Profile
Bilal Farooq is Canada Research Chair in Disruptive Transportation Technologies and Services and an Associate Professor at Toronto Metropolitan University. Always quick to take things apart to see how they work internally, Farooq calls this approach 'learning through curiosity,' and it still drives him today. Farooq's spirit of inquiry animates his work in traffic flow simulation and prediction, connected autonomous vehicles, and urban congestion.
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Workshop 3: Urban Systems Research This in-person workshop at Concordia University was convened to provide general updates and garner feedback on Bridging Divides projects taking under the Place and Infrastructure Theme Group. The workshop included presentations on the following:
Thomas Zhao: VR based neighbourhood preference analysis
Isaac Otchere: Customized Smartphone App for Travel Surveys
Houman Haghi: Choice set generation problem
Lina Waqfi: Migration location choice literature
Samuel Guardino: Multiagent microsimulation of urban systems
Farbod Abbasi: Population synthesis
Tareq Alsaleh: Travel mode shift analysis
Mahan Mollajafari: Travel behaviour analysis using pre-trained ML models
Alemtsehay Subhatu: Ontology for infrastructure needs of migrants
Thet workshop was also attended by researchers from McGill, ETS, and Concordia. Additionally, professionals from ministry of transportation Quebec, ARTM, and a few more agencies in the region attended. Toronto Metropolitan University, Concordia University Activity 2024-10-04 Farooq, B. ,
Patterson, Z. ,
Zhao, T. ,
Otchere, I. ,
Haghi, H. , Lina Waqfi,
" Samuel Guardino
" ,
Abbasi, F. ,
" Tareq Alsaleh
" ,
Mollajafari, M. ,
Subhatu, A. Multi-Agent Large-Scale Urban Systems Simulation for Montréal Luke GuardinoTRS3 5.4 Toronto Metropolitan University Conference 2024-10-10 TRS3 5.4 Behavioural modelling of automated to manual control transition in conditionally automated driving Toronto Metropolitan University Publication 2023-04-01 Immigrant’s choice behaviour At the subcommittee meeting on Stated Response Surveys of the 2025 Annual General Meeting of the Transportation Research Board on Jan 06, 2025, I organized a special presentation session and associated discussion on the immigrant’s choice behaviour, especially in connection with travel and location choices. It gathered a strong interest and we came to the conclusion that the topic is very important for countries such as US, Canada, Australia, and New Zealand, but is rarely addressed by our research community. We also provided further information to our international research community on the BD program and how it addresses some of these issues.TRS3 4.1, TRS3 4.3, TRS3 5.3, TRS3 5.4, TRS3 7.1 Toronto Metropolitan University Conference 2025-02-16 TRS3 4.1, TRS3 4.3, TRS3 5.3, TRS3 5.4, TRS3 7.1 Bridging Divides TMU Fall Retreat A one-day interactive retreat for Bridging Divides researchers, affiliated researchers, and HQPs at Toronto Metropolitan University (TMU). The retreat, led by Karen Soldatic and Bilal Farooq, is designed to foster collaboration, innovation, and knowledge sharing across disciplines within the Bridging Divides research program. Attendees will participate in workshops on innovative practices in health science and engineering, present their own research, network, and engage in feedback sessions to promote interdisciplinary solutions. The event encourages proposals from students and early-career researchers for short research presentations, especially in fields outside health sciences and engineering.
Other Toronto Metropolitan University Conference 2024-10-10 Other Migration Disrupted: How technological transformation is reshaping human mobility Migration Disrupted was an interdisciplinary, hybrid conference held at Toronto Metropolitan University from May 7-9, 2024, organized by CERC Migration and Bridging Divides. The conference focused on how advanced digital technologies (ADTs) are transforming human mobility, with an emphasis on their impact on migrant integration, citizenship, employment, health care, and urban experience in Canada and beyond. Through a series of panels, fireside chats, and breakout sessions, researchers, policymakers, and civil society leaders examined both opportunities and challenges created by technological transformation, including digital divides, the ethics of artificial intelligence, infrastructure for inclusive cities, and the future of migrant work. Video recordings of sessions are available.
