A deep learning approach to real-time parking availability prediction for smart cities

Abstract

Nowadays, urban traffic affects the quality of life in cities and metropolitan areas as the problem becomes ever more exacerbated by parking issues: congestion increases due to drivers looking for slots to park their vehicles. An Internet of Things approach permits drivers to know the parking space availability in real time through wireless networks of sensor devices. This research focuses on studying the data generated by parking systems in order to develop predictive models that generate forecasted information. This can be useful in improving the management of parking areas, especially on-street parking, while having an important effect on urban traffic. This work begins by looking at the state of the art in predictive methods based on machine learning for time series. Similar proposed solutions for parking prediction are described in terms of the technology and current state-of-the-art predictive models. This paper then introduces the recurrent neural network method that was used in this research, namely Gated Recurrent Unit, as well as the model developed according to a real scenario in the city of Riyadh. In order to improve the quality of the model, exogenous variables related with weather and calendar effects are considered, and the baseline model is compared to the models that used this extra information. Finally, the obtained results are described, followed by suggestions for future research.