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Computer Vision for Intelligent Traffic Monitoring and Control
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Automation
Abstract
Urban traffic congestion remains a critical challenge, driven by increasing vehicle numbers and limitations in state of art traffic control systems. Conventional traffic management approaches often lack the flexibility to respond dynamically to real-time conditions, leading to increased congestion, greater energy consumption, and prolonged travel times. To address this, this paper proposes an Intelligent Traffic System (ITS) that leverages computer vision to enable real-time traffic control. The system uses Closed Circuit Television (CCTV) cameras strategically placed at traffic intersections to capture live streams, from which vehicle counts in each lane are determined through image processing techniques. Based on these counts, adaptive signal timings are allocated to optimize traffic flow. The results show that the proposed system saved 75.42% of the commute time as compared to a conventional traffic system. This approach not only enhances traffic fluidity but also paves the way for more responsive and efficient urban traffic management infrastructure.
Keywords
Computer Vision (CV), Dynamic Traffic Management, Intelligent Traffic System (ITS), Vehicle Detection.
How to cite this paper
@article{1706535,
author = {Obi-Obuoha Abiamamela, Rizama Victor Samuel},
title = {Computer Vision for Intelligent Traffic Monitoring and Control},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {5},
pages = {392-405},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706535.pdf},
abstract = {Urban traffic congestion remains a critical challenge, driven by increasing vehicle numbers and limitations in state of art traffic control systems. Conventional traffic management approaches often lack the flexibility to respond dynamically to real-time conditions, leading to increased congestion, greater energy consumption, and prolonged travel times. To address this, this paper proposes an Intelligent Traffic System (ITS) that leverages computer vision to enable real-time traffic control. The system uses Closed Circuit Television (CCTV) cameras strategically placed at traffic intersections to capture live streams, from which vehicle counts in each lane are determined through image processing techniques. Based on these counts, adaptive signal timings are allocated to optimize traffic flow. The results show that the proposed system saved 75.42% of the commute time as compared to a conventional traffic system. This approach not only enhances traffic fluidity but also paves the way for more responsive and efficient urban traffic management infrastructure.},
keywords = {Computer Vision (CV), Dynamic Traffic Management, Intelligent Traffic System (ITS), Vehicle Detection.},
month = {November},
}