Term 252

Smart Parking availability & Prediction system for Campus

Project Type: Self-Initiated

Project Description

The Smart Parking Availability and Prediction System is an intelligent, IoT-based solution designed to address the persistent challenge of limited and inefficient parking on university campuses. Traditional parking systems often lack real-time visibility and predictive capabilities, leading to increased search time, traffic congestion, fuel consumption, and user frustration. This project aims to overcome these limitations by integrating advanced technologies to deliver a more efficient and user-centered parking experience. The system utilizes a computer vision-based approach, where cameras connected to Raspberry Pi units continuously monitor parking areas. Video streams are processed using a YOLO-based deep learning model deployed on cloud infrastructure to accurately detect parking occupancy and identify violations in real time. The processed data is transmitted to a cloud backend built on Firebase and Google Cloud services, ensuring real-time synchronization, reliable storage, and scalable data processing. In addition to real-time monitoring, the system incorporates predictive analytics to forecast parking availability using historical data, time-based patterns, and campus activity schedules. Industrial and Systems Engineering techniques such as time-study analysis and discrete-event simulation are applied to estimate walking distances, parking search time, and system performance under varying conditions. These predictive insights enable users to make informed decisions before arriving at parking areas. The user interface is delivered through a cross-platform mobile application developed using Flutter, providing users with live parking availability, predictive recommendations, and navigation support. An administrative interface is also included to monitor violations, manage parking facilities, and analyze usage trends. Overall, the project presents a multidisciplinary to enhance parking efficiency, reduce congestion, and improve the overall campus mobility experience.