Abstract
An effective road traffic control system ensures continuous movement of traffic and helps in preventing crashes or accidents. Better traffic management requires traffic signal control based on vehicle density. One such technique proposed in this paper finds the solution to traffic flow control, depending on the number of vehicles on the lane. It has two separate systems to control the traffic flow. One of the systems first collects the vehicle density (data) on the individual lanes using ultrasonic sensors. Second system uses this data in order to control traffic lights. By sharing this data, using NRF24L01 transceiver module, the system handles the traffic lights. The LEDs in the traffic lights are then appropriately controlled and powered based on the data received. With separate systems, maintenance of the components is simpler and it can also be observed that the lack of physical contact between the systems enables us to modify the implementation in one of them without any requirement of changes in the other. This technique uses an Arduino Uno and an Arduino Mega as the base for the two different systems. This solution is built to be compatible with subsequent improvements and can be extended to an IoT model.
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References
Hobbs, F.D., Jovanis, P.P.: https://www.britannica.com/technology/traffic-control. Encyclopædia Britannica, Inc, 08 September 2000. [Online]. Available: https://www.britannica.com/technology/traffic-control#accordion-article-history. Accessed 05 June 2019
Papageorgiou, M., Kiakaki, C., Dinopoulou, V., Kotsialos, A., Wang, Y.: Review of road traffic control strategies. Proc. IEEE 91(12), 2043–2067 (2003)
Misbahuddin, S., Zubairi, J.A., Saggaf, A., Basuni, J., A-Wadany, S., Al-Sofi, A.: IoT based dynamic road traffic management for smart cities. In: 2015 12th International Conference on High-Capacity Optical Networks and Enabling/Emerging Technologies, HONET-ICT 2015 (2016)
Suresh Kumar, S., Rajesh Babu, M., Vineeth, R., Varun, S., Sahil, A.N., Sharanraj, S.: Autonomous Traffic Light Control System for Smart Cities, pp. 325–335 (2019)
Banerjee, S., Chakraborty, C., Chatterjee, S.: A survey on IoT based traffic control and prediction mechanism. In: Intelligent Systems Reference Library, vol. 154, pp. 53–75. Springer Science and Business Media Deutschland GmbH (2019)
Nellore, K., Hancke, G.P.: A Survey on Urban Traffic Management System Using Wireless Sensor Networks, vol. 16. MDPI AG (2016)
Jain, N.K., Saini, R.K., Mittal, P.: A review on traffic monitoring system techniques. In: Soft Computing: Theories and Applications, pp. 569–577. Springer, Berlin (2019)
Parekh, S., Dhami, N., Patel, S., Undavia, J.: Traffic signal automation through IoT by sensing and detecting traffic intensity through IR sensors. In: Information and Communication Technology for Intelligent Systems, pp. 53–65. Springer, Berlin (2019)
BBC, “BBC News,” BBC, 10 June 2016. [Online]. Available: https://www.bbc.com/news/world-asia-india-36496375. Accessed 5 June 2019
Sundar, R., Hebbar, S., Golla, V.: Implementing intelligent traffic control system for congestion control, ambulance clearance, and stolen vehicle detection. IEEE Sens. J. 15(2), 1109–1113 (2015)
Rath, M., Pati, B., Pattanayak, B.K.: Mobile agent-based improved traffic control system in VANET. In: Studies in Computational Intelligence, vol. 771. Springer, Berlin, pp. 261–269 (2019)
Dandala, T.T., Krishnamurthy, V., Alwan, R.: Internet of Vehicles (IoV) for traffic management. In: 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP) (2017)
Kumar, P.M., Devi, U., Manogaran, G., Sundarasekar, R., Chilamkurti, N., Varatharajan, R.: Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Comput. Netw. 144, 154–162 (2018)
Wiering, M.A., Veenen, J., Vreeken, J., Koopman, A.: Intelligent traffic light control. Utrecht University: Information and Computing Sciences (2004)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)
Liang, X., Du, X., Wang, G., Han, Z.: A deep reinforcement learning network for traffic light cycle control. IEEE Trans. Veh. Technol. 68(2), 1243–1253 (2019)
Kumaran, S.K., Mohapatra, S., Dogra, D.P., Roy, P.P., Kim, B.-G.: Computer vision-guided intelligent traffic signaling for isolated intersections. Expert Syst. Appl. (2019)
Santhosh, K.K., Dogra, D.P., Roy, P.P.: Temporal unknown incremental clustering model for analysis of traffic surveillance videos. IEEE Trans. Intell. Transp. Syst. 99, 1–12 (2018)
Tutorialspoint (2019) Arduino—Ultrasonic Sensor. https://www.tutorialspoint.com/arduino/arduino_ultrasonic_sensor.htm. Accessed 05 June 2019
How to Mechatronics (2019) Arduino wireless communication—NRF24L01 tutorial. https://howtomechatronics.com/tutorials/arduino/arduino-wireless-communication-nrf24l01-tutorial/. Accessed 05 June 2019
Arduino (2019) millis(). https://www.arduino.cc/reference/en/language/functions/time/millis/. Accessed 5 June 2019
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Ravish, R., Shenoy, D.P., Rangaswamy, S. (2020). Sensor-Based Traffic Control System. In: Mandal, J., Mukhopadhyay, S. (eds) Proceedings of the Global AI Congress 2019. Advances in Intelligent Systems and Computing, vol 1112. Springer, Singapore. https://doi.org/10.1007/978-981-15-2188-1_17
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DOI: https://doi.org/10.1007/978-981-15-2188-1_17
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