I study urban mobility systems as a complex, data-rich transportation problem. My work combines machine learning, simulation, and network-based analysis to understand congestion, mobility services, EV charging, and the resilience of urban transport infrastructure.

For presentations and invited lectures, see Talks.

Societal Relevance and SDGs

My research contributes most directly to SDG 11, Sustainable Cities and Communities. I study urban traffic congestion because it affects the reliability, accessibility, and livability of cities. My work focuses on predicting congestion, explaining where it forms, and evaluating how future urban infrastructures can better absorb traffic-related disruptions.

The work also relates to SDG 3, Good Health and Well-being, because congestion is linked to air quality, productive time loss, and urban livability. I study congestion as a transport-system factor that shapes the conditions under which people live and move in cities.

A methodological part of my research contributes to SDG 12, Responsible Consumption and Production. In my work on complexity-aware traffic prediction, I study how model choice can be matched to the intrinsic complexity of the prediction task. This supports more computationally efficient and deployable traffic prediction workflows.

My work also supports SDG 17, Partnerships for the Goals, through reproducibility, open datasets, open-source code, and transferable methods. Since my 2021 survey on deep learning for traffic congestion, I have tried to use freely available datasets and release paper-specific code whenever possible, so that research can be reproduced, compared, and extended across cities.

Future Directions

I plan to focus on transport research that remains useful beyond isolated case studies, with methods that are transferable, interpretable, and practical for real planning settings.

• I plan to study how open and crowdsourced mobility datasets can support congestion prediction across cities with different data environments.
• I plan to develop interpretable methods that connect model predictions to transport-system features, especially road-network structure and infrastructure constraints.
• I plan to study the resilience of interconnected urban infrastructures, such as transport and electric-vehicle charging systems, under non-recurrent congestion and other disruptions.

Global Collaborators

I maintain an active collaboration network globally across transport AI, simulation, mobility data, and infrastructure resilience. The figure below shows the current affiliations of my most recent collaborators.