A Smart City Traffic and Congestion Management System Using Machine Learning

Abstract

A thriving transportation system is one of the most discussed issues at the onset of the twenty-first century,
representing one of the many global challenges. Technology is essential in addressing contemporary transportation
challenges as smart cities (SC) develop. Smart cities represent a modern evolution of civilization globally, utilizing
advancing technologies, including machine learning, to manage urban initiatives and tackle urban difficulties. Traffic
is a significant obstacle in urban development due to multiple factors, including inadequate planning. The creative
strategies, demonstrated by integrating shopping and delivery services in SC, will mitigate traffic congestion issues.
This research analysis identifies the concepts of Actor-Network Theory (ANT) and pragmatic theory as foundational
research and integration improvement techniques. The study employed data-gathering methods such as interviews,
states, and media coverage, which are crucial for comprehending the intricate connections of brilliant city efforts. The
data analysis illustrates that the ANT and practical framework underpin the development of future urban environments.