Reconstruction of public transport state

Interest towards the applications of ICT in public and private urban transport has grown significantly over the last few years. In the field of the user interfaces with transportation, however, continuous, highly context-Aware, real time interaction can still be found only in a very limited number of cases, mostly in private transportation. One of the main issues in actually developing an assistive, portable, continuously interacting application is getting to know the transports system state (equations of motion of the means, position of the users on the means). In most cities the most temporally accurate data available is the estimated departure time of the next train or bus at the stops. In this paper, we propose a state-based Bayesian approach to the reconstruction of the transit system state from limited available knowledge (estimated departure time at the stops, data from users phone). The system is general and effective also in the presence of various real-world kinds of noise, such as information blackouts. In addition, we propose an extension to seamlessly exploit location of users to estimate which means they're on-board, and describe some scenarios in which such information would be of great value for the transit agencies, and would enable innovative social applications and interactions.