Modeling Transportation Routines using Hybrid Dynamic Mixed Networks

TL;DR

This paper introduces Hybrid Dynamic Mixed Networks (HDMNs), a flexible framework combining Bayesian networks with deterministic constraints, and proposes approximate inference algorithms for modeling and predicting individual travel activities over time.

Contribution

The paper presents a novel framework, HDMNs, integrating probabilistic and deterministic reasoning, along with approximate inference algorithms tailored for dynamic transportation modeling.

Findings

Effective modeling of travel activities demonstrated

Algorithms improve prediction accuracy

Framework handles complex deterministic constraints

Abstract

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.