Mochizuki, Yosuke (The University of Tokyo), Urata, Junji (University of Tsukuba), Hato, Eiji (The University of Tokyo)

Object-oriented Bayesian Networks for Activity-based Model with deep generative graph averaging

Scheduled for presentation during the Poster Session "Travel Behavior Under ITS" (WeAT16), Wednesday, September 25, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Abstract

This study focused on enhancing the use of Bayesian networks (BN) in activity-based models (ABM) for transportation by integrating domain-specific knowledge and stabilizing structure learning. An object-oriented Bayesian network (OOBN) and a novel graph averaging method were used to construct a BN graph that captures an individual’s decision-making in a data-oriented manner. In these methods, a deep generative model was used to improve explanatory power and learning stability. The effectiveness of this approach was validated through numerical experiments, which demonstrated the ability of the proposed method to rapidly and consistently generate plausible BN graph structures that fit the observed data better than existing BN-based ABMs did.