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This study delves into the integration of machine learning (ML) and artificial intelligence (AI) techniques within Internet of Things (IoT)-enabled smart parking systems to optimize urban parking management. The purpose of this research is to enhance operational efficiency, alleviate congestion, and elevate user experience in urban environments. Methodologically, we conducted an extensive review and analysis of various data sources, ML algorithms, and AI technologies employed in smart parking infrastructures. Our investigation reveals that the convergence of IoT, ML, and AI enables intelligent data collection, analysis, and decision-making processes. By leveraging real-time and historical parking data, ML algorithms predict parking availability, optimize space utilization, and dynamically adjust pricing strategies. AI-powered systems enhance user experience through personalized recommendations, seamless navigation, and proactive maintenance. Furthermore, IoT sensors and connectivity enable remote monitoring, predictive maintenance, and efficient resource allocation, contributing to sustainable urban development. Results from case studies and simulations demonstrate significant improvements in parking efficiency, reduction in traffic congestion, and enhancement of overall urban mobility. Moreover, the integration of ML and AI fosters adaptive learning systems capable of continuously optimizing parking operations based on evolving user preferences and environmental conditions. This research underscores the pivotal role of technology-driven innovations in addressing the challenges of urbanization and fostering sustainable urban development. As smart parking systems continue to evolve and integrate advanced technologies, they offer promising solutions to enhance not only parking management but also overall urban mobility, paving the way for smarter, more efficient cities of the future. Recommendations include further exploration of advanced machine learning models and the development of standardized protocols for interoperability among IoT devices. Additional data on user acceptance and system scalability are essential for comprehensive evaluation.

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Integrating Machine Learning and AI into IoT-Enabled Smart Parking

  • 会议论文

  • 发表日期：

    2025年

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  • Vesna Knights,
  • Olivera Petrovska,
  • Marija Prchkovska

摘要

This study delves into the integration of machine learning (ML) and artificial intelligence (AI) techniques within Internet of Things (IoT)-enabled smart parking systems to optimize urban parking management. The purpose of this research is to enhance operational efficiency, alleviate congestion, and elevate user experience in urban environments. Methodologically, we conducted an extensive review and analysis of various data sources, ML algorithms, and AI technologies employed in smart parking infrastructures. Our investigation reveals that the convergence of IoT, ML, and AI enables intelligent data collection, analysis, and decision-making processes. By leveraging real-time and historical parking data, ML algorithms predict parking availability, optimize space utilization, and dynamically adjust pricing strategies. AI-powered systems enhance user experience through personalized recommendations, seamless navigation, and proactive maintenance. Furthermore, IoT sensors and connectivity enable remote monitoring, predictive maintenance, and efficient resource allocation, contributing to sustainable urban development. Results from case studies and simulations demonstrate significant improvements in parking efficiency, reduction in traffic congestion, and enhancement of overall urban mobility. Moreover, the integration of ML and AI fosters adaptive learning systems capable of continuously optimizing parking operations based on evolving user preferences and environmental conditions. This research underscores the pivotal role of technology-driven innovations in addressing the challenges of urbanization and fostering sustainable urban development. As smart parking systems continue to evolve and integrate advanced technologies, they offer promising solutions to enhance not only parking management but also overall urban mobility, paving the way for smarter, more efficient cities of the future. Recommendations include further exploration of advanced machine learning models and the development of standardized protocols for interoperability among IoT devices. Additional data on user acceptance and system scalability are essential for comprehensive evaluation.