Welcome to Lab for Urban Mobility Systems (LUMOS) at NUS

Dr. Liu Yang (刘杨)

Associate Professor

Department of Civil and Environmental Engineering

Department of Industrial Systems Engineering and Management

National University of Singapore

The mission of the Lab for Urban Mobility Systems (LUMOS) is to advance intelligent transportation systems, formulate new design and operational strategies, devise effective solutions to transportation problems, and bridge academic communities with industry to improve the mobility, reliability, and sustainability of transportation systems.

Our lab's research activities has been profiled at IEEE Intelligent Transportation Systems Magazine.

Our Research

We focus on future urban mobility and transport systems, which cover the areas of shared mobility system operation and design, travel demand and congestion management, and data-driven transportation system modeling and analysis.

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.

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Lab News

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We present our latest research on dynamic senior-centric type matching optimisation in ISTTT26!

News | July 2026

ISTTT Podium Session 18: Dynamic Senior-Centric Type Matching Optimization for Mobility-on-Demand Management in Aging Societies.

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New paper! Equitable transportation network design for signal-free smart intersections

Journal | Dec 2025

This research is published in Transportation Research Part B: Methodological.

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Two of our papers on AI for Transportation are accepted by ISTTT26!

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!!

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New paper! Interpretable machine learning for traffic congestion prediction: Unveiling the impact of different COVID-19 periods

Journal | Dec 2025

This research is published in Communications in Transportation Research.

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Joining LUMOS

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.