525.
40. Khan, A. A., Mashat, D. S., & Dong, K. (2024). Evaluating Sustainable Urban Development Strategies through Spherical CRITIC-WASPAS Analysis. Journal of Urban Development and Management, 3(1), 1-17. https://doi.org/10.56578/judm030101.
41. Kılıç, B. N., & Asal, Ö. (2024). Multi-Criteria Decision-Making Approaches in Determining Optimal Traffic Control Strategies. Kent Akademisi Dergisi, 17(2), 551-567. https://doi.org/10.35674/kent.1416395.
42. Kockott, S. R. (2021). Road Traffic Safety. Claremont: Juta.
43. Kumar, C. P. (2023). A Guide to Road Safety and Accident Prevention. India: Independently published.
44. Li, X., Liu, Y., Chen, X., Reichetzeder, C., Elitok, S., Krämer, B. K., & Hocher, B. (2024). Target Values for 25-Hydroxy and 1,25-Dihydroxy Vitamin D Based on Their Associations with Inflammation and Calcium-Phosphate Metabolism. Nutrients, 16, 1-16. https://doi.org/10.3390/nu16162679.
45. Lu, Y., & Gao, H. (2021). Analysis of Fuel Consumption in Urban Road Congestion Based on SPSS Statistical Software. International Conference on Cognitive-based Information Processing and Applications (CIPA 2021): In: Lecture Notes on Data Engineering and Communications Technologies (Volume 84, 113–123). Singapore: Springer.
46. Mandhare, P., Yadav, J., Kharat, V., & Patil, C. Y. (2021). Control and Coordination of Self-Adaptive Traffic Signal Using Deep Reinforcement Learning. International Journal of Next-Generation Computing, 12(2), 190–199. https://doi.org/10.47164/ijngc.v12i2.207.
47. Marti-Puig, P., Hernández, J. Á., Solé-Casals, J., & Serra-Serra, M. (2014). Enhancing Reliability in Wind Turbine Power Curve Estimation. Appl. Sci., 14, 1-16. https://doi.org/10.3390/app14062479.
48. Meduri, K., Nadella, G. S., Gonaygunta, H., & Meduri, S. S. (2023). Developing a Fog Computing-based AI Framework for Real-time Traffic Management and Optimization. International Journal of Sustainable Development, 5(4), 1-24.
49. Mirjalili, S., Lork, A., & Nejad, B. A. (2024). Risk Assessment in Construction Projects Portfolio Management by Critic MCDM method. Journal of Structural and Construction Engineering, 10(12), 5-17. https://doi.org/10.22065/jsce.2023.357970.3030.
50. Modipa, M. (2022). Analysing factors contributing to road traffic accidents in South Africa. International Journal Of Research in Business And Social Science, 11(4), 439-447. https://doi.org/10.20525/ijrbs.v11i4.1715.
51. Nguyen-Phuoc, D. Q., Mai, N. X., Kim, I., & Oviedo-Trespalacios, O. (2024). Questioning penalties and road safety Policies: Are they enough to deter risky motorcyclist Behavior? Accident Analysis & Prevention, 207, 1-15. https://doi.org/10.1016/j.aap.2024.107756.
52. Ortiz, K., & Bennett, S. (2018). Traffic Safety. London: Westminster Publishing.
53. Öztel, A., & Alp, İ. (2020). Çok Kriterli Karar Verme Seçiminde Yeni Bir Yaklaşım. İstanbul: Kriter Yayıncılık.
54. Pajic, V., Andrejic, M., & Poledica, M. (2024). A Novel Approach Based on CRITIC-MOOSRA Methods for Evaluation and Selection of Cold Chain Monitoring Devices. Journal of Intelligent Management Decision, 3(2), 68-76. https://doi.org/10.56578/jimd030201.
55. Prothmann, H., Tomforde, S., Branke, J., Hähner, J., Müller-Schloer, C., & Schmeck, H. (2011). Organic Traffic Control. C. S. Müller-Schloer içinde, Organic Computing A Paradigm Shift for Complex Systems. Autonomic Systems (pp. 431–446). Basel: Springer.
