Towards automatic extraction and validation of on-street parking spaces using park-out events data

• URL: http://arxiv.org/abs/2102.06758v1
• Date: Fri, 12 Feb 2021 20:22:38 GMT
• Title: Towards automatic extraction and validation of on-street parking spaces using park-out events data
• Authors: Martin Gebert and J.-Emeterio Navarro-B
• Abstract summary: We propose two approaches to automatically create a map for valid on-street car parking spaces. The first one uses spatial aggregation and the second a machine learning algorithm. We show our results for a neighborhood in the city of Berlin and report a classification accuracy of 92% on the original imbalanced data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article proposes two different approaches to automatically create a map for valid on-street car parking spaces. For this, we use park-out events data from car2go. 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 92% 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.

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