IoT-Enabled Smart Traffic Management and Alert System with Green Corridor for Emergency Vehicles

Abstract

This paper presents a Smart Traffic Management Alert System (STMAS) designed to improve emergency medical response time in urban environments by leveraging Internet of Things (IOT) technology. The system addresses the critical delays ambulances face due to conventional, fixed-time traffic signal patterns that fail to adapt to real-time emergency needs. Our approach integrates GPS- enabled ambulance tracking, microcontroller-based traffic signal control, cloud-based data processing, and digital display boards to ensure prioritized passage for emergency vehicles. When an ambulance is detected within a predefined radius of an intersection, the system first activates LED display boards with warning messages to alert nearby drivers and pedestrians. Once the path is cleared, the corresponding traffic signal is switched to green in the ambulance’s direction. Tests and simulations show that the system can greatly reduce the time ambulances spend in traffic and improve coordination between emergency services and traffic control. This determining the patient’s chances of survival. Unfortunately, traditional traffic control systems, which are based on fixed-time signal patterns, lack the flexibility to adapt to real-time traffic conditions or prioritize specific vehicles. As a result, ambulances often face unnecessary delays while navigating through crowded intersections, even when their journey is time-critical. These delays can be the difference between life and death, highlighting the urgent need for an intelligent, responsive traffic management system. The advancement of the Internet of Things (IoT), cloud computing, and real-time data analytics has opened up new opportunities to address these challenges. IoT makes it possible for devices and systems to share data instantly, allowing traffic lights, sensors, and emergency vehicles to communicate project adds to the field of intelligent transportation systems (ITS) by offering an automated, scalable, and dependable solution for giving priority to emergency vehicles, which can help save more lives in critical situations.

Introduction

Urban areas are facing increased traffic congestion due to rising population and vehicle ownership, which severely affects ambulance response times in critical emergencies like heart attacks and accidents.

Traditional traffic systems based on fixed signal timings cannot prioritize ambulances, often causing life-threatening delays.

II. Solution: Smart Traffic Management Alert System (STMAS)

The proposed STMAS uses:

• GPS tracking for real-time ambulance location

• IoT-enabled microcontrollers (ESP32)

• Cloud-based data processing

• Dynamic traffic signal control

• LED display boards to alert drivers

• Backup SMS notifications to traffic personnel

???? When an ambulance enters a detection zone, the system:

1. Sends location data to the cloud server

2. Alerts the public with messages like "Ambulance Approaching – Give Way"

3. Automatically switches traffic lights to green in the ambulance's direction

III. Related Work

Various studies explored IoT and AI-driven traffic control for emergency vehicles:

• GPS-based systems reduced delay times by up to 40%

• Edge computing, machine learning, and vehicle-to-infrastructure (V2I) communication have been used

• However, public awareness mechanisms and multi-channel redundancy (e.g., display + SMS) are often missing

STMAS improves on prior systems by integrating:

• Real-time public alerts

• Redundant communication channels

• Comprehensive, scalable architecture

IV. Methodology

A. GPS Data Collection

• ESP32 microcontroller + NEO-7M GPS module

• Connects via ambulance hotspot

• Sends data using HTTP POST to server

B. Location Logging

• Stores GPS data in MySQL database

• Logs recent ambulance positions for tracking

C. Communication

• Uses Wi-Fi + HTTP GET/POST

• Static devices poll the server to get ambulance status

D. Decision-Making

• Uses Haversine Formula for accurate distance calculations between ambulance and intersections

• Initial bearing is calculated to determine direction

• If within ~25 meters, a status command is sent to the signal unit

E. Database

• Two tables:

  • gps_data (ambulance coordinates)

  • static_device (intersection location)

F. Output/Feedback

• Traffic lights:

  • All red for 5 seconds (clearance)

  • Green in ambulance's direction

• Message displayed for drivers to prepare

• All controlled by a single ESP32 per signal

V. Hardware Implementation

A. Modules

• Ambulance Unit (GPS + ESP32)

• Traffic Signal Unit (ESP32 + LED or relays)

B. Key Components

• ESP32: Controls logic, communication, signal change

• NEO-7M GPS Module: Sends real-time location data

• Traffic Signal LEDs: Red, yellow, green control

• Relay Modules: For industrial-grade signal systems

• Power Supply: Li-Po battery + DC-DC boost converter

C. Circuit Design

• Separate circuits for ambulance and traffic units

• Optocouplers ensure electrical isolation

• Supports low-voltage prototype and high-voltage real-world deployment

VI. System Workflow

1. Ambulance GPS sends data to server

2. Server identifies nearest traffic signal

3. Sends command to traffic unit

4. Signal turns green in ambulance's direction

5. Post-passage, signal reverts to normal

? Benefits:

• Faster ambulance movement

• Reduced manual control

• Enhanced road safety

VII. Future Work

1. Wider Deployment in both urban and rural areas

2. 4G/5G or Satellite Communication for better reliability

3. Hospital Integration for advance medical preparation

Conclusion

The proposed Traffic Management System for ambulances aims to reduce delays during emergencies by dynamically managing traffic signals using the real-time location and movement data of ambulances. The system integrates GPS tracking, IoT devices (such as the ESP32 microcontroller), and real-time server communication to identify the ambulance nearest to a given traffic signal. By calculating distance and direction using formulas such as Haversine distance and bearing calculation, the system sends advance control signals to change upcoming traffic lights, enabling ambulances to pass without unnecessary stops and thus improving response times. This approach minimizes human intervention, facilitating large-scale implementation in busy urban areas. Through the use of microcontrollers and internet-based APIs, quick and reliable data transfer is achieved between moving ambulances and fixed traffic devices. Integrating this solution into city traffic networks enhances the ability of emergency services to respond promptly, potentially saving lives and decreasing accident risks related to delayed medical attention. This project demonstrates how IoT and location-based technologies can transform traditional traffic systems into intelligent, responsive networks suitable for future smart city paradigms.

References

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Copyright

Copyright © 2025 Ishika Bhalla, Meet Shivhare, Prathmesh Nishane, Timothy Shandy, Ansar Sheikh. 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.