AI-Based Urban Monitoring for Traffic Violations and Its Impact on Municipal Solid Waste Management Efficiency

Abstract

Efficient municipal solid waste (MSW) management in rapidly urbanizing cities is increasingly challenged by traffic congestion and illegal parking, which obstruct
waste collection routes and lead to delays in service delivery. This study proposes an AI-based urban monitoring framework that leverages a lightweight YOLO
(You Only Look Once) deep learning model for real-time detection of traffic violations, particularly illegal parking in critical urban zones. The proposed system is
designed for deployment in resource-constrained environments, ensuring low computational overhead while maintaining high detection accuracy.
An extensive experimental evaluation was conducted utilizing urban traffic datasets, wherein the model exhibited enhanced inference speed and competitive
precision relative to traditional object detection methodologies. The study further explores the indirect effects of traffic violations on municipal solid waste (MSW)
management efficiency by examining waste collection delays, route obstructions, and accumulation patterns in impacted areas. The findings suggest that the
integration of real-time traffic violation detection with municipal waste management systems can substantially improve route optimization, decrease collection
delays, and enhance overall urban sanitation.
The findings highlight the potential of combining computer vision and intelligent urban infrastructure to support sustainable waste management practices. This
research contributes to the development of smart city solutions by bridging the gap between traffic monitoring and MSW management through an efficient, scalable,
and cost-effective AI-driven approach.