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@article{172534,
        author = {Aishwarya Santosh Auti and Shradha Rajesh Arsewar and Rutuja Vinayak Aware and Chitra Ravindra Ragit},
        title = {Optimized Smart Parking System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {158-161},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172534},
        abstract = {Urbanization has exacerbated parking Efficiencies, leading to increased traffic congestion, time wastage, and higher emissions as drivers search for available spots. This paper presents an Optimized Smart Parking System that leverages real-time data, dynamic pricing, and AI-driven algorithms to streamline parking management. The system features a user-friendly mobile application developed with Flutter, enabling users to search, book, and navigate to parking spots seamlessly. By utilizing GPS technology for real-time tracking and implementing a dynamic pricing model based on demand and location, the system maximizes parking space utilization and optimizes revenue for parking operators. Furthermore, machine learning algorithms analyze historical data to predict parking availability, enhancing user experience. The integration of geofencing ensures timely notifications when users enter or exit parking areas, contributing to efficient space management. Overall, this project provides a cost-effective, scalable, and environmentally sustainable solution to modern urban parking challenges.},
        keywords = {Smart Parking System, Urban Mobility, Dynamic Pricing, Real-Time Data, Machine Learning, GPS Navigation.},
        month = {January},
        }