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Görüntü İşleme ve KNN Sınıflandırma Algoritmasına Dayalı Akıllı Trafik Işığı Kontrol Sisteminde Veri Madenciliği

Year 2020, Ejosat Special Issue 2020 (ICCEES), 461 - 465, 05.10.2020
https://doi.org/10.31590/ejosat.819762

Abstract

Günümüzün modern dünyasında, insanların ve malların iletişimi, ulaşımı ve hareketi önemlidir ve bunu mümkün olan en kısa sürede yapmak da gerekli ve hayati önem taşımaktadır. Geçtiğimiz on yılda yolcu ve araç sayısındaki önemli artış ve haberleşme dizilerinin kapasite kısıtlamaları nedeniyle akıllı trafik kontrol ve yönetimine kesinlikle yeni teknolojilerin uygulanması gerekmektedir. Akıllı ulaşım sistemi (AUS), ulaşım ihtiyaçlarını karşılamak için bilgi işleme, telekomünikasyon ve elektronik kontrol alanlarında gelişmiş teknolojileri kullanır. Bu sistemlerin amacı, önemli ve hassas rotalardaki trafiği düzene koymak ve ayrıca trafik güvenliği, bilgi, zamanında trafik kontrolü ve ulaşım arterlerinin optimum kapasitesinin kullanılmasını sağlamaktır. Bu makale, görüntü işleme ve veri madenciliği KNN sınıflandırma algoritmasını kullanarak sinyalize otoyol ile ilişkili trafik parametrelerini çıkarmak için yeni bir yöntem sunmaktadır. Bu parametreler arasında kırmızı ışıklı LED'in uzunluğu, geçen araçların hacmi ve yeşil fazda otobanları geçen yayaların hacmi yer alıyor. Aşağıda, yukarıda bahsedilen üç trafik parametresini alarak trafik sinyali zamanlamasını optimize etmeye devam eden bir Veri Madenciliği Trafik Işığı Kontrol Sistemi tanıtılmaktadır. Sonunda MATLAB yazılım ortamında iki fazlı bir ortak otoyol simüle edilerek, görüntü işleme algoritmalarının ve bunun için tasarlanan Veri Madenciliği Trafik Işığı Kontrol Sisteminin sonuçları değerlendirilir.

References

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Data Mining in A Smart Traffic Light Control System Based on Image Processing and KNN Classification Algorithm

Year 2020, Ejosat Special Issue 2020 (ICCEES), 461 - 465, 05.10.2020
https://doi.org/10.31590/ejosat.819762

Abstract

In today's modern world, communication, transportation and the movement of people and merchandises are important, and doing so in the shortest possible time is also essential and vital. In the past decade, due to the significant increase in the number of passengers and vehicles along with the capacity limitations of communication arrays, it is absolutely necessary to apply new technologies to intelligent traffic control and management. The intelligent transportation system (ITS) utilizes advanced technologies in the fields of information processing, telecommunications and electronic control to meet transportation needs. The purpose of these systems is to streamline traffic in important and sensitive routes, and in addition to providing traffic safety, information, timely traffic control and the use of optimal capacity of transport arteries. This paper presents new method for extracting traffic parameters associated with a signalized highway using image processing and data mining KNN classification algorithm. These parameters include the length of red light LED, the volume of passing vehicles and the volume of pedestrians passing the highways in the green phase. In what follows, a Data Mining Traffic Light Control System is introduced, which by receiving the three traffic parameters mentioned above, proceeds to optimize the traffic signal timing. At the end, a two-phase common highway is simulated in the MATLAB software environment, and the results of the image processing algorithms and the Data Mining Traffic Light Control System designed for it are evaluated.

References

  • Zheng, Jianyang, et al. "Detecting cycle failures at signalized intersections using video image processing." Computer‐Aided Civil and Infrastructure Engineering 21.6 (2006): 425-435.
  • Reyes, Mac Michael. "Traffic Light Control System Simulation Through Vehicle Detection By Image Processing." (2008).
  • Wang, Kunfeng, et al. "An automated vehicle counting system for traffic surveillance." Vehicular Electronics and Safety, 2007. ICVES. IEEE International Conference on. IEEE, 2007.
  • Lee, Daeho, and Youngtae Park. "Measurement of traffic parameters in image sequence using spatio-temporal information." Measurement Science and Technology 19.11 (2008): 115503.
  • Bhaskar, Lala, et al. "Intelligent traffic light controller using inductive loops for vehicle detection." Next Generation Computing Technologies (NGCT), 2015 1st International Conference on. IEEE, 2015.
  • Hsu, W-L., et al. "Real-time traffic parameter extraction using entropy." IEE Proceedings-Vision, Image and Signal Processing 151.3 (2004): 194-202.
  • Sivakumar, R., et al. "Automated traffic light control system and stolen vehicle detection." Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE International Conference on. IEEE, 2016.
  • Mirchandani, Pitu, and Larry Head. "A real-time traffic signal control system: architecture, algorithms, and analysis." Transportation Research Part C: Emerging Technologies 9.6 (2001): 415-432.
  • Hu, Peiyun, and Deva Ramanan. "Finding tiny faces." 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017.
  • Zamani, Zahra, Mahmoud Pourmand, and Mohammad Hossein Saraee. "Application of data mining in traffic management: case of city of Isfahan." Electronic Computer Technology (ICECT), 2010 International Conference on. IEEE, 2010.
  • Thakare, Vishakha S., et al. "Design of smart traffic light controller using embedded system." ISOR-JE 10.1 (2013): 30-3.
  • Kotsiantis, Sotiris B., I. Zaharakis, and P. Pintelas. "Supervised machine learning: A review of classification techniques." (2007): 3-24
There are 12 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Abdullah Yusefı 0000-0001-7557-8526

Adem Alpaslan Altun 0000-0002-3960-5141

Cemil Sungur 0000-0003-2340-6225

Publication Date October 5, 2020
Published in Issue Year 2020 Ejosat Special Issue 2020 (ICCEES)

Cite

APA Yusefı, A., Altun, A. A., & Sungur, C. (2020). Data Mining in A Smart Traffic Light Control System Based on Image Processing and KNN Classification Algorithm. Avrupa Bilim Ve Teknoloji Dergisi461-465. https://doi.org/10.31590/ejosat.819762