Smart Traffic Management System for Reducing Urban Congestion in Major Indonesian Cities Using IOT and AI Technologies

Michael Thobie Rahadian Kartono (Universitas Udayana (Unud))
Nuvia Kurnia Sari (Universitas Udayana (Unud))
Andi Trio Suroso (Universitas Udayana (Unud))

Proceeding of The International Conferences on Engineering Sciences
Vol. 2 No. 1 (2025): January : Proceeding of the International Conferences on Engineering Sciences

Urban traffic congestion is a growing problem in Indonesian cities, affecting economic productivity and quality of life. This research explores the development of a smart traffic management system utilizing Internet of Things (IoT) sensors and artificial intelligence (AI) algorithms to analyze traffic patterns and optimize flow. The proposed system collects real-time data and uses predictive analytics to adjust traffic signals dynamically. Field tests in Jakarta demonstrate a 15% improvement in traffic flow and reduced travel times during peak hours. The findings suggest significant potential for scalable smart city solutions in urban traffic management across Indonesia.