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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65290
Volume:
10 |
6 |
Jun 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
electronics and telecommunication
matoshri college of engineering and research centre
electronics and telecommunication
matoshri college of engineering and research centre
electronics and telecommunication
matoshri college of engineering and research centre
electronics and telecommunication
matoshri college of engineering and research centre
Nowadays congestion in traffic is a serious issue. The traffic congestion can also be caused by large red light delays etc. The delay of respective light is hard coded in the traffic light and it is not dependent on traffic. In this paper we studied the optimization of traffic light controller in a city using microcontroller. The system tries to reduce possibilities of traffic jams, caused by traffic lights, to an extent. The system is based on raspberry-pi. The system contains IR transmitter and IR receiver which are mounted on either sides of roads respectively. Based on different vehicles count, the raspberry pi takes desicision and updates the traffic lights delays as a result. Thus based on vehicles count, raspberry-pi defines different ranges for traffic light delays and updates those accordingly. This recorded vehicle count data can be used in future to analyze traffic condition at respective traffic lights connected to the system. For appropriate analysis, the record data can be downloaded to the controller through communication between raspberry-pi and the computer then it will send correct signal into the LED lights . In future in this system can be used to inform people about different places traffic condition.
KEYWORDS
This project presented the design and implementation of a Smart Traffic Management System using Arduino Uno and IR sensors. The system automatically monitors traffic density and controls traffic signals based on real-time vehicle movement. By dynamically adjusting signal timings, the proposed system improves traffic flow, reduces congestion, minimizes fuel consumption, and enhances road safety. The experimental results demonstrated that the system operates effectively and provides better traffic management compared to conventional fixed-time traffic signals. The use of low-cost hardware components makes the system suitable for educational projects and small-scale traffic control applications. Future work may include the integration of IoT technology, cloud-based monitoring, emergency vehicle priority systems, image processing techniques, and artificial intelligence algorithms for more advanced and intelligent traffic management solutions.
[1] P. Patel and R. Shah, “Density-based traffic signal control system using IR sensors,” International Journal of Engineering Research and Technology, vol. 8, no. 4, pp. 45–50, 2021. [2] A. Kumar and S. Singh, “Arduino-based smart traffic management system,” International Journal of Advanced Research in Electronics and Communication Engineering, vol. 9, no. 2, pp. 120–126, 2022. [3] R. Sharma and P. Verma, “IoT-enabled intelligent traffic monitoring and control system,” International Journal of Smart City Applications, vol. 5, no. 1, pp. 25–32, 2023. [4] M. Gupta, N. Agarwal, and S. Jain, “Smart traffic control using image processing techniques,” IEEE International Conference on Smart Transportation Systems, pp. 112–117, 2022. [5] S. Roy and K. Das, “Intelligent transportation systems for smart cities: A review,” IEEE Access, vol. 10, pp. 45876–45890, 2022. [6] N. Singh and P. Joshi, “Vehicle density detection and adaptive traffic signal control,” International Journal of Computer Applications, vol. 174, no. 15, pp. 10–15, 2021. [7] Arduino Documentation, “Arduino Uno Rev3 Technical Specifications,” 2024. [Online]. Available: https://www.arduino.cc [8] “IR Sensor Module Interfacing with Arduino,” Arduino Project Hub, 2024. [Online]. Available: https://projecthub.arduino.cc [9] J. Lee and M. Chen, “Smart traffic management for urban transportation systems,” IEEE Smart Cities Journal, vol. 11, no. 3, pp. 78–89, 2023. [10] A. Rahman, “Real-time traffic monitoring and signal optimization using embedded systems,” Sensors and Systems Journal, vol. 12, no. 1, pp. 55–63, 2023.
Publication Date
6/23/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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