Keywords: Bayesian nonparametrics, Hidden Markov Model, scalable inference, Gaussian process, spatio-temporal data
TL;DR: Proposed a Bayesian nonparametric model to extract interpretable patterns from complex spatio-temporal data and proposed a scalable inference algorithm to analyze large volume of data, which we demonstrate on the NGSIM traffic dataset.
Abstract: Learning and understanding heterogeneous patterns in complex spatio-temporal data is an important and challenging task across domains in science and engineering. In this work, we develop a model for learning heterogeneous and dynamic patterns of velocity field data, motivated by applications in the transportation domain. We draw from basic nonparametric Bayesian modeling elements such as the infinite hidden Markov model and Gaussian process and focus on making the learning of such a stochastic model scalable for voluminous and streaming data. This is achieved by employing sequential MAP estimates from the infinite HMM model, an efficient sequential sparse GP posterior computation, and refinement of the estimates using the Viterbi algorithm, which is shown to work effectively on a careful simulation study. We demonstrate the efficacy of our techniques to the NGSIM dataset of complex multi-vehicle interactions.
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