Abstract. We describe a method called Predestination that uses a history of a driver's destinations, along with data about driving behaviors, to predict where a driver is going as a trip progresses. Driving behaviors include types of destinations, driving efficiency, and trip times. Beyond considering previously visited destinations, Predestination leverages an open-world modeling methodology that considers the likelihood of users visiting previously unobserved locations based on trends in the data and on the background properties of locations. This allows our algorithm to smoothly transition between "out of the box" with no training data to more fully trained with increasing numbers of observations. Multiple components of the analysis are fused via Bayesian inference to produce a probabilistic map of destinations. Our algorithm was trained and tested on hold-out data drawn from a database of GPS driving data gathered from 169 different subjects who drove 7,335 different trips.
Krumm et al. in REF presented methodology that considers the likelihood of users visiting previously unobserved locations based on trends in the survey data, e.g., driving efficiency, trip times etc., and background properties of locations, e.g., ground and water cover.
Predestination: Inferring Destinations from Partial Trajectories
Ubicomp
Computer Science