TY - GEN

T1 - Joint estimation of road roughness from crowd-sourced bicycle acceleration measurements

AU - Wage, Oskar

AU - Sester, Monika

N1 - Funding Information: This work is partially funded by the BMBF Germany projects USEfUL and USEfUL XT (grants 03SF0547B & 03SF0609C), as well as supported by the DFG GRK 1931 SocialCars. The authors also thank the volunteers who contributed to the data collection and the reviewers for their valid and helpful feedback.

PY - 2021/6/17

Y1 - 2021/6/17

N2 - In contrast to cars, route choices for cycling are barely influenced by the respective traffic situation, but to a large extent by the routes’ comfort. Especially in urban settings with several alternatives, segments with many or long stops at traffic lights and badly maintained roads are avoided due to a low comfort and cyclists vary from the shortest route. This fact is only indirectly considered in common navigation applications.This work aims to integrate surface roughness measurements collected from diverse bicycles to a joint scale via a least-squares adjustment. Data was collected using smartphones, which were mounted to bike hand bars and measured positions and vertical accelerations on user’s trips. As this way sensed roughness also depends on the bike setting and type, the resulting values would be different for different users. Thus, this paper presents a novel approach to harmonize observations from differing sensitive setups. The basic concept idea of bundle block adjustment is adapted to calibrate a basic scale model and in parallel adjust the observations of surface roughness to a common scale.This way a crowd-sourced roughness map can be generated. Such a map can be used to enrich bike focused routing services and thus encourage cycling in daily live. In addition, it can also be used to derive hints for infrastructure servicing.

AB - In contrast to cars, route choices for cycling are barely influenced by the respective traffic situation, but to a large extent by the routes’ comfort. Especially in urban settings with several alternatives, segments with many or long stops at traffic lights and badly maintained roads are avoided due to a low comfort and cyclists vary from the shortest route. This fact is only indirectly considered in common navigation applications.This work aims to integrate surface roughness measurements collected from diverse bicycles to a joint scale via a least-squares adjustment. Data was collected using smartphones, which were mounted to bike hand bars and measured positions and vertical accelerations on user’s trips. As this way sensed roughness also depends on the bike setting and type, the resulting values would be different for different users. Thus, this paper presents a novel approach to harmonize observations from differing sensitive setups. The basic concept idea of bundle block adjustment is adapted to calibrate a basic scale model and in parallel adjust the observations of surface roughness to a common scale.This way a crowd-sourced roughness map can be generated. Such a map can be used to enrich bike focused routing services and thus encourage cycling in daily live. In addition, it can also be used to derive hints for infrastructure servicing.

KW - Cycling comfort

KW - Data integration

KW - Floating bike data

KW - Volunteered geographic information

UR - http://www.scopus.com/inward/record.url?scp=85119695383&partnerID=8YFLogxK

U2 - 10.5194/isprs-annals-V-4-2021-89-2021

DO - 10.5194/isprs-annals-V-4-2021-89-2021

M3 - Conference contribution

VL - V-4-2021

T3 - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SP - 89

EP - 96

BT - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

PB - Copernicus Publications

ER -