Analyzing tweets to enable sustainable, multi-modal and personalized urban mobility: Approaches and results from the Italian project TAM-TAM

This paper presents the approaches proposed in the Italian project TAM-TAM to support a smarter, personalized and sustainable urban mobility, by taking into the loop the users of the transportation services, in particular citizens, tourists and commuters in Milan. A computational module has been defined and developed in order to collect and analyze relevant tweets posted by users as well transport operator. The main goals are two: identifying events (e.g. accidents, sudden traffic jams, service interruptions, etc.) and evaluating the overall sentiment about the service as well as mobility options. Detected events are used by other computational modules of TAM-TAM in order to support a more effective travel planning; on the other hand, the perceived quality of service is provided both to users, enabling more personalized choices, and to transport company, supporting them in the management of mobility supply.