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Learning Latent Factor Models of Human Travel
Abstract : This paper describes probability models for human travel, using latent factors learned from data. The latent factors represent interpretable properties: travel distance cost, desirability of destinations, and affinity between locations. Individuals are clustered into distinct styles of travel. The latent factors combine in a multiplicative manner, and are learned using Maximum Likelihood. The resulting models exhibit significant improvements in predictive power over previous methods, while also using far fewer parameters than histogram-based methods. The method is demonstrated on travel data from two sources: geotags from a social image sharing site (Flickr), and GPS tracks from Shanghai taxis.
Cited literature [22 references]
https://hal.inria.fr/hal-00756192
Contributor : Thoth Team <>
Submitted on : Thursday, November 22, 2012 - 3:42:13 PM
Last modification on : Thursday, March 26, 2020 - 8:49:28 PM
Document(s) archivé(s) le : Saturday, February 23, 2013 - 3:45:32 AM
Contributor : Thoth Team <>
Submitted on : Thursday, November 22, 2012 - 3:42:13 PM
Last modification on : Thursday, March 26, 2020 - 8:49:28 PM
Document(s) archivé(s) le : Saturday, February 23, 2013 - 3:45:32 AM
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