Socio-Economic Impacts Resulting From The Integration Of Artificial Intelligence Into Electronic Surveillance Systems In Traffic

Abstract

In the last decade, electronic surveillance systems have been actively employed for monitoring traffic rule violations with the aim of enhancing traffic regulation. The utilization of these systems has resulted in increased compliance with traffic regulations, consequently leading to a reduction in losses attributed to traffic accidents. The impact created by these systems is expected to be further amplified through the incorporation of artificial intelligence (AI) support. Within the scope of this study, a detailed analysis of the socio-economic impact of AI-assisted Electronic Traffic Monitoring Systems has been conducted, focusing on economic, mobility, health, environmental, and quality of life aspects.

Keywords

• Artificial intelligence
• Socio-economic impact
• Life quality

Trafikteki Elektronik Gözetim Sistemlerine Yapay Zeka Entegrasyonu Sonucu Elde Edilecek Sosyo-Ekonomik Etkiler

Abstract

Son on yılda, elektronik gözetim sistemleri, trafik düzenlemesini geliştirmek amacıyla trafik kural ihlallerini izlemek için aktif olarak kullanılmaktadır. Bu sistemlerin kullanımı, trafik düzenlemelerine uyumun artmasına ve dolayısıyla trafik kazalarından kaynaklanan kayıpların azalmasına yol açmıştır. Bu sistemlerin yarattığı etkinin, yapay zeka (AI) desteğinin dahil edilmesiyle daha da artması beklenmektedir. Bu çalışma kapsamında, yapay zeka destekli Elektronik Trafik İzleme Sistemlerinin sosyo-ekonomik etkilerinin detaylı bir analizi yapılmış ve ekonomik, mobilite, sağlık, çevre ve yaşam kalitesi boyutlarına odaklanılmıştır.

Keywords

• Yapay zeka
• sosyoekonomik etki
• yaşam kalitesi

References

1. Akgüngör A.P., Doğan, E. (2010). An artificial intelligent approach to traffic accident estimation: Model development and application. Transport, 24(2). doi.org/10.3846/1648-4142.2009.24.135-142
2. Amiri, A.M., Naderi, K., Cooper, J.F., Nadimi, N. (2021). Evaluating the impact of socio-economic contributing factors of cities in California on their traffic safety condition. Journal of Transport & Health, 101010(20). doi.org/10.1016/j.jth.2021.101010
3. Biagioni, D., John, F., Venu, G., Peter, G., NAlinrat, G., Yi, H., Wesley, J., Joe, S., Devon, S., Austin, T., Juliette, U., Quichao, W., Stan, Y. (2021). Advanced Computing, Data Science, and Artificial Intelligence Research Opportunities for Energy-Focused Transportation Science. Golden: ORNEL, CO: National Renewable Energy Laboratory. NREL/ TP-2C00-79589. doi.org/10.2172/1812196
4. Contini, L., El-Basyouny, K. (2016, Eylül). Lesson learned from the application of intersection safety devices in Edmonton. Accident Analysis & Prevention, 94, 127-134. doi:10.1016/j.aap.2016.05.023
5. Council, F.M., Persaud, B.N., Eccles, K.A., Lyon, C. and Griffith, M.S. (2005). Safety Evaluation of Red-Light Cameras . U.S. Department of Transportation Federal Highway Administration.
6. Cunneen, M. (2023). Autonomous Vehicles, Artificial Intelligence, Risk and Colliding Narratives. In: Fossa, F., Cheli, F. (eds) Connected and Automated Vehicles: Integrating Engineering and Ethics. tudies in Applied Philosophy, Epistemology and Rational Ethics, vol 67. Springer, Cham. (s. 175–195). içinde doi.org/10.1007/978-3-031-39991-6_10
7. Cunneen, M., Mullins, M., & Murphy, F. (2019). Autonomous Vehicles and Embedded Artificial Intelligence:The Challenges of Framing Machine Driving Decision. APPLIED ARTIFICIAL INTELLIGENCE, 33(8), s. 706-731. doi.org/10.1080/08839514.2019.1600301
8. Das, C. P., Swain, B. K., Goswami, S., & Das, M. (2021). Prediction of traffic noise induced annoyance: A two-staged SEM-Artificial Neural Network approach. Transportation Research Part D: Transport and Environment, 100, 103055. doi.org/10.1016/j.trd.2021.103055

