Road Patterns Identification and Risk Analysis Based on Machine Learning Framework: Powered Two-Wheelers Case

Analysis of motorcyclists’ behaviour and the risk that this mode of transport incurs for the mobility as well as their safety have gained more attention in recent years. In the context of the European project SimuSafe, various experimentations have been done using instrumented motorbikes in a naturalistic riding study and the riders’ behaviours are subjected to self-confrontation interviews with traffic psychologists. This paper aims at the identification of different riding patterns using machine learning techniques and allows for a deeper understanding of pattern-specific risk exposure from multi-source data (video footage and interviews). More specifically, we focus on the roundabout pattern analysis as it is the most important source of collisions and a set of rules are designed using a decision tree to analyse their related risks. The generated rules may also fuel a multi-agent simulator to reflect the riders’ real-world behaviours.