Other Toronto Metropolitan University, University of Alberta, University of British Columbia, Concordia University Conference 2024-05-07 Triandafyllidou, A. ,
Bagheri, E. ,
Gruzd, A. , 249,
Abu-Laban, Y. ,
Agrawal, S. ,
Farooq, B. , Joel Dissanayake,
Banerjee, R. ,
Huot, S. ,
Mazalek, A. ,
Zhuang, Z. ,
Rockwell, G. ,
Wong, J. ,
Paquet, M. Other The effects of urban form on public transportation demand in a developing city Toronto Metropolitan University Publication 2025-03-01 Exploring the combined effects of major fuel technologies, eco-routing, and eco-driving for sustainable traffic decarbonization in downtown Toronto Toronto Metropolitan University Publication 2025-01-25 Robustness Analysis of Deep Learning Models for Population Synthesis Toronto Metropolitan University Publication 2025-01-01 A Coalition Game for On-demand Multi-modal 3D Automated Delivery System We introduce a multi-modal autonomous delivery optimization framework as a coalition game for a fleet of UAVs and ADRs operating in two overlaying networks to address last-mile delivery in urban environments, including high-density areas, road-based routing, and real-world operational challenges. The problem is defined as multiple depot pickup and delivery with time windows constrained over operational restrictions, such as vehicle battery limitation, precedence time window, and building obstruction. Subsequently, the coalition game theory is applied to investigate cooperation structures among the modes to capture how strategic collaboration among vehicles can improve overall routing efficiency. To do so, a generalized reinforcement learning model is designed to evaluate the cost-sharing and allocation to different coalitions for which sub-additive property and non-empty core exist. Our methodology leverages an end-to-end deep multi-agent policy gradient method augmented by a novel spatio-temporal adjacency neighbourhood graph attention network and transformer architecture using a heterogeneous edge-enhanced attention model. Conducting several numerical experiments on last-mile delivery applications, the result from the case study in the city of Mississauga shows that despite the incorporation of an extensive network in the graph for two modes and a complex training structure, the model addresses realistic operational constraints and achieves high-quality solutions compared with the existing transformer-based and heuristics methods and can perform well on non-homogeneous data distribution, generalizes well on the different scale and configuration, and demonstrate a robust performance under stochastic scenarios subject to wind speed and direction. Toronto Metropolitan University Publication 2024-12-22 Decoding pedestrian stress on urban streets using electrodermal activity monitoring in virtual immersive reality Toronto Metropolitan University Publication 2024-12-12 Mohsen Nazemi, Bara Rababah, Daniel S Ramos, Tianhong Zhao,
Farooq, B. Simulation Models for Sustainable, Resilient, and Optimized Global Electric Vehicles Supply Chain While the transition to electric vehicles (EVs) is essential for decarbonizing the transportation system, the production and distribution of EVs entail substantial carbon costs. To ensure these emissions are accurately accounted for and effectively mitigated, this research introduces a digital twin of the EV's supply chain, addressing a critical gap in current EV life cycle analyses and providing the first comprehensive quantification of its environmental sustainability and resilience. This simulation model replicates global market dynamics and captures the complexity and uncertainty of the EV supply chain, enabling a thorough evaluation of its carbon footprint, sustainability, resilience, and what-if counterfactual scenarios for alternative market structures. The results reveal that average supply chain emissions range from 6.42 to 6.94 Kg e-CO2/KWh across different battery technologies. Additionally, the mass flow analysis shows unbalanced dependencies at all supply phases, with one geographical region significantly dominating the supply chain structure, highlighting the current supply chain architecture's low resilience and high vulnerability. In light of these findings, the study introduces an optimization model for hub and resource allocation configuration, effectively reducing vulnerability levels and supply chain emissions by up to 80%. Toronto Metropolitan University Publication 2024-09-09 Corrigendum to “Ordinal-ResLogit: Interpretable deep residual neural networks for ordered choices” [J. Choice Model., 50 (2024) 100454] Toronto Metropolitan University Publication 2024-08-15 Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems Toronto Metropolitan University Publication 2024-06-24 A deep causal inference model for fully-interpretable travel behaviour analysis 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 CAusal infeRence mOdel for traveL behavIour aNAlysis (CAROLINA), a framework that explicitly models causality in travel behaviour, enhances predictive accuracy, and maintains interpretability by leveraging causal inference, deep learning, and traditional discrete choice modelling. Within this framework, we introduce a Generative Counterfactual model for forecasting human behaviour by adapting the Normalizing Flow method. Through the case studies of virtual reality-based pedestrian crossing behaviour, revealed preference travel behaviour from London, and synthetic data, we demonstrate the effectiveness of our proposed models in uncovering causal relationships, prediction accuracy, and assessing policy interventions. Our results show that intervention mechanisms that can reduce pedestrian stress levels lead to a 38.5% increase in individuals experiencing shorter waiting times. Reducing the travel distances in London results in a 47% increase in