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This paper describes a general framework called Hybrid Dynamic Mixed Networks(HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation ofdiscrete deterministic information in the form of constraints. We proposeapproximate inference algorithms that integrate and adjust well knownalgorithmic principles such as Generalized Belief Propagation,Rao-Blackwellised Particle Filtering and Constraint Propagation to address thecomplexity of modeling and reasoning in HDMNs. We use this framework to model aperson's travel activity over time and to predict destination and routes giventhe current location. We present a preliminary empirical evaluationdemonstrating the effectiveness of our modeling framework and algorithms usingseveral variants of the activity model.

Modeling Transportation Routines using Hybrid Dynamic Mixed Networks