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Smart cities around the world have begun monitoring parking areas in order toestimate available parking spots and help drivers looking for parking. Thecurrent results are promising, indeed. However, existing approaches are limitedby the high cost of sensors that need to be installed throughout the city inorder to achieve an accurate estimation. This work investigates the extensionof estimating parking information from areas equipped with sensors to areaswhere they are missing. To this end, the similarity between city neighborhoodsis determined based on background data, i.e., from geographic informationsystems. Using the derived similarity values, we analyze the adaptation ofoccupancy rates from monitored- to unmonitored parking areas.

Transferring knowledge from monitored to unmonitored areas for forecasting parking spaces