Book Chapter10.1007/978-3-540-73078-1_45
Driver Destination Models
John Krumm,Eric Horvitz +1 more
- 25 Jul 2007
- pp 360-364
TL;DR: This work focuses on the probability of observing drivers visit previously unobserved destinations given time of day and day of week, and the rate of decline of observing such new destinations with time, and discovers a statistically significant difference based on gender.
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Abstract: Predictive models of destinations represent an opportunity in the context of the increasing availability and sophistication of in-car driving aids. We present analyses of drivers' destinations based on GPS data recorded from 180 volunteer subjects. We focus on the probability of observing drivers visit previously unobserved destinations given time of day and day of week, and the rate of decline of observing such new destinations with time. For the latter, we discover a statistically significant difference based on gender.
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Citations
Patent
Open-world modeling
Eric Horvitz,John Krumm,Murugesan S. Subramani +2 more
- 29 Jul 2011
TL;DR: In this article, the authors present systems and methods that facilitate generating an inference about events that may not have yet been observed, using both open-world and closed-world models.
14
Implementation of a Multi-Hop Vehicular Network Analyzer
Junghoon Lee
- 02 Sep 2008
TL;DR: A connectivity analysis function is implemented by a thread routine which runs the well-known Dijkstra's shortest path algorithm to calculate the reachability matrix, and then plots the individual path on the digital map.
References
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Daniel Ashbrook,Thad Starner +1 more
- 01 Oct 2003
TL;DR: This work presents a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales and incorporates these locations into a Markov model that can be consulted for use with a variety of applications in both single-user and collaborative scenarios.
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Learning and inferring transportation routines
TL;DR: In this paper, a hierarchical Markov model is proposed to infer a user's daily movements through an urban community using multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as user's destination and mode of transportation.
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Predestination: inferring destinations from partial trajectories
John Krumm,Eric Horvitz +1 more
- 17 Sep 2006
TL;DR: A method called Predestination is described 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, to produce a probabilistic map of destinations.
•Proceedings Article
Learning and inferring transportation routines
Lin Liao,Dieter Fox,Henry Kautz +2 more
- 25 Jul 2004
TL;DR: A hierarchical Markov model that can learn and infer a user's daily movements through an urban community and an application called ''Opportunity Knocks'' that employs the techniques to help cognitively-impaired people use public transportation safely.
562
Project lachesis: Parsing and modeling location histories
Ramaswamy Hariharan,Kentaro Toyama +1 more
- 20 Oct 2004
TL;DR: This paper proposes a number of rigorously defined data structures and algorithms for analyzing and generating location histories, using stays and destinations as examples, and proposes two methods for modeling location histories probabilistically.
329