![]() ![]() This type of crowd-sourced data provides larger datasets than were previously attainable by travel surveys and cyclist counts, which allows for the comparison of trip characteristics between geographic regions and the study of temporal trends in bicycle ridership. This isĮspecially important for cities that strive to improve the safety situation for bicyclists in order toĪddress prevailing safety concerns that keep people from using the bicycle as a utilitarian mode ofĭata from GPS-based fitness tracker apps have become a prominent source for studying cycling behavior. The resultsįacilitate a better understanding of spatial patterns of bicycle crash rates on the local scale. Additionally, we provideĪ measure for the statistical robustness on the level of single reference units and consider modifiableĪreal unit problem (MAUP) effects in our analysis. ![]() So, we directly account for the spatial heterogeneity of crash occurrences. Using an agent-basedįlow model and a bicycle crash database covering 10 continuous years of observation allows us toĬalculate and map the crash risk on various spatial scales for the city of Salzburg (Austria). The understanding of spatial patterns at local scale levels remains vague. ![]() Currently, mainly aggregated statistics are used for bicycle crash risk calculations. ![]()
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