Road Condition Estimation Based on Heterogeneous Extended Floating Car Data

Simple item page
dc.contributor.authorLaubis, Kevin
dc.contributor.authorSimko, Viliam
dc.contributor.authorSchuller, Alexander
dc.contributor.authorWeinhardt, Christof
dc.date.accessioned2016-12-29T00:39:58Z
dc.date.available2016-12-29T00:39:58Z
dc.date.issued2017-01-04
dc.description.abstractRoad condition estimation based on Extended Floating Car Data (XFCD) from smart devices allows for determining given quality indicators like the international roughness index (IRI). Such approaches currently face the challenge to utilize measurements from heterogeneous sources. This paper investigates how a statistical learning based self-calibration overcomes individual sensor characteristics. We investigate how well the approach handles variations in the sensing frequency. Since the self-calibration approach requires the training of individual models for each participant, it is examined how a reduction of the amount of data sent to the backend system for training purposes affects the model performance. We show that reducing the amount of data by approximately 50 % does not reduce the models’ performance. Likewise, we observe that the approach can handle sensing frequencies up to 25 Hz without a performance reduction compared to the baseline scenario with 50 Hz.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2017.191
dc.identifier.isbn978-0-9981331-0-2
dc.identifier.urihttp://hdl.handle.net/10125/41344
dc.language.isoeng
dc.relation.ispartofProceedings of the 50th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectInternational Roughness Index
dc.subjectParticipatory Sensing
dc.subjectRoad Roughness
dc.subjectService Analytics
dc.subjectStatistical Learning
dc.titleRoad Condition Estimation Based on Heterogeneous Extended Floating Car Data
dc.typeConference Paper
dc.type.dcmiText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
paper0195.pdf
Size:
1.16 MB
Format:
Adobe Portable Document Format
Download

Collections

Service Analytics Minitrack