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

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.