326 citations · 1.2k across the 59 of their papers we have counts for
9 papers · 1 filter
Debiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviour
Kimia Kamal, Bilal Farooq
Before the transition of AVs to urban roads and subsequently unprecedented changes in traffic conditions, evaluation of transportation policies and futuristic road design related t…
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…
Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Road link-level
S. Roderick Zhang, Bilal Farooq
To help systematically lower anthropogenic Greenhouse gas (GHG) emissions, accurate and precise GHG emission prediction models have become a key focus of the climate research. The…
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…
Ordered-logit pedestrian stress model for traffic flow with automated vehicles
Kimia Kamal, Bilal Farooq, Mahwish Mudassar +1
An ordered-logit model is developed to study the effects of Automated Vehicles (AVs) in the traffic mix on the average stress level of a pedestrian when crossing an urban street at…
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…