  Bike usage in Smart Cities becomes paramount for sustainable urban
development. Cycling provides tremendous opportunities for a more healthy
lifestyle, lower energy consumption and carbon emissions as well as reduction
of traffic jams. While the number of cyclists increase along with the expansion
of bike sharing initiatives and infrastructures, the number of bike accidents
rises drastically threatening to jeopardize the bike urban movement. This paper
studies cycling risk and discomfort using a diverse spectrum of data sources
about geolocated bike accidents and their severity. Empirical continuous
spatial risk estimations are calculated via kernel density contours that map
safety in a case study of Zurich city. The role of weather, time, accident type
and severity are illustrated. Given the predominance of self-caused accidents,
an open-source software artifact for personalized route recommendations is
introduced. The software is also used to collect open baseline route data that
are compared with alternative ones that minimize risk or discomfort. These
contributions can provide invaluable insights for urban planners to improve
infrastructure. They can also improve the risk awareness of existing cyclists'
as well as support new cyclists, such as tourists, to safely explore a new
urban environment by bike.
