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Smart city article details
| Title | Ai-Powered Hybrid Smart Parking: Optimizing Parking Management Across Diverse Applications In Smart Cities |
|---|---|
| ID_Doc | 7088 |
| Authors | Shalini M.K.; Hanumanthappa J.; Santhosh Kumar K.S.; Shiva Prakash S.P. |
| Year | 2025 |
| Published | Procedia Computer Science, 258 |
| DOI | http://dx.doi.org/10.1016/j.procs.2025.04.385 |
| Abstract | An Artificial Intelligence (AI) powered hybrid smart parking system optimizes parking allocation across various applications, including smart hospitals, colleges, offices, and shopping malls. The system uses AI and IoT technologies to enhance the user experience, streamline operations, and improve efficiency. It dynamically allocates parking spaces based on real-time demand, user preferences, and contextual factors. The system accurately predicts parking demand, optimizes space allocation and provides personalized recommendations, reducing congestion and waiting times. The hybrid smart parking algorithm combines machine learning techniques with domain-specific insights to prioritize parking allocation in diverse environments. The study emphasizes the importance of leveraging advanced technologies to address complex urban challenges, such as parking management, and aims to pave the way for sustainable, efficient, and user-centric parking solutions in smart cities. and Random Forest with an overall average score of 0.9400. Machine learning has become crucial for optimizing parking management systems, especially in densely populated cities. To address this challenge, advanced predictive models have been developed to anticipate parking duration based on slot availability, peak hours, and traffic conditions. The Random Forest model outperforms Logistic Regression, Random Forest, and K-Nearest Neighbors in predicting parking length, achieving high accuracy and performance metrics. It maintains user satisfaction and low operational costs, making it a recommended system for further implementation in parking management tasks. The classification report shows a respectable performance with 50% accuracy and a good recall for open slots. The optimization procedure was effective and did not reveal any notable areas for improvement. The optimized route (Slot2, Slot1, Slot3) provides an efficient parking sequence, with high accuracy (95%), good user satisfaction (90%) and suitability for use in smart parking situations. © 2025 The Author(s). |
| Author Keywords | Artificial Intelligence; Machine Learning; Smart Applications; Smart Parking: Smart city |
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