Learning mobility profiles: an application as personalised weather warning system

Learning mobility pro les of citizens can play a crucial role in many applications, including tra c demand estimation, urban planning or personalized advertising. In this paper we demonstrate a framework for building and constantly readjusting mobility pro les using smart phone data coupled with manual user input and personalised discrete choice models. The methods are applied as weather warning service supporting the daily mode choice decisions of users of the system by supplying personalised information based on their mobility pro le and current weather conditions. Since it is well known that weather conditions in uence the tra c demand and the modal split of transport modes, the Framework can also further the understanding of mobility Patterns and their variability due to weather or tra c events.