Analysis of approaches to improving intelligent parking management technologies

Rapid growth in urban motorization has led to a critical shortage of parking spaces, contributing to increased congestion, higher emissions of harmful pollutants, and a decline in residents’ quality of life. This study examines contemporary methods for enhancing parking management technology, moving beyond traditional manual control and static data toward intelligent systems capable of adapting to real-time traffic dynamics and demand. The research focuses on solutions based on the Internet of Things, automated parking complexes, artificial-intelligence algorithms for occupancy forecasting, and dynamic pricing mechanisms. Methodology includes a systematic review of over forty scientific publications from 2018 to 2025, comparative analysis of technical and economic performance indicators for various technologies, SWOT analysis, and scenario modeling that incorporates social and environmental considerations. Findings indicate that deploying IoT solutions with sensor-based monitoring and mobile applications significantly reduces the average time spent searching for a parking space and corresponding CO₂ emissions. Automated parking systems deliver high vehicle density and lower operational costs, while artificial-intelligence algorithms improve the accuracy of demand forecasts. Dynamic pricing balances demand across different times of day, helping to alleviate congestion. The practical significance of this work lies in the development of recommendations for integrating these technologies into urban infrastructure and in crafting a roadmap tailored to the specific needs of Ukrainian cities. The proposed approaches can guide local authorities and investors in optimizing parking resources, enhancing urban mobility, and reducing environmental impact.