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The powered two-wheelers (PTW) are among the most vulnerable transport users. It is crucial to identify the appropriate action that should be undertaken during a specific situation to reduce the risk. In this article, the aim is to improve the current state of the art in identification of riding patterns through neural network architectures and to explain how a decision is made by a model which is considered as a black box. In this regard, a new visualization tool specific to time series is suggested to help identify the most influential factors and hopefully to develop appropriate risk mitigation strategies.