Safety concerns remain a barrier to the widespread adoption of cycling. Assessing cycling safety facilitates planning safer cycling routes, which helps to boost cycling confidence. However, existing research primarily concentrates on assessing cycling safety at regional or urban levels, with few studies assessing safety at the road segment level, often without considering detailed lane information. This article leverages the complementary strengths of OSM’s rich semantic information on roads and CityGML with lane-level geometry to facilitate cycling safety assessment. Precisely, an informed map matching using Kernel Density Estimation (KDE) for bidirectional attribute transfer, cycling 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:trån 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 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.

Attractive cycling routes can effectively promote active mobility, thus reducing the twin pressures of the population boom and the greenhouse effect. However, the existing approaches for cycling route recommendation primarily concentrate on identifying the most efficient routes while ignoring the urban spatial context, which is essential to meet the user’s particular preferences. This article proposes a novel method for informed sampling and recommending cycling routes leveraging crowd-sourced trajectories with weighted-latent Dirichlet allocation (WLDA). Precisely, spatial context mapping, incorporating a weighting mechanism into LDA, latent topics mining, and cycling route recommendation based on informed sampling are introduced. We collected 1,016 cycling trajectories around Cologne, Germany, for experimental analysis. The experimental results show that the three latent topics within the trajectories, leisure, city, and green tours, are clearly presented in the line density analysis. The insightful recommendation for unfamiliar cyclists could also be actively sampled upon the WLDA model. These findings suggest that our approach could shift the route recommendation paradigm from GIS analysis to a semantic mining perspective, yielding highly interpretable results and offering novel research avenues for applying machine learning in route planning.

Efficient geovisualization is beneficial for understanding geospatial phenomena, an important research direction for GISers and Cartographers. However, the current research on geovisualization overemphasizes the visual effects while neglecting the prominent representation of crucial information and failing to consider the user’s cognitive workload of information processing. Following the laws of visual perception of the human eyes, this article proposes a visual attention-guided augmented representation approach of geographic scenes that involves area of interest computation, background simplification, and compound graphic variables. Finally, we select bridge stress visualization as a case study for experimental analysis. The experimental results of eye-tracking show that augmented representation could draw the participants’ attention to areas of interest in a short time, increasing their duration of fixations and the accuracy of completing given tasks. These findings suggest that our approach can enhance geographic scenes’ cognitive efficiency, offers a new idea for the theoretical studies of geovisualization, and holds promising potential for broader application in various geographical phenomena visualization.