Clustering Trajectories by Relevant Parts for Air Traffic Analysis
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 781
- Type
- D - Journal article
- DOI
-
10.1109/TVCG.2017.2744322
- Title of journal
- IEEE Transactions on Visualization and Computer Graphics
- Article number
- -
- First page
- 34
- Volume
- 24
- Issue
- 1
- ISSN
- 1077-2626
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 25
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Presenting a novel framework to identify patterns from large volumes of aircraft, this output targets the improvement of air traffic management services in Europe. The research was funded by EU-H2020 datAcron (687591), published in the most prestigious journal in the field, and presented at IEEE VIS (A CORE conference) and around the World (e.g. University of Zurich, keynote at at 2020 Online Apsara Conference “The World’s Most Influential Technology Conference.”). Follow-up publications and funded projects include Andrienko etal. (2018), Andrienko etal. (2020) and EU H2020 CESAR TAPAS (892358) and CESAR SIMBAD (894241).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
