Staff Profile
Dr Duo Li
Senior Lecturer Transport Engineering
- Email: duo.li@ncl.ac.uk
- Address: Future Mobility Group
School of Engineering
Room 4.011 Stephenson Building
Newcastle University
NE1 7RU
Previous Academic Positions
2022–2023 Senior Lecturer in Transport Systems, Nottingham Trent University, United Kingdom
2020–2022 Research Associate, University of Cambridge, United Kingdom
2018–2019 Alexander von Humboldt Fellowship, German Aerospace Centre (DLR), Germany
2015–2018 Academic Positions, Chang`an University, China
Qualifications
2012 – 2015 PhD: Transport Engineering, University of Auckland, New Zealand
2010 – 2011 MSc: Transport Engineering, University of Queensland, Australia
2006 – 2010 BSc: Transport Engineering, Huazhong University of Science and Technology, China
Other activities
Senior Member, Institute of Electrical and Electronics Engineers (IEEE)
Charted Member, the Chartered Institute of Logistics and Transport (CILT)
Associate Editor, Transportation Research Record (TRR)
Academic Editor, Journal of Traffic and Transportation Engineering
Fellow, the World Economic Forum (WEF) Sustainable Tourism Council
Academic Committee Member, National Annual Forum of Urban Planning Society of China
Session Chair, Conference of the International Federation of Operational Research Societies
Technical Committee Member, World Transport Convention (WRC)
Program Committee Member, SUMO User Conference
Technical Committee Member, International Conference of Transport Infrastructure and Materials
I am currently seeking motivated PhD/MPhil students. If you're keen to explore this field, please connect with me.
Research Areas
• Urban mobility data computing
• Motorway traffic flow control and management
• Digital twins for smart transport
• Resilience and sustainability in transport systems
• Connected, Autonomous, Shared, and Electric (CASE) transport modelling and operations
• Data-centric approaches in transportation
• AI Applications in transportation, e.g., transformer-like architectures, graph neural networks, auto machine learning (AutoML) and deep reinforcement learning