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Working principle and performance evolution of camera-based intelligent signalized intersections: Samsun city, Türkiye example

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the current literature, it is clearly seen that most of the traffic chaos is generally observed at intersections of the urban roads in cities. On the other hand, many current traffic studies and research prove that fixed-time signalized intersections cannot have a good ability to control and manage current traffic flow at signalized intersection legs. For this aim, intelligent intersections were developed and started to be used in many cities all over the world in the last decade. These new intelligent intersection systems suggest dynamic signal times for all intersection legs by using real-time measured traffic data. These systems generally use cameras or loop detectors, which are located in the proper places on a signalized intersection leg and record vehicle movements. Within the scope of this study, a performance comparison was made for before and after the camera-based intelligent intersection applications at three isolated pilot signalized intersections within the scope of the "Smart City Traffic Safety" project, which is one of the largest Intelligent Transportation System projects in Turkey. After the system was activated, it was observed that the drivers had impatient behaviors in the beginning and had difficulty getting used to these new systems. After the signal cycle was regulated with the learning of artificial intelligence, it was seen that the drivers had more patience and more observant behaviors. It was also obtained from the analysis results that these new intelligent systems resulted in an average 16% decrease in control delays and a 20% decrease in vehicle speeds.
Rocznik
Tom
Strony
5--17
Opis fizyczny
Bibliogr. 20 poz.
Twórcy
  • Faculty of Engineering, Ondokuz Mayıs University, Kurupelit Kampüsü, 55217 Samsun, Turkey
  • School of Geography and Planning, Cardiff University, CF10 3WA, Cardiff, United Kingdom
  • School of Engineering, Cardiff University, CF24 3AA, Cardiff, United Kingdom
Bibliografia
  • 1. Yang S., E. Bailey, Z. Yang, J. Ostrometzky, G. Zussman, I. Seskar, Z. Kostic. 2020. “Cosmos smart intersection: Edge compute and communications for bird's eye object tracking”. In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops): 1-7. IEEE.
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  • 3. Czech Piotr. 2017. „Physically disabled pedestrians - road users in terms of road accidents”. Advances in Intelligent Systems and Computing 505: 33-44. DOI: https://doi.org/10.1007/978-3-319-43991-4_4. Springer, Cham. ISBN: 978-3-319-43990-7; 978-3-319-43991-4. ISSN: 2194-5357. In: Sierpinski Grzegorz (eds), Intelligent transport systems and travel behaviour, 13th Scientific and Technical Conference „Transport Systems Theory and Practice”, Katowice, Poland, September 19-21, 2016.
  • 4. Czech Piotr. 2017. „Underage pedestrian road users in terms of road accidents”. Advances in Intelligent Systems and Computing 505: 33-44. DOI: https://doi.org/10.1007/978-3-319-43991-4_4. Springer, Cham. ISBN: 978-3-319-43990-7; 978-3-319-43991-4. ISSN: 2194-5357. In: Sierpinski Grzegorz (eds), Intelligent transport systems and travel behaviour, 13th Scientific and Technical Conference „Transport Systems Theory and Practice”, Katowice, Poland, September 19-21, 2016.
  • 5. Olaya-Quiñones J.D., J.C. Perafan-Villota. 2021. “A smart algorithm for traffic lights intersections control in developing countries”. In: IEEE Colombian Conference on Applications of Computational Intelligence: 93-106. Springer, Cham.
  • 6. Mu H., L. Liu, X. Li. 2018. “Signal preemption control of emergency vehicles based on timed colored petri nets”. Discrete Dynamics in Nature and Society: 1-15.
  • 7. Wang X.B., K. Yin, H. Liu. 2018. “Vehicle actuated signal performance under general traffic at an isolated intersection”. Transportation Research Part C: Emerging Technologies 95: 582-598.
  • 8. Zheng J., Y. Wang, N.L. Nihan, M.E. Hallenbeck. 2006. “Detecting cycle failures at signalized intersections using video image processing”. Computer‐Aided Civil and Infrastructure Engineering 21(6): 425-435.
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  • 10. Fathy M., M.Y. Siyal. 1995. “Real-time image processing approach to measure traffic queue parameters”. IEEE Proceedings: Vision, Image, and Signal Processing 142(5): 297-303.
  • 11. Yin Z., Y. Fan, H. Liu, B. Ran. 2004. “Using image sensors to measure real-time traffic flow parameters, Preprint CD-ROM”. In: 83rd Transportation Research Board Annual Meeting. January 2004. Washington, DC, USA.
  • 12. Gupte S., O. Masoud, F.K.R. Martin, N.P. Papanikolopoulos. 2002. “Detection and classification of vehicles”. IEEE Transactions on Intelligent Transportation Systems 3(1): 37-47.
  • 13. Saito M., J. Walker, A. Zundel. 2001. “Using image analysis to estimate average stopped delays per vehicle at signalized intersections”. Transportation Research Record: Journal of the Transportation Research Board 1776: 106-113.
  • 14. Saunier N., T. Sayed. 2007. “Automated analysis of road safety with video data”. Transportation Research Record 2019(1): 57-64.
  • 15. Messelodi S., C.M. Modena, M. Zanin. 2005. “A computer vision system for the detection and classification of vehicles at urban road intersections”. Pattern Analysis and Applications 8(1): 17-31.
  • 16. Rinner B., W. Wolf. 2008. “An introduction to distributed smart cameras”. Proceedings of the IEEE 96(10): 1565-1575.
  • 17. Shi Y., F.D. Real. 2009. “Smart cameras: Fundamentals and classification”. In: Smart cameras: 19-34. Springer, Boston, MA.
  • 18. Hu Z., C. Wang, K. Uchimura. 2007. “3D vehicle extraction and tracking from multiple viewpoints for traffic monitoring by using probability fusion map”. In: 2007 IEEE Intelligent Transportation Systems Conference: 30-35. IEEE.
  • 19. Subedi S., H. Tang. 2018. “Development of a multiple-camera 3D vehicle tracking system for traffic data collection at intersections”. IET Intelligent Transport Systems 13(4): 614-621.
  • 20. “Cyclops Smart Intersection Management System”. Available at: https://www.mosas.com.tr/sinyalizasyon/solutions/cyclops/.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d115ddbd-e644-4d2b-9823-806887d9683f
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