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Predicting Parking Demand with Open Data

Abstract : This paper focuses on demand forecasts for parking facilities. Our work utilizes open parking data for predictions. Several cities in Europe already publish this data continuously in the standardized DATEX II format. Traffic related information will become more ubiquitous in the future as all EU-member states must implement real-time traffic information services including parking status data since July 2017 implementing the EU directives 2010/40 and 2015/962. We demonstrate how to extract reliable and easily comprehensible forecast models for future-parking demand based on open data. These models find multiple use cases not only on a business planning level and for financial revenue forecasting but also to make traffic information systems more resilient to outages and to improve routing of drivers directing them to parking facilities with availability upon predicted arrival. Our approach takes into consideration that the data constitutes irregular time series and incorporates contextual information into the predictive models to obtain higher precision forecasts.
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https://hal.inria.fr/hal-02510141
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Submitted on : Tuesday, March 17, 2020 - 2:55:59 PM
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Thomas Schuster, Raphael Volz. Predicting Parking Demand with Open Data. 18th Conference on e-Business, e-Services and e-Society (I3E), Sep 2019, Trondheim, Norway. pp.218-229, ⟨10.1007/978-3-030-29374-1_18⟩. ⟨hal-02510141⟩

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