Measuring traffic congestion: an approach based on learning weighted inequality, spread and aggregation indices from comparison data

Beliakov, Gleb, Gagolewski, Marek, James, Simon, Pace, Shannon, Pastorello, Nicola, Thilliez, Elodie and Vasa, Rajesh 2018, Measuring traffic congestion: an approach based on learning weighted inequality, spread and aggregation indices from comparison data, Applied soft computing, vol. 67, pp. 910-919, doi: 10.1016/j.asoc.2017.07.014.

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Title Measuring traffic congestion: an approach based on learning weighted inequality, spread and aggregation indices from comparison data
Author(s) Beliakov, GlebORCID iD for Beliakov, Gleb orcid.org/0000-0002-9841-5292
Gagolewski, Marek
James, SimonORCID iD for James, Simon orcid.org/0000-0003-1150-0628
Pace, Shannon
Pastorello, NicolaORCID iD for Pastorello, Nicola orcid.org/0000-0003-3032-3866
Thilliez, Elodie
Vasa, RajeshORCID iD for Vasa, Rajesh orcid.org/0000-0003-4805-1467
Journal name Applied soft computing
Volume number 67
Start page 910
End page 919
Total pages 10
Publisher Elsevier B.V.
Place of publication Amsterdam, The Netherlands
Publication date 2018-06
ISSN 1568-4946
Keyword(s) aggregation functions
inequality indices
spread measures
learning weights
congestion
traffic analysis
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Computer Science
PREDICTION
DIVERSITY
Language eng
DOI 10.1016/j.asoc.2017.07.014
Field of Research 0102 Applied Mathematics
0801 Artificial Intelligence And Image Processing
0806 Information Systems
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, Elsevier B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30104229

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