Smart Parking and Traffic Management System

Abstract

— Urban centers worldwide continue to struggle with increasing traffic congestion, limited parking spaces, and rising air pollution. These issues lead to longer travel times, wasted fuel, and stress for commuters. To address these challenges, this paper proposes a Smart Parking and Traffic Management System (SPTMS) that integrates Internet of Things (IoT) sensors, machine learning, and real-time analytics. The system monitors parking areas, predicts slot availability, and provides route guidance through mobile and web applications. Users receive live updates on parking status and traffic flow, reducing waiting time and unnecessary vehicle movement.
For administrators, a cloud-based dashboard displays real-time data on parking utilization and traffic conditions, helping with planning and policy decisions. The prototype, developed using Python, JavaScript, OpenCV, and cloud services, ensures scalability and responsiveness. Experimental results show improved parking efficiency, reduced congestion, and enhanced commuter convenience. The study demonstrates how IoT and analytics can promote sustainable urban mobility. Future extensions may include predictive maintenance, adaptive traffic control, and integration with larger smart city ecosystems to further support efficient and eco-friendly transportation management.