Currently, mainly aggregated statistics are used for bicycle crash risk calculations. Thus, the understanding of spatial patterns at local scale levels remains vague. Using an agent-based flow model and a bicycle crash database covering 10 continuous years of observation allows us to calculate and map the crash risk on various spatial scales for the city of Salzburg (Austria). In doing so, we directly account for the spatial heterogeneity of crash occurrences. Additionally, we provide a measure for the statistical robustness on the level of single reference units and consider modifiable areal unit problem (MAUP) effects in our analysis. This study is the first of its kind. The results facilitate a better understanding of spatial patterns of bicycle crash rates on the local scale. This is especially important for cities that strive to improve the safety situation for bicyclists in order to address prevailing safety concerns that keep people from using the bicycle as a utilitarian mode of (urban) transport.
Road safety is a core element of any bicycle promotion strategy. In order to gain an overview of the status quo, and to provide a basis for evidence-based decisions and measures, the quality of the road network in terms of safety needs to be assessed. For this, a webbased tool is introduced, which conceptually builds upon an indicator-based assessment model. Its applicability is demonstrated in a case study deploying the workflow on Austria's authoritative transportation network dataset GIP (Graphenintegrations-Plattform).
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