I'm a Transportation & Data Science researcher, with a Ph.D. from the University of British Columbia (UBC). My work sits at the intersection of urban systems modelling, machine learning, and data-driven transportation planning.

I build models that help cities move smarter — from predicting travel behaviour and transit resilience to simulating long-term land use impacts. My research has been published in top-tier journals, presented at major international conferences, and covered by outlets including The Globe & Mail.

🎓  Ph.D.         →  Transportation Engineering, UBC (2025)
📍  Location      →  British Columbia, Canada
📄  Publications  →  35+ peer-reviewed journal articles & conference papers

Languages & Frameworks

Machine Learning & Data Science

Simulation & Modelling

GIS & Remote Sensing

Towards a Next Generation of Integrated Urban Model

A Python-based, agent-driven framework that unifies land use dynamics, transportation demand, and demographic evolution into a single long-term simulation engine. Benchmarked machine learning against logistic regression for demographic microsimulation; multi-task learning achieved 40% faster travel-demand prediction and 20% higher accuracy over single-output baselines.

Agent-based modelling of energy consumption under pandemic-induced work-from-home

Coupled EnergyPlus with an agent-based model to simulate household energy consumption across the Central Okanagan region. Found a 29% increase in residential energy demand during COVID-19 lockdowns — covered by UBC News (Nov. 2022).

Machine learning for real-time incident clearance forecasting

Spatio-temporal analysis of 110,000+ incident records (Houston, TX, 2004–2013). Developed ensemble and deep learning models achieving a best-case clearance time error of 14 minutes — supporting real-time traffic management.

System-level impacts of LRT disruptions on service quality

Evaluated Edmonton Transit System performance under extreme disruption scenarios (full LRT shutdowns, increased headways), measuring cascading effects on delays and cancelled trips to inform resilience planning policy.

Satellite + ground-station analysis of pollutant changes pre/post lockdown

Multi-year, multi-station analysis using satellite imagery and ground observations across the UAE. Documented 20–50% improvement in key pollutant levels during lockdown — published across 3 journals including Remote Sensing Applications and Sustainability.

Full list available on Google Scholar · 30+ articles in journals including Transportation Research Part D, Expert Systems with Applications, Sustainable Cities and Society, Transportation, Engineering Applications of AI, Remote Sensing, and more.

Highlights:

• Khalil, M. & Fatmi, M.R. (2025). How effective are discrete-continuous multi-task learning compared to single-output models? Expert Systems with Applications, 274, 127002.
• Khalil, M., Fatmi, M.R., & Orvin, M. (2025). Developing and microsimulating demographic dynamics for an IUM. Transportation, 52(4), 1621–1655.
• Khalil, M. & Fatmi, M.R. (2022). How residential energy consumption has changed due to COVID-19. Sustainable Cities and Society, 81, 103832.
• Khalil, M., Hamad, K., & Shanableh, A. (2019). Developing ML models to predict roadway traffic noise. Transportation Research Record.

🥈 2nd Best Academic Research — Dubai Award for Sustainable Transport (DAST), 2019
🥈 2nd Best Academic Research — Dubai Award for Sustainable Transport (DAST), 2018
🎓 Graduate Scholarship — University of British Columbia, 2021–2025

• TRB Committee AED50 — Artificial Intelligence & Advanced Computing Applications (Friend)
• TRB Committee AEP80 — Transportation-Related Noise and Vibration (Friend)
• Peer Reviewer — Multiple transportation and sustainability journals
• Judge — ITE-uAlberta Research Poster Competition (2026)
• External Reviewer — UBC Okanagan Graduate Studies (2024)