Using 5 parallel passive data streams to report on a wide range of mobility options

Abstract Montreal welcomes a variety of transportation options: transit, carsharing, bikesharing, taxi, etc. In the recent years, the “new” alternatives have gained market shares and it has become critical to understand the role each mode plays in the daily mobility of people as well as their interactions. This paper presents an analysis process of passive data streams and a typology of typical days of usage of these modes using clustering techniques. It shows that the most important attributes determining the type of multimodal usage day are type of day (working / non-working), temperature, fuel price and precipitations.