Published December 1, 2020 | Version Accepted pre-print
Journal article Open

Predicting parking occupancy via machine learning in the web of things

  • 1. Dept. of Computer Science University of Twente, Enschede, The Netherlands
  • 2. Dept. of Computer Science University of Twente, Enschede, The Netherlands Research Centre on Interactive Media Smart Systems and Emerging Technologies (RISE) Nicosia, Cyprus
  • 3. Centre of Transport Studies University of Twente, Enschede, The Netherlands
  • 4. DAT.Mobility, Deventer, The Netherlands


The Web of Things (WoT) enables information
gathered by sensors deployed in urban environments to be easily
shared utilizing open Web standards and semantic technologies,
creating easier integration with other Web-based information,
towards advanced knowledge. Besides WoT, an essential aspect
of understanding dynamic urban systems is artificial intelligence
(AI). Via AI, data produced byWoT-enabled sensory observations
can be analyzed and transformed into meaningful information,
which describes and predicts current and future situations in
time and space. This paper examines the impact of WoT and
AI in smart cities, considering a real-world problem, the one of
predicting parking availability. Traffic cameras are used as WoT
sensors, together with weather forecasting Web services. Machine
learning (ML) is employed for AI analysis, using predictive
models based on neural networks and random forests. The
performance of the ML models for the prediction of parking
occupancy is better than the state of the art work in the problem
under study, scoring an MSE of 7.18 at a time horizon of 60


This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.



Additional details


RISE – Research Center on Interactive Media, Smart System and Emerging Technologies 739578
European Commission