Smart Park - A Predictive and User Centric Parking Platform

Traditional city parking infrastructures cannot adapt to the demands of modern motorists, resulting in congestion, delays, and increased emissions. This project aims to optimize parking space utilization and user experience by employing machine learning algorithms. Based on historical data as well as real-time user-updated information, the system predicts parking availability and enables optimal parking management without extensive-scale IoT infrastructure. The app utilizes Firebase for a scalable and secure database and authentication mechanism, and Google Maps integration provides real-time navigation assistance for better parking directions. Flutter ensures cross-platform compatibility, making it possible for both Android and iOS users to have an intuitive and smooth UI/UX. The primary features include advance booking of parking lots, the possibility of time extension, and slot availability based on the hour, granting flexibility to users. A secure reservation system prevents double booking, and GPS-based navigation simplifies finding a parking space in an easy and convenient way. By preventing the use of costly hardware installations, this cost-effective, scalable, and eco-friendly solution enables sustainable urban mobility by reducing emissions and improving the overall parking experience.

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Publication Details

Published
2025-04-24
DOI
https://doi.org/10.1109/icoei65986.2025.11013724
Primary Topic
Smart Parking Systems Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Smart Park - A Predictive and User Centric Parking Platform

Nils C. Ger­hardt, G. S. V. Radha Krishna Rao, S Swathi, S Devasenan et al.
Smart Parking Systems Research
article

Smart Park - A Predictive and User Centric Parking Platform

Nils C. Ger­hardt, G. S. V. Radha Krishna Rao, S Swathi, S Devasenan, M Rathakrishnan
article en

Abstract

Traditional city parking infrastructures cannot adapt to the demands of modern motorists, resulting in congestion, delays, and increased emissions. This project aims to optimize parking space utilization and user experience by employing machine learning algorithms. Based on historical data as well as real-time user-updated information, the system predicts parking availability and enables optimal parking management without extensive-scale IoT infrastructure. The app utilizes Firebase for a scalable and secure database and authentication mechanism, and Google Maps integration provides real-time navigation assistance for better parking directions. Flutter ensures cross-platform compatibility, making it possible for both Android and iOS users to have an intuitive and smooth UI/UX. The primary features include advance booking of parking lots, the possibility of time extension, and slot availability based on the hour, granting flexibility to users. A secure reservation system prevents double booking, and GPS-based navigation simplifies finding a parking space in an easy and convenient way. By preventing the use of costly hardware installations, this cost-effective, scalable, and eco-friendly solution enables sustainable urban mobility by reducing emissions and improving the overall parking experience.

Karpagam Academy of Higher Education (IN)
Sustainable cities and communities
Openalex Percentile: Top 88%
Smart Parking Systems Research
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