SMART TRAFFIC MANAGEMENT SYSTEM

This project aims to develop an innovative Smart Traffic Management System that combines an Arduino-based platform with an IP camera and object detection algorithms for efficient traffic monitoring and control. The system enhances traffic flow by detecting various vehicle types such as cars, bikes, and trucks, and predicting traffic wait times using real- time vehicle counts. The system's core utilizes the YOLO (You Only Look Once) algorithm, a cutting-edge real-time object detection model, for vehicle identification and classification. The system uses an IP camera installed at traffic intersections to capture live video feeds. These feeds are processed with the YOLO algorithm to identify and classify vehicles in real-time, differentiating between cars, bikes, and trucks. The high speed and accuracy of the YOLO algorithm enable minimal latency, ensuring the traffic data remains current. The Arduino platform is central to processing data from the IP camera and controlling the system's components. After detecting objects, the system counts and categorizes vehicles. This data is used to predict traffic wait times, which are displayed on LED boards at the intersection. These predictions inform drivers of expected wait times, helping to reduce unnecessary idling and improve traffic flow. Additionally, the system uses LED lights to communicate traffic conditions visually. Different colors indicate various levels of congestion, giving drivers an immediate understanding of the traffic situation. For instance, green may signify low congestion, while red indicates heavy traffic, helping drivers make informed decisions. This project integrates computer vision, real-time data processing, and embedded systems to create a comprehensive solution for modern urban traffic management. By utilizing the YOLO algorithm for vehicle detection and traffic prediction, the system aims to enhance traffic efficiency, reduce congestion, and improve the overall driving experience. The use of Arduino and LED components ensures a cost-effective, scalable solution suitable for various urban environments.

Authors

Publication Details

Journal
International Journal on Science and Technology
Published
2025-02-12
DOI
https://doi.org/10.71097/ijsat.v16.i1.1882
Primary Topic
IoT and GPS-based Vehicle Safety Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

SMART TRAFFIC MANAGEMENT SYSTEM

S - Drushya, M P - Anush, B P - Sunil
International Journal on Science and Technology
IoT and GPS-based Vehicle Safety Systems
article

SMART TRAFFIC MANAGEMENT SYSTEM

S - Drushya, M P - Anush, B P - Sunil
article en

Abstract

This project aims to develop an innovative Smart Traffic Management System that combines an Arduino-based platform with an IP camera and object detection algorithms for efficient traffic monitoring and control. The system enhances traffic flow by detecting various vehicle types such as cars, bikes, and trucks, and predicting traffic wait times using real- time vehicle counts. The system's core utilizes the YOLO (You Only Look Once) algorithm, a cutting-edge real-time object detection model, for vehicle identification and classification. The system uses an IP camera installed at traffic intersections to capture live video feeds. These feeds are processed with the YOLO algorithm to identify and classify vehicles in real-time, differentiating between cars, bikes, and trucks. The high speed and accuracy of the YOLO algorithm enable minimal latency, ensuring the traffic data remains current. The Arduino platform is central to processing data from the IP camera and controlling the system's components. After detecting objects, the system counts and categorizes vehicles. This data is used to predict traffic wait times, which are displayed on LED boards at the intersection. These predictions inform drivers of expected wait times, helping to reduce unnecessary idling and improve traffic flow. Additionally, the system uses LED lights to communicate traffic conditions visually. Different colors indicate various levels of congestion, giving drivers an immediate understanding of the traffic situation. For instance, green may signify low congestion, while red indicates heavy traffic, helping drivers make informed decisions. This project integrates computer vision, real-time data processing, and embedded systems to create a comprehensive solution for modern urban traffic management. By utilizing the YOLO algorithm for vehicle detection and traffic prediction, the system aims to enhance traffic efficiency, reduce congestion, and improve the overall driving experience. The use of Arduino and LED components ensures a cost-effective, scalable solution suitable for various urban environments.

International Journal on Science and TechnologyVol. 16(1)
Openalex Percentile: Top 97%
IoT and GPS-based Vehicle Safety Systems
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SMART TRAFFIC MANAGEMENT SYSTEM — S - Drushya, M P - Anush, et al. · International Journal on Science and Technology (2025) | TGRS Research Map | TGRS