Population Synthesis Using a Bayesian Network Modeling Technique

Population Synthesis Using a Bayesian Network Modeling Technique

Presented at the 2023 BayesiaLab Conference.


Agent-based microsimulation modeling techniques are adopted for urban system modeling mainly because of their capacity to address the complex interactions among individuals, households, and other urban elements. The performance of urban simulation models largely depends on the quality of the input data, which is generated through a population synthesis procedure. This study proposes a Bayesian network and generalized raking techniques for population synthesis. The Bayesian network is used to generate the synthetic population pool from the microsample, and generalized raking is used to fit the synthetic population with the control total. Some of the key features of the proposed population synthesis are as follows: accommodating heterogeneity based on both household and individual attributes, tackling missing/incomplete observations in the microsample, and generating a true synthesis of the population from the microsamples. A data-driven structure learning technique is adopted to generate effective and optimal structures among heterogeneous households and individuals. This Bayesian network + generalized raking procedure is implemented to generate a 100% synthetic population at the smallest zonal level of dissemination area for the Central Okanagan region of British Columbia. The results suggest that capturing heterogeneity within the Bayesian network has tremendously benefitted the reconstruction process in efficiently and accurately generating a synthetic population from the available microsample. Finally, this population synthesis is developed as a component of the agent-based integrated urban model, currently under development at The University of British Columbia’s Okanagan campus.

Presentation Video

Presentation Slides

About the Presenter

Dr. Mahmudur Fatmi is an assistant professor in Civil Engineering at UBC-Okanagan. His expertise revolves around travel demand modeling, particularly focusing on the interactions among population socio-demographics and their transportation and land use-related decisions. He contributes to developing econometric models and agent-based microsimulation techniques. He has partnerships with cities and transit agencies, and his research findings assist them in making effective transportation and land use policies and infrastructure investment decisions. He is a member of several transportation research and professional communities and societies. Dr. Fatmi’s contributions have been recognized through several local, provincial, and national awards, such as Transport Canada Scholarship and Nova Scotia Research and Innovation Scholarship.

For North America

Bayesia USA

4235 Hillsboro Pike
Suite 300-688
Nashville, TN 37215, USA

+1 888-386-8383

Head Office

Bayesia S.A.S.

Parc Ceres, Batiment N 21
rue Ferdinand Buisson
53810 Change, France

For Asia/Pacific

Bayesia Singapore

1 Fusionopolis Place
#03-20 Galaxis
Singapore 138522

Copyright © 2024 Bayesia S.A.S., Bayesia USA, LLC, and Bayesia Singapore Pte. Ltd. All Rights Reserved.