On computing temporal functions for time-dependent networks using trajectory data streams

Time dependent networks in mobility scenario are key for many applications that need to cope with real world dynamics. However, the quality of a time dependent network relies on the accuracy of its temporal functions. To this aim, we propose a new method for computing temporal functions for a time dependent network using Trajectory Data Streams. This proposal extends the previous Piecewise linear model, which uses a smooth curve approach, called LOESS, that can estimate where the breakpoints values occurs in a Piecewise linear function. A challenge faced by the use of trajectory data streams is related with the time constraint to update time dependent network time functions. Our model computes the time dependent network and update the temporal function that needs to reflect recent data and discard old data. We described our solution and present experimental results, which show that our approach is efficient and effective comparing to their competitors.