A data-driven IoT framework for real-time and predictive smart parking management in urban cities

Authors

https://doi.org/10.22105/sci.v2i2.36

Abstract

Urbanization and increasing vehicles have led to parking challenges, causing traffic congestion, pollution, and wasted time searching for parking spots. Traditional parking systems are inefficient due to the lack of real-time data. This project presents an IoT-based smart parking system that monitors and manages parking spaces in real time, optimizing space utilization and improving user convenience. The system uses IoT sensors to detect space occupancy and transmit the data to a central server. Users can access this information via a mobile app that displays available parking spots in real time. The system also includes automated entry and exit as well as digital payment options, reducing the need for manual intervention and streamlining the process. In tests, the system improved parking efficiency by 30%, reducing the time spent finding parking spots. Additionally, vehicle idling was minimized, leading to a 20% reduction in carbon emissions in congested areas. This IoT-based smart parking system provides a scalable solution to enhance urban mobility, offering a more sustainable and efficient approach to managing city parking.

Keywords:

Internet of things, Smart parking, Real-time monitoring, Urban mobility, Parking management, Sensors, Digital payment

References

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Published

2025-06-20

Issue

Vol. 2 No. 2 (2025): Smart City Insights

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Articles

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How to Cite

Saberi Najafi, H., Fischer, S., & Esdauletova, I. M. (2025). A data-driven IoT framework for real-time and predictive smart parking management in urban cities. Smart City Insights, 2(2), 77-87. https://doi.org/10.22105/sci.v2i2.36

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