Maximum entropy bridgelets for trajectory completion

Location measurements from people and vehicles often have long temporal gaps between them. However, we would still like to reason about location behavior during these gaps. This paper presents a new method for filling these gaps that is both principled and data-driven. Unlike the most common method, linear interpolation, our method explicitly represents the location uncertainty in the gaps with probability. It also learns from actual mobility data. We introduce bridgelets, which are small, spatio-temporal, maximum entropy clouds that model spatial uncertainty over small gaps. Using actual trajectories, we combine bridgelets into probabilistic bridges that are specific to absolute start and end locations on the map. The resulting bridges give the probability of visiting certain in-between locations given only the start and end points. Using real trajectory data, we compare our maximum entropy bridges to a popular baseline to show how our approach is much more accurate.