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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Stichworte:
Drone · Aerial observation · Interaction of different modes · Big data · Open data
Zeitschriftentitel:
Data Science for Transportation
Jahr:
2024
Band / Volume:
6:15
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:https://doi.org/10.1007/s42421-024-00101-5
Verlag / Institution:
Springer Nature
TUM Einrichtung:
Lehrstuhl für Verkehrstechnik
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