Predestination: Inferring Destinations from Partial Trajectories

By John Krumm, Eric Horvitz. Ubiquitous Computing, 2006

517 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e53233fa342d9a93f3d989488225be568007605b

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Citations in the corpus

Travel route recommendation using geotags in photo sharing sites (2010, methodology): “Krumm et al. also proposed a method for predicting the driver’s destination based on multiple drivers’ GPS trajectories [13].”
Mining interesting locations and travel sequences from GPS trajectories (2009, methodology): “MSMLS [11] used a history of a driver s destinations, along with data about driving behavior extracted from multiple users GPS trajectories, to predict where a driver may be going as a trip progresses.”
NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems (2011, background): “Moreover, these techniques are often based on prediction models that are not able to extend predictions further in the future, since they mainly focus on the next movement of a user [2,14,16,19,23,26].”
Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior (2008, background): “The Predestination sytem [ 12 ] grids the world into a number of cells and uses the observation that the partially traveled route is usually an efficient path through the grid to the final destination.”
Energy-accuracy trade-off for continuous mobile device location (2010, background): “user locations learned from other mobile user locations or even land use [14] can be employed.”
Mining Individual Life Pattern Based on Location History (2009, background): “In the meantime, some techniques have been proposed to discovery higher level knowledge from individual GPS data [15], including detecting significant locations of an individual, predicting the movement destination [6], [11], recognize individual mobility [14], etc.”
Recruitment Framework for Participatory Sensing Data Collections (2010, background): “For instance, [18] models transitions of individuals using Markov processes, and [19] incorporates factors such as land-use information to help with destination prediction.”
Inference Attacks on Location Tracks (2007, background): “Tracks such as these can be used benevolently to assess traffic[22], train a system about a user’s habits[26], create customized driving routes[20], help predict where a user is going[19], or create a travelogue[10].”
Mining user similarity based on location history (2008, methodology): “Meanwhile, a few techniques [6][10][11][16][21] have been proposed to mine knowledge from multiple users’ GPS logs.”
A survey of computational location privacy (2009, background): “Other GPS inferences include determining which road a driver is on in spite of noisy GPS data [38], and predicting where a driver will drive [19, 36] and end a trip [37].”
iBAT: detecting anomalous taxi trajectories from GPS traces (2011, background): “[13, 8] showed it is possible to predict the destination and entire route of a vehicle based on historical GPS traces, and recently reported further results building routable road networks from raw GPS traces [7].”
Understanding mobility based on GPS data (2008, background): “These works include extracting significant places of an individual [3][7][8], predicting a person’s movement [6][10] and modeling a user’s transportation routine [8][10].”