Learning Latent Factor Models of Human Travel

Michael Guerzhoy 1, 2 Aaron Hertzmann 1, 3
2 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
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.
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Michael Guerzhoy, Aaron Hertzmann. Learning Latent Factor Models of Human Travel. NIPS Wokshop on Social Network and Social Media Analysis: Methods, Models and Applications, Dec 2012, Lake Tahoe, Nevada, United States. ⟨hal-00756192⟩

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