The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 427–434, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-427-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 427–434, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-427-2022
 
02 Jun 2022
02 Jun 2022

RISK ANALYSIS OF CYCLING ACCIDENTS USING A TRAFFIC DEMAND MODEL

O. Wage1, L. Bienzeisler2, and M. Sester1 O. Wage et al.
  • 1Institute of Cartography and Geoinformatics, Leibniz University Hannover, Germany
  • 2Institute of Transportation and Urban Engineering, TU Braunschweig, Germany

Keywords: incident, bike, routing, safety, crash, MATSim, simulation

Abstract. In Germany, accidents are collected nationwide, including also bicycle accidents. To determine accident-prone locations, it is necessary to not only look at the number of accidents but also in relation to the absolute number of cyclists traversing that location. Thus, this study exploits a collection of bicycle accidents in combination with estimated cyclist volumes on street level in Hanover (Germany). The basis for the generated bicycle volumes is the resulting origin-destination demand for bicycle mode from an agent-based traffic simulation model. A normalization of the accidents by an absolute bicycle volume allows to estimate a risk score and to compare high frequented ways with less popular minor paths in an objective manner. This method is used to show locations with comparatively high risk for cyclists. Besides highlighting these spots on a map, e.g. for city planners, the resulting risk scores can be integrated into bicycle routing to avoid those areas for future trips.