Learning latent factor models of travel data for travel prediction and analysis

We describe latent factor probability models of human travel, which we learn 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. We show that our models explain the data significantly better than histogram-based methods. We also visualize the model parameters to show information about travelers and travel patterns. We show that different individuals exhibit different propensity to travel large distances. We extract the desirability of destinations on the map, which is distinct from their popularity. We show that pairs of locations have different affinities with each other, and that these affinities are partly explained by travelers' preference for staying within national borders and within the borders of linguistic areas. The method is demonstrated on two sources of travel data: geotags from Flickr images, and GPS tracks from Shanghai taxis.