56. Rahimi, H., Nazari, S. S., Soori, H., Motevalian, S. A., Momeni, E., & Azar, A. (2017). Traffic Police Effectiveness and Efficiency Evaluations, An Overview of Methodological Considerations. Arch Trauma Res., 6(1), 1-12. https://doi.org/10.5812/atr.36927.
57. Rathod, R., Joshi, G., & Shriniwas, A. (2024). Evaluation of Spatiotemporal Transit Accessibility: Weighted Indexing Using the CRITIC-MCDM Approach and Performance Gap Analysis. Journal of Advanced Transportation, 2024, 1-22. https://doi.org/10.1155/2024/6343594.
58. Rivers, R. W. (2011). Traffic Crash Investigators' Manual: A Level 1 and 2 Reference, Training and Investigation Manual. Springfield: Charles C Thomas Pub Ltd.
59. Rolison, J. J. (2020). Identifying the causes of road traffic collisions: Using police officers’ expertise to improve the reporting of contributory factors data. Accident Analysis and Prevention, 135, 1-8. https://doi.org/10.1016/j.aap.2019.105390.
60. Safari, M., Alizadeh, S. S., Bazargani, H. S., Aliashrafi, A., Maleki, A., Moshashaei, P., & Shakerkhatibi, M. (2019). Comprehensive Review on Risk Factors Affecting the Crash Severity. Iranian Journal of Health, Safety & Environment, 6(4), 1366-1376.
61. Sam, E. F. (2022). How effective are police road presence and enforcement in a developing country context? Humanities and social sciences communications, 9(55), 1-11. https://doi.org/10.1057/s41599-022-01071-1.
62. Schoeters, A., Wijnen, W., Carnis, L., Weijermars, W., Elvik, R., Daniels, S., & Johanssen, H. (2020). Costs Related to Serious Road Injuries: A European Perspective. European Transport Research Review (ETRR), 12(58), 1-12. https://doi.org/10.1186/s12544-020-00448-0.
63. Schakel, W., Tamminga, G., Knoppers, P., & Verbraeck, A. (2016). Getting the human factor into traffic flow models: A new open-source design to simulate next-generation traffic operations. Journal of the Transportation Research Board, 2561(1), 25- https://doi.org/10.3141/2561-04.
64. Shinar, D. (2017). Traffic Safety and Human Behavior. Leeds: Emerald Publishing Limited.
65. Sidha, Z., Asingo, P., & Magutu, J. (2021). Street Level Bureaucrats as Policy Entrepreneurs: The Nexus between Timing of Traffic Enforcement Activities and Road Safety Policy Outcomes. Politics & Policy, 49(1), 186-204. https://doi.org/10.1111/polp.12386.
66. Sinha, A., Singh, S. K., & Singh, V. L. (2021). Linkage Between Road Safety and Economic Development: A Case Study of India. Metamorphosis, 20(1), 7–15. https://doi.org/10.1177/09726225211030829.
67. Siri, S., Pasquale, C., Sacone , S., & Ferrara, A. (2021). Freeway traffic control: A survey. Automatica, 130, 1-10. https://doi.org/10.1016/j.automatica.2021.109655.
68. Sum, Y., & Ortiz, J. (2024). Machine Learning-Driven Pedestrian Recognition and Behavior Prediction for Enhancing Public Safety in Smart Cities. Journal of Artificial Intelligence and Information, 1, 51–57.
69. Sümer, N., & Kaygısız, Ö. (2014). Trafİk Denetimlerinin Kazalara Etkisi. Ankara: Uzerler Matbacılık Sanayi.
70. Sümer, N., & Kaygısız, Ö. (2015). Türkiye’de denetleme, cezalar ve trafik güvenliği göstergeleri arasındaki ilişkiler: 2008-2012 yılları analizi. Turk J Public Health, 13(3), 193-205.
71. Syah, B. M. (2024). Traffic Police Management to support Security, Safety, Order and Smoothness (Kamseltibcar Lantas) in DKI Jakarta Province, Indonesia. Syntax Admiration, 5(5), 1764-1773.
