On automatic extraction of on-street parking spaces using park-out events data
This addresses the need for efficient urban parking management, though it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of automatically mapping on-street parking spaces using car sharing park-out events data, achieving a classification accuracy of 91.6% on imbalanced data in a Berlin neighborhood.
This article proposes two different approaches to automatically create a map for valid on-street car parking spaces. For this, we use car sharing park-out events data. The first one uses spatial aggregation and the second a machine learning algorithm. For the former, we chose rasterization and road sectioning; for the latter we chose decision trees. We compare the results of these approaches and discuss their advantages and disadvantages. Furthermore, we show our results for a neighborhood in the city of Berlin and report a classification accuracy of 91.6\% on the original imbalanced data. Finally, we discuss further work; from gathering more data over a longer period of time to fitting spatial Gaussian densities to the data and the usage of apps for manual validation and annotation of parking spaces to improve ground truth data.