Using Deep Generative Machine Learning Methods to Generate Synthetic Population
Z Yang - 2022 - search.proquest.com
Population synthesis is an important area of research aiming at generating synthetic data
about households and individuals that would be representative of real large populations …
about households and individuals that would be representative of real large populations …
[PDF][PDF] DATGAN: Integrating expert knowledge into deeplearning for population synthesis
Agent-based simulations and activity-based models used to analyse nationwide transport
networks require detailed synthetic populations. These applications are becoming more and …
networks require detailed synthetic populations. These applications are becoming more and …
Robustness Analysis of Deep Learning Models for Population Synthesis
D Opoku Mensah, G Badu-Marfo, B Farooq - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
Deep generative models have become useful for synthetic data generation, particularly
population synthesis. The models implicitly learn the probability distribution of a dataset and …
population synthesis. The models implicitly learn the probability distribution of a dataset and …
Robustness Analysis of Deep Learning Models for Population Synthesis
DO Mensah, G Badu-Marfo, B Farooq - arXiv preprint arXiv:2211.13339, 2022 - arxiv.org
Deep generative models have become useful for synthetic data generation, particularly
population synthesis. The models implicitly learn the probability distribution of a dataset and …
population synthesis. The models implicitly learn the probability distribution of a dataset and …
Using generative adversarial networks to assist synthetic population creation for simulations
Synthetic populations are heavily used in agent-based simulations and microsimulations to
create realistic representations of real-world populations. Many existing techniques rely on …
create realistic representations of real-world populations. Many existing techniques rely on …
Evaluation of Synthetic Population Data Created Using Generative Adversarial Networks
S KOTNANA, T Anderson, A Züfle… - Journal of Student …, 2021 - journal.gmu.edu
The generation of realistic synthetic populations is an important function for many agent-
based models to provide accurate predictions. The problem with synthetic population data …
based models to provide accurate predictions. The problem with synthetic population data …
[PDF][PDF] Population synthesis meets deep generative modelling
Agent-based transport models depend to a high degree on the formation of the underlying
population. Models and methods for generating such populations, possibly under …
population. Models and methods for generating such populations, possibly under …
Prediction of rare feature combinations in population synthesis: Application of deep generative modelling
Population synthesis is concerned with the generation of agents for agent-based modelling
in many fields, such as economics, transportation, ecology and epidemiology. When the …
in many fields, such as economics, transportation, ecology and epidemiology. When the …
Generative population synthesis for joint household and individual characteristics
Z Aemmer, D MacKenzie - Computers, Environment and Urban Systems, 2022 - Elsevier
Household surveys provide immense value in the fields of transportation and urban
planning. However, even the most well-funded surveying agencies rely on sampling …
planning. However, even the most well-funded surveying agencies rely on sampling …
Composite travel generative adversarial networks for tabular and sequential population synthesis
G Badu-Marfo, B Farooq… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Agent-based microsimulation has become the standard to analyze intelligent transportation
systems, using disaggregate travel demand data for entire populations, data that are not …
systems, using disaggregate travel demand data for entire populations, data that are not …