Reconstructing Activity Location Sequences From Incomplete Check-In Data: A Semi-Markov Continuous-Time Bayesian Network Model

Geo-location data from the check-ins made in online social media offers us information, in new ways, to understand activity-location choices of a large number of people. However, one of the major challenges of using check-in data is that it has missing activities, since users share their activities voluntarily. In this paper, we present a probabilistic modeling approach to reconstruct user activity-location sequences from this incomplete activity participation information. Specifically, we answer the question of how to predict an individual’s next activity, its duration and location given the incomplete trajectory data. The model describes the dynamics of individual activity participation behavior evolving over continuous time. A semi-Markov modeling approach is used to capture the stochastic processes involved in the activity generation mechanism. We present a particle-based Markov chain Monte Carlo sampler to run inference over the model. We further develop an expectation-maximization algorithm to learn the unknown parameters of the model from incomplete trajectory data. Finally, the method is applied to synthetically generated activity-location sequences and a data set of Foursquare check-ins of the users from New York City. Our experiments show that this method can successfully extract the true transition and duration distributions given the incomplete trajectory information. The proposed approach can help building many intelligent transportation applications using check-in data.