326 citations · 1.2k across the 59 of their papers we have counts for
17 papers · 1 filter
Attention-LSTM for Multivariate Traffic State Prediction on Rural Roads
Elahe Sherafat, Bilal Farooq, Amir Hossein Karbasi +1
Accurate traffic volume and speed prediction have a wide range of applications in transportation. It can result in useful and timely information for both travellers and transportat…
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…
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…
On the Initial Behavior Monitoring Issues in Federated Learning
Ranwa Al Mallah, Godwin Badu-Marfo, Bilal Farooq
In Federated Learning (FL), a group of workers participate to build a global model under the coordination of one node, the chief. Regarding the cybersecurity of FL, some attacks ai…
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…
Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey
Ali Yazdizadeh, Zachary Patterson, Bilal Farooq
We develop ensemble Convolutional Neural Networks (CNNs) to classify the transportation mode of trip data collected as part of a large-scale smartphone travel survey in Montreal, C…
Distributed Classification of Urban Congestion Using VANET
Al Mallah Ranwa, Farooq Bilal, Quintero Alejandro
Vehicular Ad-hoc NETworks (VANET) can efficiently detect traffic congestion, but detection is not enough because congestion can be further classified as recurrent and non-recurrent…
A semi-supervised deep residual network for mode detection in Wi-Fi signals
Arash Kalatian, Bilal Farooq
Due to their ubiquitous and pervasive nature, Wi-Fi networks have the potential to collect large-scale, low-cost, and disaggregate data on multimodal transportation. In this study,…
Semi-supervised GANs to Infer Travel Modes in GPS Trajectories
Ali Yazdizadeh, Zachary Patterson, Bilal Farooq
Semi-supervised Generative Adversarial Networks (GANs) are developed in the context of travel mode inference with uni-dimensional smartphone trajectory data. We use data from a lar…
Mobility Mode Detection Using WiFi Signals
Arash Kalatian, Bilal Farooq
We utilize Wi-Fi communications from smartphones to predict their mobility mode, i.e. walking, biking and driving. Wi-Fi sensors were deployed at four strategic locations in a clos…
Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning
Melvin Wong, Bilal Farooq
In this paper, we implement an information-theoretic approach to travel behaviour analysis by introducing a generative modelling framework to identify informative latent characteri…
Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling
Melvin Wong, Bilal Farooq, Guillaume-Alexandre Bilodeau
Conventional methods of estimating latent behaviour generally use attitudinal questions which are subjective and these survey questions may not always be available. We hypothesize…
An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service
Ismaïl Saadi, Melvin Wong, Bilal Farooq +2
In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal e…