Creative Commons Attribution 4.0 license (CC BY 4.0)when quoting this document, please refer to the following
DOI: 10.4230/DagRep.12.4.26
URN: urn:nbn:de:0030-drops-172792
URL: https://drops.dagstuhl.de/opus/volltexte/2022/17279/
Jonietz, David ; Sester, Monika ; Stewart, Kathleen ; Winter, Stephan ; Tomko, Martin ; Xin, Yanan
Weitere Beteiligte (Hrsg. etc.): David Jonietz and Monika Sester and Kathleen Stewart and Stephan Winter and Martin Tomko and Yanan Xin
Urban Mobility Analytics (Dagstuhl Seminar 22162)
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Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22162 "Urban Mobility Analytics". The seminar brought together researchers from academia and industry who work in complementary ways on urban mobility analytics. The seminar especially aimed at bringing together ideas and approaches from deep learning research, which is requiring large datasets, and reproducible research, which is requiring access to data.
BibTeX - Entry
@Article{jonietz_et_al:DagRep.12.4.26,
author = {Jonietz, David and Sester, Monika and Stewart, Kathleen and Winter, Stephan and Tomko, Martin and Xin, Yanan},
title = {{Urban Mobility Analytics (Dagstuhl Seminar 22162)}},
pages = {26--53},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2022},
volume = {12},
number = {4},
editor = {Jonietz, David and Sester, Monika and Stewart, Kathleen and Winter, Stephan and Tomko, Martin and Xin, Yanan},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2022/17279},
URN = {urn:nbn:de:0030-drops-172792},
doi = {10.4230/DagRep.12.4.26},
annote = {Keywords: data analytics, Deep learning, Reproducible research, urban mobility}
}
| Keywords: | data analytics, Deep learning, Reproducible research, urban mobility | |
| Seminar: | DagRep, Volume 12, Issue 4 | |
| Issue date: | 2022 | |
| Date of publication: | 14.11.2022 |
