Abstract
Many drives in crowded cities end with a challenging parking search, and visitors often do not know which streets allow on-street parking. Therefore, we present a learning-based approach to automatically generate on-street parking maps from parked vehicle positions detected by sensing vehicles. Multiple sets of features are proposed to describe the occupancy of every small road segment and its surroundings at different time instances. The usage of k-means algorithm as unsupervised learning and random forests as supervised learning are compared by applying these feature sets. The proposed approach is evaluated with repeated LiDAR measurements on more than five kilometers of potential parking space length. Our approaches, while keeping the model more generic, reveal slightly better results than an approach from literature. In particular, the unsupervised approach does not need a training data set and is free of any area specific parameter choice.
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Acknowledgments
This research has been supported by the German Research Foundation (DFG) through the Research Training Group SocialCars (GRK 1931). The focus of the SocialCars Research Training Group is on significantly improving the city’s future road traffic, through cooperative approaches. This support is gratefully acknowledged.
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Bock, F., Liu, J., Sester, M. (2016). Learning On-Street Parking Maps from Position Information of Parked Vehicles. In: Sarjakoski, T., Santos, M., Sarjakoski, L. (eds) Geospatial Data in a Changing World. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-33783-8_17
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DOI: https://doi.org/10.1007/978-3-319-33783-8_17
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