Intelligent Traffic Management Systems: Case Studies and Real-World Applications

This paper presents a real-time Intelligent Traffic Management System (ITMS) designed for urban smart cities with high congestion, limited human resources, and rising vehicle density. The system integrates AI/ML algorithms, OpenCV, OCR, and live Google Maps API to dynamically manage traffic signals, detect violations, and prioritize emergency routing through green corridors. The modular design includes components for license plate recognition, traffic statistics, fine automation, and a centralized operator dashboard. The proposed system was tested in simulation with over 98% license plate recognition accuracy and 95% violation detection accuracy. It demonstrates significant improvements in traffic flow, safety, and operational efficiency. This work is part of a broader vision for deploying scalable, smart, and adaptive traffic systems in India’s rapidly growing urban regions.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-20
DOI
https://doi.org/10.5281/zenodo.15660352
Primary Topic
IoT and GPS-based Vehicle Safety Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Intelligent Traffic Management Systems: Case Studies and Real-World Applications

Zenodo (CERN European Organization for Nuclear Research)
IoT and GPS-based Vehicle Safety Systems
article

Intelligent Traffic Management Systems: Case Studies and Real-World Applications

article en

Abstract

This paper presents a real-time Intelligent Traffic Management System (ITMS) designed for urban smart cities with high congestion, limited human resources, and rising vehicle density. The system integrates AI/ML algorithms, OpenCV, OCR, and live Google Maps API to dynamically manage traffic signals, detect violations, and prioritize emergency routing through green corridors. The modular design includes components for license plate recognition, traffic statistics, fine automation, and a centralized operator dashboard. The proposed system was tested in simulation with over 98% license plate recognition accuracy and 95% violation detection accuracy. It demonstrates significant improvements in traffic flow, safety, and operational efficiency. This work is part of a broader vision for deploying scalable, smart, and adaptive traffic systems in India’s rapidly growing urban regions.

Zenodo (CERN European Organization for Nuclear Research)
Sustainable cities and communities
Openalex Percentile: Top 56%
IoT and GPS-based Vehicle Safety Systems
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