Bidirectional enrichment of OpenStreetMap and CityGML 3.0 to enhance cycling safety assessment

Safety concerns remain a barrier to the widespread adoption of cycling. Assessing cycling safety facilitates planning safer cycling routes, which helps boost cycling confidence, especially among older adults and novice cyclists. However, the existing research primarily concentrates on assessing cycling safety at regional or urban levels, with few studies using data-driven methods to assess safety at the road segment level, often without considering detailed lane information. This article proposes a novel approach that leverages the complementary strengths of OSM’s rich semantic information on roads and CityGML with lane-level geometry to enhance cycling safety assessment. Precisely, an informed map matching using Kernel Density Estimation (KDE) for bidirectional attribute transfer, road segment safety scores calculation, and CityGML enrichment with cycling safety are introduced in detail. OpenDRIVE data from the Test Track for Autonomous and Connected Driving (TAVF) in Hamburg, Germany, was converted to a CityGML 3.0-compliant structure using the r:tr˚an tool and used together with the corresponding OSM data for experimental analysis. The experimental results show that integrating OSM and CityGML is conducive to improving the comprehensive cycling safety assessment at the road segment level. The assessment results are further embedded into bicycle-related semantics within CityGML 3.0 for the subsequent 3D representation of cycling safety, paving the way for safest path navigation and enhanced perception of cycling safety in 3D environments.