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Weighted aggregation in the domain of crowd-based road condition monitoring

Laubis, Kevin; Simko, Viliam; Weinhardt, Christof

Abstract: This paper focuses on crowd-based road condition monitoring using smart devices, such as smartphones and evaluates different strategies for aggregating multiple measurements (arithmetic mean and weighted means using R2 and RMSE) for predicting the longitudinal road roughness. The results confirm that aggregating predictions from single drives leads to a higher model performance. This has been expected and confirms the intuition. The overall R2 could be increased from 0.69 to 0.75 on average and the NRMSE could be decreased from 9% to 8% on average. However, contrary to the intuition, the results show that weighted aggregations of single predictions should be avoided, which is consistent with previous findings in other domains, such as financial forecasting.

Zugehörige Institution(en) am KIT Forschungszentrum Informatik, Karlsruhe (FZI)
Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Jahr 2016
Sprache Englisch
Identifikator ISBN: 978-3-88579-653-4
ISSN: 1617-5468
URN: urn:nbn:de:swb:90-619813
KITopen ID: 1000061981
Erschienen in INFORMATIK 2016 : Informatik von Menschen für Menschen, 46. Jahrestagung der Gesellschaft für Informatik, 26.-30. September 2016, Klagenfurt. Hrsg.: H. C. Mayr
Verlag Gesellschaft für Informatik, Bonn
Seiten 385-393
Embargofrist Die Publikation ist in KITopen am 31.10.2017 als Volltext frei zugänglich (Open Access).
Serie GI-Edition / Proceedings. Lecture Notes in Informatics ; 259
URLs Volltext
Schlagworte Crowd-based sensing, road condition monitoring, international roughness index, predictive road maintenance, weighted aggregation, ensemble learning
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