Predestination: Inferring Destinations from Partial Trajectories

  • John Krumm
  • Eric Horvitz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4206)


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.


Ground Cover Kullback Leibler Driving Time Destination Cell National Household Travel Survey 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • John Krumm
    • 1
  • Eric Horvitz
    • 1
  1. 1.Microsoft ResearchMicrosoft CorporationRedmondUSA

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