Smart Traffic Management System Using Vehicle Detection with ESP32 and Web-Based Control Interface

Abstract

Urban traffic flow is suffering severe crisis due to the rapid increasing traffic volume and the limitation of fixed-time traffic signal coordination. This paper presents a smart traffic management system for improving traffic efficiency of three-lane intersection by real-time signal control based on vehicle density. This system uses video-based vehicle detection and ESP32 for signal control. Web interface is developed for signal countdown display through local IP address displayed on an in-built OLED display. Emergency control using RF with emergency switches to give priority to emergency vehicles is also incorporated . This system improves traffic flow, reduces stop time, and is an efficient and economic method for handling heavy traffic volume.

Introduction

The text describes a smart traffic management system designed to reduce urban congestion caused by increasing vehicle density and inefficient fixed-time traffic signals. Traditional systems often fail to adapt to real-time traffic conditions, leading to unnecessary delays. To address this, the proposed system uses vehicle detection and an ESP32-based embedded controller to dynamically adjust traffic signal timings based on actual lane traffic.

The system also includes features such as a web interface for real-time monitoring and an emergency RF-based control mechanism to prioritize emergency vehicles. Unlike conventional methods, the signal changes are adaptive and respond to current traffic flow conditions.

The literature review highlights limitations in existing systems: fixed-time signals are inefficient, sensor-based systems struggle with accuracy, and while vision-based and IoT-based systems offer improvements, they are often not fully integrated into a unified solution.

The proposed project aims to build a three-lane adaptive traffic system that analyzes traffic density using prerecorded video, controls signals using ESP32, displays system information on an OLED screen, and provides real-time updates through a web interface. The system dynamically adjusts signal timing—giving longer green lights to busier lanes and faster switching when congestion increases.

Methodologically, the system uses video input for vehicle detection and density estimation on a computer, which is then sent to the ESP32 microcontroller for signal control. All components, including detection, signal control, emergency handling, and web monitoring, are integrated into a single coordinated system.

Conclusion

A single idea stands out when watching cars move through a busy three-way crossing: timing matters. Instead of fixed lights, changes happen as needed, shaped by what the cameras see. Video clips recorded ahead of time help spot each vehicle. From there, signals shift based on flow, not schedules. An ESP32 chip runs the decisions, making adjustments live. Rules aren’t static - they bend slightly with every passing minute.Right off, the online display lets everyone see what is happening at once. Not only that, radio signals give urgent transport a clear path when seconds matter. This setup runs without hiccups most days. The proposed system reduces waiting time and improves traffic efficiency

References

[1] S. Sharma and A. Kumar, “Smart traffic management system using IoT,” International Journal of Engineering Research, vol. 7, no. 4, pp. 123–128, 2020. [2] M. Patel, R. Shah, and P. Mehta, “Vehicle detection and traffic control using image processing,” Proceedings of IEEE Conference on Intelligent Systems, pp. 45–50, 2019. [3] Espressif Systems, “ESP32 Technical Reference Manual,” 2023. [Online]. Available: https://www.espressif.com [4] R. Singh and P. Verma, “RF based wireless communication system,” International Journal of Electronics and Communication, vol. 6, no. 2, pp. 78–82, 2018. [5] Jain and S. Gupta, “Real-time traffic monitoring using computer vision,” International Journal of Computer Applications, vol. 182, no. 12, pp. 15–20, 2021. [6] K. Lee and J. Park, “IoT-based smart traffic light control system,” IEEE Access, vol. 8, pp. 123456–123465, 2020.

Copyright

Copyright © 2026 Dhairya Bhoyar, Kaustubh Raut, Tejas Bawane, Om Datke, Prof.(Mrs.) P.J. Suryawanshi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.