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

摘要

This paper quantifies the monetary impact of using road roughness data for path planning. Using a crowd-based data source and 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 and road roughness levels. Moreover, the dependency between fuel price and overall cost savings is presented. Although the cost savings depend on vehicle type and 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.