  With the focus that cities around the world have put on sustainable
transportation during the past few years, biking has become one of the foci for
local governments around the world. Cities all over the world invest in bike
infrastructure, including bike lanes, bike parking racks, shared (dockless)
bike systems etc. However, one of the critical factors in converting
city-dwellers to (regular) bike users/commuters is safety. In this work, we
utilize bike accident data from different cities to model the biking safety
based on street-level (geographical and infrastructural) features. Our
evaluations indicate that our model provides well-calibrated probabilities that
accurately capture the risk of a biking accident. We further perform cross-city
comparisons in order to explore whether there are universal features that
relate to cycling safety. Finally, we discuss and showcase how our model can be
utilized to explore "what-if" scenarios and facilitate policy decision making.
