  In cities worldwide, cars cause health and traffic problems whichcould be
partly mitigated through an increased modal share of bicycles. Many people,
however, avoid cycling due to a lack of perceived safety. For city planners,
addressing this is hard as they lack insights intowhere cyclists feel safe and
where they do not. To gain such insights,we have in previous work proposed the
crowdsourcing platform SimRa,which allows cyclists to record their rides and
report near miss incidentsvia a smartphone app. In this paper, we present
CycleSense, a combination of signal pro-cessing and Machine Learning
techniques, which partially automatesthe detection of near miss incidents, thus
making the reporting of nearmiss incidents easier. Using the SimRa data set, we
evaluate CycleSenseby comparing it to a baseline method used by SimRa and show
that itsignificantly improves incident detection.
