Abstract
Nowadays, congestion in traffic is a serious issue all over the world. The traffic congestion is caused because of large red light delays. The delay of the respective light is coded hardly in the traffic light and also it is not dependent on traffic density. The existing system varies the particular light delay time by taking the vehicle count using IR sensors which has several disadvantages. This project presents the system based on raspberry pi. It includes a high-resolution camera. It captures images of vehicles. It performs the blob detection of a vehicle. It gives a separate count of vehicles and people too. This recorded vehicle count data is used in the future to analyze traffic conditions at respective traffic lights connected to the system. For appropriate analysis, the raspberry pi will work on the information to send correct signal into the LED lights. However, to solve the problem of emergency vehicles stuck in the overcrowded roads, a portable controller device is designed. The system will give the vehicle count by the deep neural technique. After vehicle detection and its count, the system will apply conditional probability to glow the green signal for a specific time period on a particular side according to the vehicle count.
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References
Kham, N., Nwe, C.: Implementation of modem traffic light control system. Inter. J. Sci. Res. Pub. (IJSRP) 4(6) (2014)
Kamal, M.A.S., Imur, J., Ohata, A., Hayakawa, T., Aihara, K.: Control of traffic signals in a model predictive control framework. In: 13th IFAC Symposium on Control in Transportation Systems, The International Federation of Automatic Control, 978-3-902823-13-7/12, pp 221–226 (2012)
Ghazal, B., Eikhatib, K., Chahine, K., Kherfan, M.: Smart traffic light control system, pp. 140–145 (2016). ISBN 978-1-4673-6942-8/16
Poyen, E.F.B., Bhakta, A.K., Durga Manohar, B., Ali, I., Rao, A.S.A.P.: Density based traffic control. Inter. J. Adv. Eng. Manag. Sci. (IJAEMS) 2(8), 1379–1384 (2016). ISSN 2454-1311
Krishnaiah, G., Rajani, A., Rajesh, P.: Literature review on traffic signal control system based on wireless technology. ICDER, 63–68 (2014)
Choudekar, P., Banerjee, S., Muju, M.K.: Real time traffic light control using image processing. Inter. J. Comput. Sci. Eng. (IJCSE) 2(1), 6–10 (2011). ISSN 0976-5166
Bhusari, S., Patil, S., Kalbhor, M.: Traffic control system using Raspberry-pi. Global J. Adv. Eng. Technol. 4(4), 413–415 (2015). ISSN (Online) 2277-6370
Vidhyia, M., Elayaraja, S., Anitha, M., Divya M., Divya Barathi, S.: Traffic light control system using Raspberry-pi. Asian J. Electr. Sci. (AJES) 5(1), 8–12 (2016). ISSN 2249-6297
Ramteke, M.D., Pote, H.P., Ukey, A., Ugemuge, P., Gonnade, S.: Edge detection based adaptive traffic control system. Inter. J. Recent Innov. Trends Comput. Commun. (IJRITCC) 4(4), 323–332 (2016). ISSN 2321-8169
Tahmid, T., Hossain, E.: Density based smart traffic control system using canny edge detection algorithm for congregating traffic information. In: EICT. IEEE-978-1-5386-2307-7/17 (2017)
Vijayaraj, J., Loganathan, D.: Traffic congestion control of vehicles based on edge detection using image processing. Inter. J. Pure Appl. Math. (IJPAM). 119(14), 1407–1418 (2018). ISSN 1314-3395
Balasubramani, S., John Aravindhar, D.: Design traffic light control system based on location information and vehicle density in VANET. IJRTE 7(5S4) (2019). ISSN 2277-3878
Chaudhari, V.D., Patil, A.J.: Prioritized ViU departure at traffic intersection using internet of things. In: Iyer, B. et al. (eds.) Computing in Engineering and Technology, Advances in Intelligent Systems and Computing, vol. 1025, pp. 267–276. Springer Nature Singapore Pte Ltd (2020)
Sapkale, J.M., Chaudhari, V.D., Patil, A.J.: Vehicular traffic monitoring at city intersection using probability. Inter. J. Innov. Eng. Sci. (IJIES) 4(10), 82–84 (2019). ISSN 2456-3463
Deshpande, P., Iyer, B.: Research directions in the internet of every things (IoET). In: International Conference on Computing, Communication and Automation (ICCCA), pp. 1353–1357 (2017)
Patil, N., Iyer, B.: Health monitoring and tracking system for soldiers using internet of things (IoT). In: 2017 International Conference on Computing, Communication and Automation, pp. 1347–1352 (2017)
Iyer, B., Patil, N.: IoT enabled tracking and monitoring sensor for military applications. Int. J. Syst. Assur. Eng. Manag. 9, 1294–1301 (2018). https://doi.org/10.1007/s13198-018-0727-8
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Sapkale, J.M., Chaudhari, V.D., Dhande, H.V., Patil, A. (2021). Probability Analysis of Vehicular Traffic at City Intersection. In: Deshpande, P., Abraham, A., Iyer, B., Ma, K. (eds) Next Generation Information Processing System. Advances in Intelligent Systems and Computing, vol 1162 . Springer, Singapore. https://doi.org/10.1007/978-981-15-4851-2_8
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DOI: https://doi.org/10.1007/978-981-15-4851-2_8
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