Abstract
Vehicular traffic is growing almost everywhere in the world which is the prime cause of congestion, especially at road intersections. Due to unreliable and unpredictable road travel conditions, the emergency and essential services may get trapped in traffic congestion. The traffic lights traditionally have a fixed time-scheduling and this further blocks the smooth flow of traffic. In this paper, a density-based remote override traffic control system has been proposed which provides a solution to smartly manage the dynamic traffic conditions. In this test device, the design of the junction density calculation would be centered on infrared (IR) sensors, installed on each lane and interfaced with the microcontroller. The IR will be enabled as vehicles travel through the path. This may be achieved by changing the sequential order of traffic lights generated by the autonomous road surveillance network by utilizing the automated remote sensing-based system.
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References
Bhilawade V, Ragha LK (2018) Intelligent traffic control system. Int J Scientific Res Publ 8(2)
Kaur G, Sharma S (2017) Traffic management using digital image processing. IJCST 8(2)
Shirazi MS, Morris BT (2016) Vision-based turning movement monitoring: count, speed & waiting time estimation. IEEE Intell Transp Syst Mag 8(1):23–34
Mehboob F, Abbas M, Jiang R, Al-Maadeed S, Bouridane A, Tahir MA (2016) Automated vehicle density estimation from raw surveillance videos. In: SAI computing conference, London, UK
Tejashri G, Priyanka C, Komal C, Togrikar PS (2016) Implementing intelligent traffic control system for congestion control, ambulance clearance and stolen vehicle detection
Lan C, Chang G (2015) A traffic signal optimization model for intersection experiencing heavy scooter vehicle mixed traffic flows. 16(4):1771–1783
Monika G, Kalpana N, Gnanasundari P (2015) An intelligent automatic traffic light controller using embedded systems. Int J Innov Res Sci Eng Technol 4(4)
Kavya G, Saranya B (2015) Density based intelligent traffic signal system using PIC microcontroller. Int J Res Appl Sci Eng Technol (IJRASET) 3(1):205–209
Kham N, Nwe C (2014) Implementation of modern traffic light control system. Int J Sci Res Publ 4(6)
Isa I, Shaari N, Fayeez A, Azlin N (2014) Portable wireless traffic light system (PWTLS). Int J Res Eng Technol 3(2):242–247
Ali MDH, Kurokawa S et al (2013) Autonomous road surveillance system proposed model for vehicle detection and traffic signal control. Procedia Comput Sci 19
Chandrasekhar M, Saikrishna C, Chakradhar B, Kumar PP, Sasanka C (2013) Traffic control using, digital image processing. IJAEEE 2(5). ISSN (Print): 2278-8948
Jaiswal S, Agarwal T, Singh A, Lakshita (2013) Intelligent traffic control unit. Int J Electri Electron Comput Eng 2(2):66–72
Tyagi V, Kalyanaraman S, Krishnapuram R (2012) Vehicular traffic density state estimation based on cumulative road acoustics. IEEE Trans Intell Transp Syst 23(3)
Niksaz P (2020) Automatic traffic estimation using image processing. In: 2012 international conference on image, vision and computing
Aggarwal S et al (2020) Meta heuristic and evolutionary computation: algorithms and applications. Springer Nature, Berlin, p 849. https://doi.org/10.1007/978-981-15-7571-6. ISBN 978-981-15-7571-6
Jafar A et al (2021) AI and machine learning paradigms for health monitoring system: intelligent data analytics study in big data, vol 86. Springer Nature, Berlin. ISBN 978-981-334-411-2
Gopal et al (2021) Digital transformation through advances in artificial intelligence and machine learning. J Intell Fuzzy Syst 1–8. https://doi.org/10.3233/JIFS-189787
Mahto T et al (2020) Traffic signal control to optimize run time for energy saving: a smart city paradigm, metaheuristic and evolutionary computation: algorithms and applications. Studies in computational intelligence. Springer Nature, pp 491–497. https://doi.org/10.1007/978-981-15-7571-6_21
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Varshney, G., Jaiswal, A., Mittal, U., Pawar, A., Satyajeet (2022). Density-Based Remote Override Traffic Control System. In: Tomar, A., Malik, H., Kumar, P., Iqbal, A. (eds) Machine Learning, Advances in Computing, Renewable Energy and Communication. Lecture Notes in Electrical Engineering, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-16-2354-7_3
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DOI: https://doi.org/10.1007/978-981-16-2354-7_3
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