Associate Professor
Department of Civil and Environmental Engineering
Department of Industrial Systems Engineering and Management
National University of Singapore
The Lab for Urban Mobility Systems (LUMOS) at the National University of Singapore develops data-driven models and AI-enabled decision-making methods for transportation and logistics systems. We integrate reinforcement learning, mathematical optimisation, and transport system and behaviour modelling to address challenges in planning, operations, and management. Our goal is to advance efficient, equitable, and reliable mobility through research that connects methodological innovation with practical needs.
Our lab's research activities has been profiled at IEEE Intelligent Transportation Systems Magazine.Our research connects infrastructure planning, real-time operations, and traveller behaviour across urban mobility, public transport, logistics, and air transport, with a focus on emerging technologies such as autonomous vehicles (AVs) and electric vehicles (EVs).
The research team develops multidisciplinary approaches to address research questions with theoretical contributions and real-world implications for efficient and sustainable transportation system planning and management.
LUMOS aims to disseminate new insights, knowledge, and tools to academia, industry, government, and research organizations worldwide.
Journal | Oct 2026
This research is published in Transportation Research Part C: Emerging Technologies.
News | July 2026
ISTTT Podium Session 18: Dynamic Senior-Centric Type Matching Optimization for Mobility-on-Demand Management in Aging Societies.
News | June 2026
Congratulations!!
Journal | Dec 2025
This research is published in Transportation Research Part B: Methodological.
News | Dec 2025
We are thrilled to announce that our team has had two papers accepted by the ISTTT26, widely regarded as the premier venue in the field of transportation theory. The accepted works are: 1) Dynamic Senior-Centric Type Matching Optimization for Mobility-on-Demand Management in Aging Societies; and 2) Model-Supplementary Learning for Congestion Pricing: A Bias-Aware Natural Policy Gradient Approach. Congratulations!!
We are recruiting phd students and postdoctoral fellows. We are looking for researchers with strong interests and expertise in traffic simulation, mathematical modelling and programming, and data-driven optimization approaches. If you are interested in joining LUMOS, please contact Dr. Liu Yang directly by emailing to iseliuy@nus.edu.sg or ceelya@nus.edu.sg.