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Crowd Sensing of Road Conditions and Its Monetary Implications on Vehicle Navigation

Laubis, Kevin; Simko, Viliam; Schuller, Alexander

This paper quantifies the monetary impact of using road roughness data for path planning. Using a crowd-based data source, a vehicle cost model we performed a sensitivity analysis to investigate the monetary implications on vehicle owners. The results are presented as a collection of trade-off matrices showing potential yearly cost savings for different car types, road roughness levels. Moreover, the dependency between fuel price, overall cost savings is presented. Although the cost savings depend on vehicle type, on the fuel costs, our results show that the main factor is the amount of road segments with high roughness index. In particular, car owners can benefit from rerouting to a smoother road profile only in regions with road roughness at least IRI ~ 4 m/km.

Zugehörige Institution(en) am KIT Forschungszentrum Informatik, Karlsruhe (FZI)
Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Jahr 2016
Sprache Englisch
Identifikator DOI: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0132
KITopen ID: 1000065002
Erschienen in The 13th IEEE International Conference on Ubiquitous Intelligence and Computing, Toulouse, F, July 18-21, 2016
Verlag IEEE, Piscataway, NJ
Seiten 833-840
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