  Travel time estimation is a critical task, useful to many urban applications
at the individual citizen and the stakeholder level. This paper presents a
novel hybrid algorithm for travel time estimation that leverages historical and
sparse real-time trajectory data. Given a path and a departure time we estimate
the travel time taking into account the historical information, the real-time
trajectory data and the correlations among different road segments. We detect
similar road segments using historical trajectories, and use a latent
representation to model the similarities. Our experimental evaluation
demonstrates the effectiveness of our approach.

Machine Learning