Modeling Transportation Routines using Hybrid Dynamic Mixed Networks
This work addresses transportation routine prediction, which is incremental as it builds on existing methods like Bayesian networks and constraint propagation.
The paper tackles the problem of modeling a person's travel activity over time to predict destinations and routes from current location, proposing a Hybrid Dynamic Mixed Networks framework with approximate inference algorithms and showing preliminary empirical effectiveness.
This paper describes a general framework called Hybrid Dynamic Mixed Networks (HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of discrete deterministic information in the form of constraints. We propose approximate inference algorithms that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Particle Filtering and Constraint Propagation to address the complexity of modeling and reasoning in HDMNs. We use this framework to model a person's travel activity over time and to predict destination and routes given the current location. We present a preliminary empirical evaluation demonstrating the effectiveness of our modeling framework and algorithms using several variants of the activity model.