4 citations · 6 across the 5 of their papers we have counts for
5 papers
Copula-ResLogit: A Deep-Copula Framework for Unobserved Confounding Effects
Kimia Kamal, Bilal Farooq
A key challenge in travel demand analysis is the presence of unobserved factors that may generate non-causal dependencies, obscuring the true causal effects. To address the issue,…
A deep causal inference model for fully-interpretable travel behaviour analysis
Kimia Kamal, Bilal Farooq
Transport policy assessment often involves causal questions, yet the causal inference capabilities of traditional travel behavioural models are at best limited. We present the deep…
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…
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…