# Revolutionizing Traffic Management with AI-Powered Machine Vision: A Step Toward Smart Cities

arXiv: https://arxiv.org/abs/2503.02967 · HTML: https://mlsanity.com/paper/2503.02967/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Densely-Populated Traffic Detection using YOLOv5 and Non-Maximum Suppression Ensembling](https://mlsanity.com/paper/2108.12118/) (2021)
- [Evaluating Computer Vision Techniques for Urban Mobility on Large-Scale, Unconstrained Roads](https://mlsanity.com/paper/2109.05226/) (2021)
- [AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities](https://mlsanity.com/paper/2307.12128/) (2023)
- [Turning Traffic Monitoring Cameras into Intelligent Sensors for Traffic Density Estimation](https://mlsanity.com/paper/2111.00941/) (2021)

## Abstract (arXiv)

The rapid urbanization of cities and increasing vehicular congestion have posed significant challenges to traffic management and safety. This study explores the transformative potential of artificial intelligence (AI) and machine vision technologies in revolutionizing traffic systems. By leveraging advanced surveillance cameras and deep learning algorithms, this research proposes a system for real-time detection of vehicles, traffic anomalies, and driver behaviors. The system integrates geospatial and weather data to adapt dynamically to environmental conditions, ensuring robust performance in diverse scenarios. Using YOLOv8 and YOLOv11 models, the study achieves high accuracy in vehicle detection and anomaly recognition, optimizing traffic flow and enhancing road safety. These findings contribute to the development of intelligent traffic management solutions and align with the vision of creating smart cities with sustainable and efficient urban infrastructure.