72. Tarter, M. E., & Lock, M. D. (1993). Model Free Curve Estimation. Florida: Chapman and Hall/CRC.
73. Tercan, E., & Beşdok, E. (2018). Evaluation of Relations Between Factors Affecting Traffic. Iğdır Univ. J. Inst. Sci. & Tech., 8(1), 103-111. https://doi.org/10.21597/jist.407848.
74. Theo, W. L., Lim, J. S., Ho, W. S., Hashim, H., & Lee, C. T. (2017). Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods. Renewable and Sustainable Energy Reviews, 67, 531-573. https://doi.org/10.1016/j.rser.2016.09.063.
75. Uludağ, A. S., & Doğan, H. (2021). Üretim Yönetiminde Çok Kriterli Karar Verme. Ankara: Nobel.
76. Ulutaş, A., & Topal, A. (2020). Bütünleştirilmiş Çok Kriterli Karar Verme Yöntemlerinin Üretim Sektörü Uygulamaları. Ankara, Türkiye: Akademisyen Kitapevi.
77. Useche, S., Montoro, L., Alonso, F., & Oviedo-Trespalacios, O. (2018). Infrastructural and Human Factors Affecting Safety Outcomes of Cyclists. Sustainability, 10, 1-12. https://doi.org/10.3390/su10020299.
78. Wahi, R. R.-h., Haworth, N., King, M., & Debnath, A. K. (2018). Influence of Type of Traffic Control on Injury Severity in Bicycle–Motor Vehicle Crashes at Intersections. Transportation Research Record, 2672(38), 199-209. https://doi.org/10.1177/0361198118773576.
79. Wang, X., Yu, H., Nie, C., Zhou, Y., Wang, H., & Shi, X. (2019). Road traffic injuries in China from 2007 to 2016: The epidemiological characteristics, trends and influencing factors. PeerJ, 7, e7423. https://doi.org/10.7717/peerj.7423.
80. Wang, J., Ma, S., Jiao, P., Ji, L., Sun, X., & Lu, H. (2023). Analyzing the Risk Factors of Traffic Accident Severity Using a Combination of Random Forest and Association Rules. Appl.Sci., 13, 1-15. https://doi.org/10.3390/app13148559.
81. Wang, Y., Wang, W., Wang, Z., Deveci, M., Roy, S. K., & Kadry, S. (2024). Selection of sustainable food suppliers using the Pythagorean fuzzy CRITIC-MARCOS method. Information Sciences, 664, 1-22. https://doi.org/10.1016/j.ins.2024.120326.
82. Yakupova, G., Buyvol, P., & Shepelev, V. (2020). Transportation Research Procedia. Identification of factors affecting the road traffic injury rate, 50, 735–742. https://doi.org/10.1016/j.trpro.2020.10.086.
83. Yang, J. (2024). Analysis of Influencing Factors of Urban Road Traffic Accidents Based on Association Rules. Transactions on Computer Science and Intelligent Systems Research, 8, 63-70.
84. Yılmaz Özekenci, S. (2024). Financial Performance Analysis of BIST Energy Index Companies with LOPCOW-CRITIC-Based CoCoSo Methods. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 27(1), 48-64.
85. Zeng, Y., Qiang, Y., Zhang, N., Yang, X., Zhao, Z., & Wang, X. (2024). An Influencing Factors Analysis of Road Traffic Accidents Based on the Analytic Hierarchy Process and the Minimum Discrimination Information Principle. Sustainability, 16, 1-23. https://doi.org/10.3390/su16166767.
86. Zhang, G., Yau, K. K., & Chen, G. (2013). Risk Factors Associated with Traffic Violations and Accident Severity in China. Accident Analysis & Prevention, 59, 18-25. https://doi.org/10.1016/j.aap.2013.05.004.
87. Zhang, W., Zhu, J., Liu, Z., & Liu, L. (2024). The Eutrophication-related Index of Drinking Water Sources Based on the Oxidation-Reduction Potential. BioResources, 19(3), 4941-4953.