# On automatic extraction of on-street parking spaces using park-out events data

arXiv: https://arxiv.org/abs/2102.06758 · HTML: https://mlsanity.com/paper/2102.06758/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 12 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [A Comparison and Evaluation of Map Construction Algorithms](https://mlsanity.com/paper/1402.5138/) (2014)

## Abstract (arXiv)

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.