sustainable travel modes. Toronto Metropolitan University Publication 2024-05-02 Driver heterogeneity in willingness to give control to conditional automation Toronto Metropolitan University Publication 2024-04-04 Decoding Driver Takeover Behaviour in Conditional Automation with Immersive Virtual Reality The safe transition from conditional automation to manual driving control is significantly intertwined with the vehicle's lateral and longitudinal dynamics. The transition may occur as a result of a system-initiated mandatory takeover (MTOR) or as a driver-initiated discretionary takeover (DTOR). In either condition, the takeover process entails differing cognitive demands and may affect the driving behaviour differently. This study analyzes driving stability and perceived mental workload in 304 takeover attempts recorded from 104 participants within virtual and immersive reality environments. Adopting an exploratory approach, this dynamic simulator study employs a mixed factorial design. Utilizing a deep neural network-based survival analysis with SHAP interpretability, the study investigated the influence of covariates on perception-reaction time (PRT), distinguishing between safe and unsafe control transition and offering insights into the temporal dynamics of these shifts. The distributions of key parameters in experimental groups were analyzed and factors influencing the perceived mental workload were estimated using multivariate linear regression. The findings indicate a notable decrease in the risk of unsafe takeovers (described by a longer PRT) when drivers have prior control-transition experience and familiarity with Automated Vehicles (AVs). However, driver's prior familiarity and experience with AVs only decreased the perceived mental workload associated with DTOR, with an insignificant impact on the cognitive demand of MTOR. Furthermore, multitasking during automated driving significantly elevated the cognitive demand linked to DTOR and led to longer PRT in MTOR situations. Toronto Metropolitan University Publication 2024-02-25 Towards a large-scale fused and labeled dataset of human pose while interacting with robots in shared urban areas Over the last decade, Autonomous Delivery Robots (ADRs) have transformed conventional delivery methods, responding to the growing e-commerce demand. However, the readiness of ADRs to navigate safely among pedestrians in shared urban areas remains an open question. We contend that there are crucial research gaps in understanding their interactions with pedestrians in such environments. Human Pose Estimation is a vital stepping stone for various downstream applications, including pose prediction and socially aware robot path-planning. Yet, the absence of an enriched and pose-labeled dataset capturing human-robot interactions in shared urban areas hinders this objective. In this paper, we bridge this gap by repurposing, fusing, and labeling two datasets, MOT17 and NCLT, focused on pedestrian tracking and Simultaneous Localization and Mapping (SLAM), respectively. The resulting unique dataset represents thousands of real-world indoor and outdoor human-robot interaction scenarios. Leveraging YOLOv7, we obtained human pose visual and numeric outputs and provided ground truth poses using manual annotation. To overcome the distance bias present in the traditional MPJPE metric, this study introduces a novel human pose estimation error metric called Mean Scaled Joint Error (MSJE) by incorporating bounding box dimensions into it. Findings demonstrate that YOLOv7 effectively estimates human pose in both datasets. However, it exhibits weaker performance in specific scenarios, like indoor, crowded scenes with a focused light source, where both MPJPE and MSJE are recorded as 10.89 and 25.3, respectively. In contrast, YOLOv7 performs better in single-person estimation (NCLT seq 2) and outdoor scenarios (MOT17 seq1), achieving MSJE values of 5.29 and 3.38, respectively. Toronto Metropolitan University Publication 2024-01-28 Urban Infrastructure Services and Migrant Integration Enabler Ontology Toronto Metropolitan University Publication 2024-01-01 Workshop synthesis: Virtual reality, visualization and interactivity in travel survey, where we are and possible future directions This paper summarizes the discussion of the workshop B16 "Virtual reality, visualization and interactivity in travel survey, where we are and possible future directions". The workshop involved three sessions over the course of the conference. First two sessions discussed the current state of research, challenges, and possible future directions. The last session focused on synthesis of a research agenda for the next five years. It was concluded that the VR/AR tools and platforms provide a unique opportunity to proactively investigate the travel behaviour changes that are expected to happen due to the development and adoption of disruptive mobility technologies and services as well as virtual worlds and digital twins. Toronto Metropolitan University Publication 2024-01-01 Ordinal-ResLogit: Interpretable deep residual neural networks for ordered choices Toronto Metropolitan University Publication 2023-12-03 Canada Research Chair (CRC) renewed Toronto Metropolitan University Award 2023-08-29 The 4th Workshop on Urban Systems Research TRS3 5.1, TRS3 5.2, TRS3 5.3 Toronto Metropolitan University Conference 2025-10-06 TRS3 5.1, TRS3 5.2, TRS3 5.3 61st ISOCARP World Planning Congress Panel: Cities & Regions in Action – Planning Pathways to Resilience and Quality of LifeOther Toronto Metropolitan University, University of Alberta Conference 2025-12-01 Other Bridging Divides in Mobility: Migration, Behavioural Change, and Sustainable Futures TRS3 4.1 Toronto Metropolitan University Conference 2025-12-01 TRS3 4.1
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