Methods of preparation of initial data for determining traffic jams on the roads based on machine learning
Keywords:
bashoratli tahlillarAbstract
Prediction of traffic jams on roads plays an important role in effective management of transport systems and ensuring ease of movement in modern cities. Intelligent algorithms and artificial intelligence technologies are widely used in the implementation of this process. These technologies allow early detection and prevention of traffic accidents, as well as optimal management of traffic flow. The data about the movement of vehicles on the roads was also collected. The collected data has been cleaned and prepared. The impact of each indicator on traffic conditions was analyzed to select features and determine their importance.
References
Lowe, D. G. (1999) Object recognition from local scale-invariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on, volume 2, pages 1150–1157. Ieee.
Akhatov A. R., Nazarov F., Eshtemirov B.Sh. “Detection and analysis of traffic jams using computer vision technologies”, International Conference on Artificial Intelligence, Blockchain, Computing and Security (ICABCS-2023). Samarkand, Uzbekistan.
Axatov A. R., Eshtemirov B.Sh. “Mashinaviy o‘qitish asosida yo‘llardagi tirbandlik holatlarini tahlil qilishning intellektual algoritmlari” “Raqamli transformatsiya va sun’iy intellekt”, Toshkent davlat iqtisodiyot universiteti, 2024-yil, Volume 2, Issue 3.
F.M.Nazarov, Eshtemirov B.Sh., Q.Sh.Saydullayev “Microscopic and macroscopic flow models of traffic management” // Sharof Rashidov nomidagi Samarqand davlat universiteti Ilmiy axborotnomasi. (283/7.1-сон OAK qarori. №32), 2023-yil, 1-son (137/2).
J. Redmon and A. Angelova. Real-time grasp detection using convolutional neural networks. CoRR, abs/1412.3128, 2014.
Nannicini G. , "Point-to-point shortest paths on dynamic time-dependent road networks," 40R, vol. 8, pp. 327-330,2010.
S. Gidaris and N. Komodakis. Object detection via a multiregion & semantic segmentation-aware CNN model. CoRR, abs/1505.01749, 2015.
Akhatov A., Renavikar A., Rashidov A. & Nazarov F. “Development of the Big Data processing architecture based on distributed computing systems” Informatika va energetika muammolari O‘zbekiston jurnali, № (1) 2022, 71-79
M. A. Sadeghi and D. Forsyth. 30hz object detection with dpm v5. In Computer Vision–ECCV 2014, pages 65–79. Springer, 2014.
Bolikulov, F.; Nasimov, R.; Rashidov, A.; Akhmedov, F.; Cho, Y.-I. Effective Methods of Categorical Data Encoding for Artificial Intelligence Algorithms. Mathematics 2024, 12, 2553. https://doi.org/10.3390/math12162553
J. Yan, Z. Lei, L. Wen, and S. Z. Li. The fastest deformable part model for object detection. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 2497–2504. IEEE, 2014. 5.
Siu Hong Loh, Jia Jia Sim, Chu Shen Ong, Kim Ho Yeap, “Development of Smart Traffic Light Controller System with Deep Learning Capability in Image Processing”, 2021.
Waing, Dr. Nyein Aye, On the Automatic Detection System of Stop Line Violation for Myanmar Vehicles (Car), Volume 1 - Issue 4 November 2013.
Axatov A. (2023). Sun’iy intellektdan foydalanib yo‘llardagi tirbandlilarni baholash bosqichlari va algoritmlari. Amaliy matematikaning zamonaviy muammolari va istiqbollari. Qarshi davlat universiteti, 2024-yil, 24-25-may.
A. Akhatov, “Transport harakatini boshqarish usullari” Sun’iy intellekt va raqamli ta’lim texnologiyalari: amaliyot, tajriba, muammo va istiqbollari mavzusidagi xalqaro ilmiy-amaliy anjuman materiallari to‘plami. Samarqand davlat univerteti, 2024 yil 3-4-iyun.
Eshtemirov B.Sh., Nazarov F., Yarmatov Sh.Sh. “Technologies for identifying vehicles standing at traffic lights based on video data”, Central asian journal of mathematical theory and computer sciences, Volume: 03 Issue: 12 | ISSN: 2660-5309, 2022 yil, dekabr.
H. Mao-Chi and Y. Shwu-Huey, "A real-time and colorbased computer vision for traffic monitoring system," in Multimedia and Expo, 2004. ICME '04. 2004 IEEE International Conference on, 2004, pp. 2119-2122 Vol.3.
Kanungo, A.; Sharma, A.; Singla, C. Smart traffic lights switching and traffic density calculation using video processing. In Proceedings of the 2014 Recent Advances in Engineering and Computational Sciences (RAECS), Chandigarh, India, 6–8 March 2014; pp. 1–6.
Xun, F.; Yang, X.; Xie, Y.;Wang, L. Congestion detection of urban intersections based on surveillance video. In Proceedings of the 18th International Symposium on Communications and Information Technologies (ISCIT), Bangkok, Thailand, 26–28 September 2018; pp. 495–498.
Kurniawan, J.; Syahra, S.G.; Dewa, C.K. Traffic Congestion Detection: Learning from CCTV Monitoring Images using Convolutional Neural Network. Procedia Comput. Sci. 2018, 144, 291–297.
Additional Files
Published
How to Cite
License
Copyright (c) 2024 Bunyod Eshtemirov, Akmal Axatov

This work is licensed under a Creative Commons Attribution 4.0 International License.