CycleSense: Detecting Near Miss Incidents in Bicycle Traffic from Mobile Motion Sensors
This work addresses the challenge of identifying unsafe cycling conditions for city planners and cyclists, though it is incremental as it builds on an existing crowdsourcing platform.
The paper tackles the problem of detecting near miss incidents in bicycle traffic to improve cyclist safety, presenting CycleSense, which combines signal processing and machine learning to automate detection and shows significant improvement over a baseline method.
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.