Transferring knowledge from monitored to unmonitored areas for forecasting parking spaces
This work addresses the high cost of sensor deployment for parking management in smart cities, offering a solution to scale monitoring incrementally.
The paper tackles the problem of forecasting parking space availability in smart cities by transferring knowledge from monitored to unmonitored areas, using geographic information system data to determine neighborhood similarity and adapt occupancy rates, resulting in an extension of estimation capabilities without requiring additional sensors.
Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking. The current results are promising, indeed. However, existing approaches are limited by the high cost of sensors that need to be installed throughout the city in order to achieve an accurate estimation. This work investigates the extension of estimating parking information from areas equipped with sensors to areas where they are missing. To this end, the similarity between city neighborhoods is determined based on background data, i.e., from geographic information systems. Using the derived similarity values, we analyze the adaptation of occupancy rates from monitored- to unmonitored parking areas.