Traffic Vision: AI-Powered Traffic Monitoring System and Signal Optimization
This paper is focused on Traffic Vision, an integrated AI-powered traffic monitoring and management system built on computer vision, machine learning, and adaptive control strategies to optimize urban traffic flows. The major functions of the system include real-time processing of video feeds for ve...
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| Vydané v: | International journal for research in applied science and engineering technology Ročník 13; číslo 4; s. 2708 - 2715 |
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| Hlavný autor: | |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
30.04.2025
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| ISSN: | 2321-9653, 2321-9653 |
| On-line prístup: | Získať plný text |
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| Abstract | This paper is focused on Traffic Vision, an integrated AI-powered traffic monitoring and management system built on computer vision, machine learning, and adaptive control strategies to optimize urban traffic flows. The major functions of the system include real-time processing of video feeds for vehicle, pedestrian, emergency vehicle, and accident detection and tracking, as well as traffic density and flow parameters. A new adaptive traffic signal control algorithm employs this information to dynamically adapt traffic light timings according to current conditions. Experimental results show a wait time reduction of up to 30% in intersections and highly improved emergency vehicle response times. This modular architecture allows it to easily fit into any urban framework and match existing infrastructure monitoring systems. |
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| AbstractList | This paper is focused on Traffic Vision, an integrated AI-powered traffic monitoring and management system built on computer vision, machine learning, and adaptive control strategies to optimize urban traffic flows. The major functions of the system include real-time processing of video feeds for vehicle, pedestrian, emergency vehicle, and accident detection and tracking, as well as traffic density and flow parameters. A new adaptive traffic signal control algorithm employs this information to dynamically adapt traffic light timings according to current conditions. Experimental results show a wait time reduction of up to 30% in intersections and highly improved emergency vehicle response times. This modular architecture allows it to easily fit into any urban framework and match existing infrastructure monitoring systems. |
| Author | M, Prabu |
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| Snippet | This paper is focused on Traffic Vision, an integrated AI-powered traffic monitoring and management system built on computer vision, machine learning, and... |
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| Title | Traffic Vision: AI-Powered Traffic Monitoring System and Signal Optimization |
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