Automated Traffic Signal in Connected Environment

In the evolving landscape of smart cities, optimizing traffic management and enhancing road safety are essential. This paper proposes an IoT-based automated traffic signal notifier system to improve driver awareness and compliance. Using Arduino microcontrollers and sensors like IR, magnetic, and ultrasonic, the system detects vehicles within a predefined radius of traffic signals. Real-time data is processed and transmitted via IoT protocols, with notifications delivered through the Blynk application. Drivers receive timely updates on traffic signal statuses, countdowns, and delays, enabling informed decisions and reducing sudden stops, accelerations, and traffic violations. By integrating IoT technologies with traffic systems, this scalable solution enhances urban mobility, safety, and efficiency in connected vehicle environments.

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Publication Details

Published
2025-04-24
DOI
https://doi.org/10.1109/icoei65986.2025.11013733
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
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article

Automated Traffic Signal in Connected Environment

A. Karthik, S. Revathy, D Mukeshraj
Neural Networks and Applications
article

Automated Traffic Signal in Connected Environment

A. Karthik, S. Revathy, D Mukeshraj
article en

Abstract

In the evolving landscape of smart cities, optimizing traffic management and enhancing road safety are essential. This paper proposes an IoT-based automated traffic signal notifier system to improve driver awareness and compliance. Using Arduino microcontrollers and sensors like IR, magnetic, and ultrasonic, the system detects vehicles within a predefined radius of traffic signals. Real-time data is processed and transmitted via IoT protocols, with notifications delivered through the Blynk application. Drivers receive timely updates on traffic signal statuses, countdowns, and delays, enabling informed decisions and reducing sudden stops, accelerations, and traffic violations. By integrating IoT technologies with traffic systems, this scalable solution enhances urban mobility, safety, and efficiency in connected vehicle environments.

Sathyabama Institute of Science and Technology (IN)
Openalex Percentile: Top 94%
Neural Networks and Applications
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