A Smart Density Based Traffic Control System with Barricades and Emergency Vehicles Clearance

Authors

  • Buhari Ugbede Umar Federal University of Technology, Minna
  • Arifa Khatoon Haq
  • Solomon Teryima Salun

Keywords:

Barricades, Emergency Vehicle, IR Sensor, Traffic Warden, Traffic Control

Abstract

One of the most delicate aspects of humanity in modern society is the traffic congestion control system. It has raised a red flag for researchers, and further work is needed to prevent casualties. As a result of the congestion, the country faces several adverse effects. Because of people’s inability to follow traffic laws, lack of tolerance among road users, insufficient facilities, and lack of awareness among traffic wardens, traffic congestion has become a frequent occurrence in Nigeria. Nigeria’s traffic control system has been subjected to various violations (Niger state as a case study). To address some
of the challenges, this research proposed a smart density-based traffic control system with barricades and emergency clearance to address the abuses and restructure the system to achieve a free traffic flow in the state. This system is designed to do away with a manual system of traffic control, grant quick but logical access to an emergency vehicle, and replace the counter system of signal light using Pre-Empty Priority scheduling algorithm to assign higher priority to emergency vehicles. C programming language is utilised with the aid of IR sensor, Arduino AT Mega 2560, RF transceiver, LED, barricade and metal-gear-micro servo motor. The system is evaluated based on response time after trials for the speed of the servo motor. Setting the servo motor speed from 00 to 900 and back to 00 shows that the get ready to stop is recorded at 2000 microseconds. After five trials, 8,7 and 9,8 seconds were obtained for Road1 and Road2 as the high and low values, respectively. The highest value of 8 and 9 seconds implies that it takes a second longer for the system to
adequately initialised, after which it maintains a steady response. The result is significant and influential for the system. 

References

J. Liu et al., “Secure intelligent traffic light control using fog computing,” Future Generation Computer Systems, vol. 78, pp. 817- 824, 2018.

A. L. O. Paraense, K. Raizer, and R. R. Gudwin, “A machine consciousness approach to urban traffic control,” Biologically Inspired Cognitive Architectures, vol. 15, pp. 61-73, 2016.

C. Roncoli, M. Papageorgiou, and I. Papamichail, “Traffic flow optimisation in presence of vehicle automation and communication systems–Part II: Optimal control for multi-lane motorways,” Transportation Research Part C: Emerging Technologies, vol. 57, pp.260-275, 2015.

S. Araghi, A. Khosravi, and D. Creighton, “Intelligent cuckoo search optimised traffic signal controllers for multi-intersection network,” Expert Systems with Applications, vol. 42, no. 9, pp. 4422-4431, 2015.

A. Jovanović, M. Nikolić, and D. Teodorović, “Area-wide urban traffic control: A Bee Colony Optimization approach,” Transportation Research Part C: Emerging Technologies, vol. 77, pp. 329-350, 2017.

M. F. Chowdhury, M. R. A. Biplob, and J. Uddin, “Real-time traffic density measurement using computer vision and dynamic traffic control,” in 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2018, pp. 353-356: IEEE.

R. B. Devi, D. K. Reddy, E. Sravani, G. Srujan, S. Shankar, and S. Chakrabartty, “Density-based traffic signal system using Arduino Uno,” in 2017 International Conference on Inventive Computing and Informatics (ICICI), 2017, pp. 426-429: IEEE.

C. El Hatri and J. Boumhidi, “Traffic management model for vehicle re-routing and traffic light control based on Multi-Objective Particle Swarm Optimization,” Intelligent Decision Technologies, vol. 11, no. 2, pp. 199-208, 2017.

T. Seo, A. M. Bayen, T. Kusakabe, and Y. Asakura, “Traffic state estimation on highway: A comprehensive survey,” Annual reviews in control, vol. 43, pp. 128-151, 2017.

S. M. A. D. S. Biru Rajak, “An Efficient Emergency Vehicle Clearance Mechanism For Smart Cities,” Journal Of Mechanics Of Continua And Mathematical Sciences, Vol. 14, No. 05, Pp. 78 - 97, 2019.

M. Y. Shinde and M. H. Powar, “Intelligent traffic light controller using IR sensors for vehicle detection,” International Advanced Research Journal in Science, Engineering and Technology, vol. 4, no. 2, pp. 88- 90, 2017.

M. A. Kumaar, G. A. Kumar, and S. Shyni, “Advanced traffic light control system using barrier gate and GSM,” in 2016 International Conference on Computation of Power, Energy Information and Communication (ICCPEIC), 2016, pp. 291-294: IEEE.

S. G. P. a. S. Madhavanavar, “Different Techniques Used in Traffic Control System: An Introduction,” International Journal of Engineering Research & Technology (IJERT), vol. 06, no. 03, 2018.

R. Keerthi and S. HariharaGopalan, “A Survey On Various Traffic Management Schemes For Traffic Clearance, Stolen Vehicle And Emergency Vehicle,” International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE), 2016.

C. Greeshma, K. Nidhindas, and P. Sreejith, “Traffic control using computer vision,” 2019.

Y. Udoakah and I. Okure, “Design and implementation of a densitybased traffic light control with a surveillance system,” Nigerian Journal of Technology, vol. 36, no. 4, pp. 1239-1248, 2017.

L. N. Nipa, “Development of a Traffic Control System by using Microcontroller based Barricade,” Development, vol. 2, no. 4, 2015.

W.-H. Lee and C.-Y. Chiu, “Design and implementation of a smart traffic signal control system for smart city applications,” Sensors, vol. 20, no. 2, p. 508, 2020.

S. S. S. Vastava, B. Vandana, M. Bhavana, and R. Gongati, “Automatic movable road divider using Arduino UNO with Node Micro Controller Unit (MCU),” Materials Today: Proceedings, 2021.

N. Prakash, E. Udayakumar, and N. Kumareshan, “Arduino Based traffic congestion control with automatic signal clearance for emergency vehicles and Stolen Vehicle Detection,” in 2020 InternationalConference on Computer Communication and Informatics (ICCCI), 2020, pp. 1-6: IEEE.

S. Agrawal and P. Maheshwari, “Controlling of Smart Movable Road Divider and Clearance Ambulance Path Using IOT Cloud,” in 2021 International Conference on Computer Communication and Informatics (ICCCI), 2021, pp. 1-4: IEEE.

Downloads

Published

2021-12-31

How to Cite

Umar, B. U., Khatoon Haq, A. ., & Salun, S. T. (2021). A Smart Density Based Traffic Control System with Barricades and Emergency Vehicles Clearance. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 13(4), 43–48. Retrieved from https://jtec.utem.edu.my/jtec/article/view/6146