A machine learning approach to infer on-street parking occupancy based on parking meter transactions

Cruising for parking is not only stressful task for most drivers but also increases congestion and emissions. Therefore smart parking guidance systems are gaining increasing interest from researchers and city councils. These systems mostly rely on expensive and not well scalable technology like real time parking sensors or camera systems. In this paper we propose a deep learning architecture that predicts the current number of parking cars at different locations based on digital meter payment transactions. We outperform simple baseline models as well as a state of the art probabilistic approach from the literature. Transactional data does not directly translate to parking occupancy since not all people stick to their paid duration or pay at all. We therefore discuss the reliability of our method on different datasets and spatial granularities. Although our model is not as reliable as sensor data, especially for small parking zones, we find that our methodology provides an inexpensive way of inferring on-street parking occupancy and enable meaningful smart parking services.