Towards a continuous forecasting mechanism of parking occupancy in urban environments

Searching for an available parking space is a stressful and time-consuming task, which leads to increasing traffic and environmental pollution due to the emission of gases. To solve these issues, various solutions relying on information technologies (e.g., wireless networks, sensors, etc.) have been deployed over the last years to help drivers identify available parking spaces. Several recent works have also considered the use of historical data about parking availability and applied learning techniques (e.g., machine learning, deep learning) to estimate the occupancy rates in the near future. In this paper, we not only focus on training forecasting models for different types of parking lots to provide the best accuracy, but also consider the deployment of such a service in real conditions, to solve actual parking occupancy problems. It is therefore needed to continuously provide accurate information to the drivers but also to handle the frequent updates of parking occupancy data. The underlying challenges addressed in the present work so concern (1) the self-tuning of the forecasting model hyper-parameters according to the characteristics of the considered parking lots and (2) the need to maintain the performance of the forecasting model over time. To demonstrate the effectiveness of our approach, we present in the paper several evaluations using real data provided for different parking lots by the city of Lille in France. The results of these evaluations highlight the accuracy of the forecasts and the ability of our solution to maintain model performance over time.