Characterizing parking systems from sensor data through a data-driven approach

ABSTRACT

Nowadays, urban traffic affects the quality of life in cities as the problem becomes even more exacerbated by parking issues: congestion increases due to drivers searching slots to park. An Internet of Things approach permits drivers to know the parking availability in real time and provides data that can be used to develop predictive models. This can be useful in improving the management of parking areas while having an important effect on traffic. This work begins by describing the state-of-the-art parking predictive models and, then, introduces the recurrent neural network methods that were used Long Short-Term Memory and Gated Recurrent Unit, as well as the models developed according to real scenarios in Wattens and Los Angeles. To improve the quality of the models, exogenous variables related to weather and calendar are considered. Finally, the results are described, followed by suggestions for future research.

KEYWORDS:

• Parking availability forecast
• deep learning
• smart cities
• recurrent models
• time series

Acknowledgments

Throughout this work, the authors have benefitted from the support of the inLab FIB team at Universitat Politècnica de Catalunya and the company Worldsensing S.L.

Data availability

The parking data used to support the findings of this study were supplied by Worldsensing S.L. under license and so cannot be made freely available. Requests for access to these data should be made to Jamie Arjona Martínez, e-mail: [email protected].

The calendar and weather data used to support the findings of this study are available from the corresponding author upon request.

Disclosure statement

The authors declare that there is no conflict of interest regarding the publication of this paper.

Additional information

Funding

This research was funded by Secretaria d’Universitats i Recerca de la Generalitat de Catalunya [2017-SGR-1749] and under the Industrial Doctorate Program [2016-DI-79];Generalitat de Catalunya – Industrial Doctoral Program [2016-DI-79];Secretaria d’Universitats i Recerca de la Generalitat de Catalunya [2017-SGR-1749];