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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: June 2026
Volume 11 | Issue 6
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Impact Factor : 8.14
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| Paper Title: | IoT-Enabled Density-Based Traffic Light Control System |
| Authors Name: | Rejin Ram c , Sreetha Sreedhar K , Anugrah EK , Rishikesh N , Siva Priya K |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_212378
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| Published Paper Id: | IJRTI2605050 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | |
| Abstract: | This paper presents an AI-based intelligent traffic light control system that integrates ESP32 microcontrollers with AI-powered cameras, utilizing the YOLO (You Only Look Once) object detection algorithm. The system is designed to dynamically manage and optimize traffic signal timings based on real-time vehicle density at a four-way junction. Each junction is equipped with red, yellow, and green LED traffic signals on the north, south, east, and west sides—totaling twelve LEDs. The ESP32 microcontroller serves as the central processing unit, connected to the Google Cloud via the Internet for real-time data processing and synchronization. The system continuously analyzes live video feeds using the YOLO algorithm to determine traffic density on each lane. Based on this data, the traffic control server intelligently adjusts signal durations: extending green light intervals during high-density conditions and reducing them when traffic is minimal, thereby improving flow efficiency and reducing waiting times. An additional emergency vehicle prioritization mechanism instantly activates the green signal on the path of an approaching ambulance or fire truck, while setting red lights on all other sides, ensuring a clear route for emergency response. To enhance connectivity monitoring, a dedicated Wi-Fi connection LED blinks during active communication and remains steadily lit when disconnected. Furthermore, the system uploads real-time traffic density data to the cloud, enabling the public to access live traffic information for better route planning and congestion avoidance. In an era of escalating urban traffic challenges, this AI-driven intelligent traffic control system offers a scalable, responsive, and efficient solution to reduce congestion, enhance emergency vehicle response times, and empower the public with actionable traffic insights. Index Terms- Artificial Intelligence (AI), Intelligent Traffic Light Control System, Real-Time Traffic Monitoring, Cloud Computing, YOLO (You Only Look Once), Object Detection, Smart Transportation, Emergency Vehicle Prioritization, Four-Way Junction Control |
| Keywords: | Artificial Intelligence(AI),Internet of Things(IoT),Smart Traffic System |
| Cite Article: | "IoT-Enabled Density-Based Traffic Light Control System", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a430-a433, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605050.pdf |
| Downloads: | 000205566 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2605050
Registration ID:212378
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier):
Page No: a430-a433 Country: Kannur, Kerala, India Research Area: Transportation Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605050 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605050 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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