Paper Title
Intelligent Traffic Signal Management using Raspberry Pi and OpenCV
Article Identifiers
: Keywords
Raspberry Pi, OpenCV, Traffic Control, Realtime monitoring, LED lights, Traffic Density, Vehicle Detection, and Traffic Signals.
Abstract
Traffic congestion is a prevalent problem in urban areas, leading to increased travel times, fuel consumption, and environmental pollution. The Smart Traffic Management System project aims to address these big challenges by leveraging advanced technologies such as computer vision, Internet of Things (IoT), and machine learning to optimize traffic flow, enhance road safety, and improve overall transportation efficiency. The project focuses on real-time vehicle detection, traffic analysis, and intelligent traffic signal control to dynamically adapt to changing traffic conditions and alleviate congestion at some key intersections. By integrating data from traffic sensors, cameras, and IoT devices into a centralized management platform, the system provides actionable insights for some traffic management authorities to make data-driven decisions and implement proactive measures for congestion mitigation. Through the deployment of smart traffic solutions and the utilization of some data analytics techniques, the Traffic Management System project seeks to somewhat transform urban mobility, reduce travel times, and enhance the quality of life for residents in metropolitan areas.
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How To Cite (APA)
J. Divya Madhuri, Dr. Ch. Rambabu, M. Vamsi, D. Tharun Nayak, & L. Pavan Prasad (April-2024). Intelligent Traffic Signal Management using Raspberry Pi and OpenCV. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), d1-d8. https://ijnrd.org/papers/IJNRD2404301.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : d1-d8
Other Publication Details
Paper Reg. ID: IJNRD_216926
Published Paper Id: IJNRD2404301
Downloads: 000122087
Research Area: Electronics & Communication Engg.Â
Author Type: Indian Author
Country: Machilipatnam, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404301.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404301
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