Transferring Knowledge from Monitored to Unmonitored Areas for Forecasting Parking Spaces

Andrei Ionita, André Pomp, Michael Cochez, Tobias Meisen, Stefan Decker

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Article number1960003
JournalInternational Journal on Artificial Intelligence Tools
Volume28
Issue number6
DOIs
Publication statusPublished - 1 Sep 2019
Externally publishedYes

Keywords

  • data mining
  • machine learning
  • semantic annotation
  • Smart parking

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