Smart Parking Infrastructure Using Visual Analytics For Sustainable Urban Mobility

Abstract

This paper presents a smart parking infrastructure leveraging a camera-integrated Raspberry Pi system and OpenCV-based visual analytics to enhance urban mobility and promote sustainable parking practices. The system continuously monitors parking spaces, detects available slots, and identifies them using unique slot numbers, providing real-time updates to users. To ensure efficient space utilization and avoid congestion, the system dynamically restricts access to slots adjacent to oversized or improperly parked vehicles. Additionally, it maintains a comprehensive database of incoming and outgoing vehicles, parking durations, and fare calculations (if applicable). By integrating visual recognition, intelligent decision-making, and automated record-keeping, the proposed solution contributes to reduced traffic congestion, lower emissions from vehicle idling, and improved parking efficiency in smart urban environments.