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.
@article{arxiv.1908.03629,
title = {Transferring knowledge from monitored to unmonitored areas for forecasting parking spaces},
author = {Andrei Ionita and André Pomp and Michael Cochez and Tobias Meisen and Stefan Decker},
journal= {arXiv preprint arXiv:1908.03629},
year = {2019}
}
Comments
Preprint of an article to be published in Int J. on Artificial Intelligence Tools (IJAIT)