Focus
How mobility system adapt to change and uncertainty?
Mobility systems are increasingly shaped by disruption, behavioural change, technological transitions, and deep uncertainty. Traditional planning and operational approaches often struggle when travel demand, system conditions, and external contexts change rapidly.
At the Mobility Informatics Lab, we study how mobility systems can adapt under such conditions. We develop computational and AI methods that combine data, digital twins, system modelling, behavioural intelligence, and decision support for transport planning and operations.
Our research aims to support more resilient, efficient, and sustainable mobility systems across multiple domains, including public transport, railways, traffic systems and emerging mobility service
Mission
Our mission is to advance the science and practice of adaptive mobility systems under deep uncertainty.
We focus on how mobility demand, infrastructure, operations, and decision-making evolve under disruptions, long-term transitions, and nonstationary conditions. Through computational modelling, AI, and digital intelligence, we seek to build methods that are not only predictive, but adaptive, interpretable, and useful for real-world planning and management.
Research Themes
Adaptive Mobility under Deep Uncertainty
We study how mobility systems evolve under disruption, behavioural change, and uncertain futures, with a focus on adaptation in demand, planning, and system performance.
Digital Twins and System Intelligence
We develop digital twins and intelligent system models for monitoring, forecasting, simulation, control, and decision support in mobility systems.
AI for Mobility Knowledge and Decision Support
We explore AI methods, including large language models, knowledge graphs, and machine learning, to support scientific discovery, behavioural modelling, and transport decision-making.
Application Domains
Public Transport and Rail Systems
We apply these methods to public transport and railway systems, focusing on operations, reliability, disruption management, passenger information, and digital transformation.
Traffic Systems and Network Operations
We study urban traffic, motorway systems, and network-level operations using data, simulation, and AI-based control and decision-support methods.