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Title:

TUMDOT–MUC: Data Collection and Processing of Multimodal Trajectories Collected by Aerial Drones

Document type:
Zeitschriftenaufsatz
Author(s):
Kutsch, A.; Margreiter, M.; Bogenberger, K.
Abstract:
Currently available trajectory data sets undoubtedly provide valuable insights into traffic events, the behavior of road users and traffic flow theory, thus enabling a wide range of applications. However, there are still shortcomings that need to be addressed: (i) the continuous temporal recording (ii) of a coherent area covering several intersections (iii) with the detection of all road users, including pedestrians and cyclists. Therefore, this study focuses on the design of a large-scale aeria...     »
Keywords:
Drone · Aerial observation · Interaction of different modes · Big data · Open data
Journal title:
Data Science for Transportation
Year:
2024
Journal volume:
6:15
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:https://doi.org/10.1007/s42421-024-00101-5
Publisher:
Springer Nature
TUM Institution:
Lehrstuhl für Verkehrstechnik
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