9. Decina, L. E., Thomas, L., Srinivasan, R., Staplin, L. K., & TransAnalytics, L. L. C. (2007). Automated Enforcement: A Compendium of Worldwide Evaluations of Results. U.S. Department of Transportation, National Highway Traffic Safety Administration.
10. Dodia, A., Kumar, S., Rani, R., Pippal, S. K., & Meduri, P. (2023). EVATL: A novel framework for emergency vehicle communication with adaptive traffic lights for smart cities. IET Smart Cities. doi.org/10.1049/smc2.12068
11. Gu, Y., Wang, Y., & Dong, S. (2020). Public traffic congestion estimation using an artificial neural network. ISPRS International Journal of Geo-Information, 9(3), 152. doi.org/10.3390/ijgi9030152
12. Herath, H. M. K. K. M. B., & Mittal, M. (2022). Adoption of artificial intelligence in smart cities: A comprehensive review. International Journal of Information Management Data Insights, 2(1), 100076. doi.org/10.1016/j.jjimei.2022.100076
13. İBB. (2023). Kurumsal Gelişim ve Yönetim Sistemleri Daire Başkanlığı Straji Geliştirme Müdürlüğü. (tarih yok). Faaliyet Raporu. İstanbul Büyükşehir Belediyesi (İBB).
14. KGM. (2023). https://www.kgm.gov.tr/SiteCollectionDocuments/KGMdocuments/Istatistikler/DevletIlYolEnvanter/IllereGoreDevletVeIlYollari.
15. Kushwaha, M., & Abirami, M. S. (2023). Intelligent model for avoiding road accidents using artificial neural network. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 18(5). doi.org/10.15837/ijccc.2023.5.5317
16. Makhani, M., & Bodkhe, N. (2022). Road traffic accidents and their aftermath: The victims perspective. International Journal of Medical Toxicology & Legal Medicine, 25(3and4), 67-74. doi.org/10.5958/0974-4614.2022.00052.3
17. Moncayo, L., Naranjo, J. L., García, I. P., & Mosquera, R. (2017). Neural based contingent valuation of road traffic noise. Transportation Research Part D: Transport and Environment, 50, 26-39. doi.org/10.1016/j.trd.2016.10.020
18. Mondal, M. A., & Rehena, Z. (2019, May). An IoT-based congestion control framework for intelligent traffic management system. In International Conference on Artificial Intelligence and Data Engineering (pp. 1287-1297). Singapore: Springer Nature Singapore. doi.org/10.1007/978-981-15-3514-7_96
19. Olayode, I. O., Du, B., Tartibu, L. K., & Alex, F. J. (2023). Traffic flow modelling of long and short trucks using a hybrid artificial neural network optimized by particle swarm optimization. International Journal of Transportation Science and Technology. doi.org/10.1016/j.ijtst.2023.04.004
20. Poole, B. (2012). An Overview of Automated Enforcement Systems and Their Potential for Improving Pedestrian and Bicyclist Safety.
21. TÜİK. (2021a). Vehicle Accident Statistics. https://data.tuik.gov.tr/Bulten/Index?p=Motorlu-Kara-Tasitlari-Aralik-2022-49436#:~:text=T%C3%BCrkiye'de%202022%20y%C4%B1l%C4%B1%20sonu,ya%C5%9F%2014%2C8%20olarak%20hesapland%C4%B1.
22. TÜİK. (2021b). Road Traffic Accident Statistics. https://data.tuik.gov.tr/Bulten/Index?p=Road-Traffic-Accident-Statistics-2021-45658
23. TÜİK. (2021c). Health expenditure statistics. https://data.tuik.gov.tr/Bulten/Index?p=Saglik-Harcamalari-Istatistikleri-2021-45728
24. TÜİK. (2022). Motor Vehicles, December 2022. https://data.tuik.gov.tr/Bulten/Index?p=Motorlu-Kara-Tasitlari-Aralik-2022-49436#:~:text=T%C3%BCrkiye'de%202022%20y%C4%B1l%C4%B1%20sonu,ya%C5%9F%2014%2C8%20olarak%20hesapland%C4%B1.
25. TÜSSİDE. (2021). EDS and Fault Detection Analysis System Application.
26. Ulu, M., Kilic, E., & Türkan, Y. S. (2024). Prediction of Traffic Incident Locations with a Geohash-Based Model Using Machine Learning Algorithms. Applied Sciences, 14(2), 725. https://doi.org/10.3390/app14020725
27. Ulu, M., Türkan, Y. S., & Mengüç, K. (2022). Trafik kazalarını etkileyen faktörlerin ağırlıklarının BWM ve SWARA yöntemleri ile belirlenmesi. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 5(2), 227-238. https://doi.org/10.51513/jitsa.1084833