29 citations · 38 across the 15 of their papers we have counts for
12 papers · 1 filter
Robustness Analysis of Deep Learning Models for Population Synthesis
Daniel Opoku Mensah, Godwin Badu-Marfo, Bilal Farooq
Deep generative models have become useful for synthetic data generation, particularly population synthesis. The models implicitly learn the probability distribution of a dataset an…
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference
Daniel Opoku Mensah, Godwin Badu-Marfo, Ranwa Al Mallah +1
As the most significant data source in smart mobility systems, GPS trajectories can help identify user travel mode. However, these GPS datasets may contain users' private informati…
Ordinal-ResLogit: Interpretable Deep Residual Neural Networks for Ordered Choices
Kimia Kamal, Bilal Farooq
This study presents an Ordinal version of Residual Logit (Ordinal-ResLogit) model to investigate the ordinal responses. We integrate the standard ResLogit model into COnsistent RAn…
Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories
Ali Yazdizadeh, Arash Kalatian, Zachary Patterson +1
Multi-task learning is assumed as a powerful inference method, specifically, where there is a considerable correlation between multiple tasks, predicting them in an unique framewor…
A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis
Godwin Badu-Marfo, Bilal Farooq, Zachary Patterson
In this work, we develop a privacy-by-design generative model for synthesizing the activity diary of the travel population using state-of-art deep learning approaches. This propose…
Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis
Godwin Badu-Marfo, Bilal Farooq, Zachary Paterson
Agent-based transportation modelling has become the standard to simulate travel behaviour, mobility choices and activity preferences using disaggregate travel demand data for entir…