Dr Minh Le Kieu
I am a Lecturer in Transport Analytics at the Department of Civil and Environmental Engineering, University of Auckland. I have been contributing to the development and delivery of large research projects, such as an ERC Horizon 2020 in the UK (https://dust.leeds.ac.uk/), and the Premier Innovation Initiative in Australia (http://adait.io/inno-pii.html).
My research focuses on the use of data analytics and computer simulation techniques to solve urban complex problems. In Data Analytics, I adapt and develop machine learning methods to analyse Big data of individuals, such as Smart Card, mobile phone or social network data. In Computer Simulation, I am specialised in the development of data-driven Agent-based models, a cutting-edge simulation technique of heterogeneous agents interacting with each other, to explain complex urban systems.
For the access to my slides, papers and codes, please find my personal webpage here: https://leminhkieu.github.io/
Research | Current
- Big Data analytics
- Machine Learning
- Artificial Intelligence
- Agent-Based Modelling
- Computer Simulation / Digital Twins
- Data-driven systems
- Infrastructure Systems
More on my research can be found here: https://bit.ly/2020-MK
And please find my Google Scholar page for an updated list of my publications: https://scholar.google.com.au/citations?hl=en&user=mjpqmUsAAAAJ
Teaching | Current
- CIVIL 761. Planning and Design of Transport Facilities.
Traffic signal practice/safety audits, two-way highway planning, arterial traffic management, modelling and simulation and traffic flow.
CIVIL 771. Planning and Managing Transport
An advanced course on integrating land use planning and transport provisions, including planning for different land use trip types and parking, travel demand management techniques, and intelligent transport systems applications. An independent project applies this specialised knowledge towards planning, designing and managing transport infrastructure in a Territorial Local Authority (TLA) area.
CIVIL 361. Transportation Engineering 2
Planning for land transport facilities and urban development. Arrangement of street networks and environmental areas. Basic operational analyses at priority and signalised intersections for vehicles and pedestrians. Highway capacity analyses. Parking design. Introduction to transportation planning modelling.
CIVIL 759. Highway and Transportation Design
Economic and environmental assessments of transport projects. Land transport funding in NZ. Road safety engineering. Crash reduction and prevention methods. Pavement asset management. Pavement rehabilitation techniques. Heavy-duty pavements, highway drainage and chip seal design.
CIVIL 774. Smart Infrastructure Analytics (CIVIL 763 from 2022)
This paper will provide the fundamental knowledge to equip students in engineering to handle data analytics challenges. It serves as an introductory course for graduate students with little or no programming experience, and who have no previous knowledge of data analytics but wish to acquire the basics of these skills.
I am looking for motivated PhD students to work with me on research topics related, but not limited to, the integration of data analytics/machine learning/artificial intelligence with computer simulation to solve complex urban transport problems. Students with high GPA will have a good chance of obtaining the University's scholarships. Applications also welcome from country-specific scholarship students (e.g. Vietnam's VIED Project 911 fellowship and China's CSC, more information can be found at: https://www.auckland.ac.nz/en/study/international-students/scholarships-loans-and-funding/country-specific-scholarships-and-funding0.html)
- 2020, Premium Best Paper Awards (2018-2020), IET Journal of Intelligent Transport Systems for paper: "Deep learning methods in transportation domain: a review", DOI: 10.1049/iet-its.2018.0064
- 2019, Newton Fund Travel grant, Alan Turing Institute, UK
- 2015, Outstanding Higher Research Degree award, QUT, Australia
- 2011, Sparbanksstiftelsen Alpha International Scholarship, Swedbank, Sweden
- 2011, Eastern Asia Society for Transportation Studies (EASTS): Outstanding young researcher award, Korea
- 2010, Wala och Folke Danielsson fund, Linkö̈ping University, Sweden
- 2020-2022, FRDF fund, Faculty of Engineering, University of Auckland, NZ
- 2019-2022, Urban Transport Modelling for Sustainable Well-being in Hanoi, British Academy, UK
- 2019-2020, GSMA Data Science Challenge, Alan Turing Institute, UK
- 2018-2019, Improvements of Public Transport Information & Priority System, TfNSW, Australia
- 2018, NSW On-demand Transport Pilot project, TfNSW, Australia
- 2016, Traffic microsimulation and planning of the Pacific Motorway, Transport and Main Roads (TMR), Australia
- 2015, Traffic estimation using bus GPS data, TMR, Australia
Transport Representative, Teaching and Learning Quality Committee (TLQC)
Department Lead, Auckland Open Day, University of Auckland
Conjoint Student Coordinator, University of Auckland
Exchange Student Coordinator, University of Auckland
Managing Guest Editor, Computers, Environment and Urban Systems Journal
Program Committee, International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
Program Committee, ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (ACM SIGSIM PADS)
Chair, Agent-Based Modelling of Urban Systems (ABMUS)
Areas of expertise
- Agent-based modelling
- Big data analytics
- Machine learning
- Deep Learning / AI
Selected publications and creative works (Research Outputs)
- Malleson, N., Minors, K., Kieu, L. M., Ward, J. A., West, A. A., & Heppenstall, A. (2020). Simulating crowds in real time with agent-based modelling and a particle filter. JASSS, 23 (3), 1-20. 10.18564/jasss.4266
- Kieu, L. M., Ou, Y., Truong, L. T., & Cai, C. (2020). A class-specific soft voting framework for customer booking prediction in on-demand transport. Transportation Research Part C: Emerging Technologies, 114, 377-390. 10.1016/j.trc.2020.02.010
- Kieu, L.-M., Malleson, N., & Heppenstall, A. (2020). Dealing with uncertainty in agent-based models for short-term predictions. Royal Society open science, 7 (1)10.1098/rsos.191074
- Clay, R., Kieu, L. M., Ward, J. A., Heppenstall, A., & Malleson, N. (2020). Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10.1007/978-3-030-49778-1_6
- Le-Minh, K., Dong, N., Malleson, N., & Chung, E. (2019). A stochastic schedule-following simulation model of bus routes. TRANSPORTMETRICA B-TRANSPORT DYNAMICS, 7 (1), 1588-1610. 10.1080/21680566.2019.1670118
- Nguyen, H., Bentley, C., Kieu, L. M., Fu, Y., & Cai, C. (2019). Deep Learning System for Travel Speed Predictions on Multiple Arterial Road Segments. Transportation Research Record, 2673 (4), 145-157. 10.1177/0361198119838508
- Nguyen, H., Kieu, L. M., Wen, T., & Cai, C. (2018). Deep learning methods in transportation domain: A review. IET Intelligent Transport Systems, 12 (9), 998-1004. 10.1049/iet-its.2018.0064
- Kieu, L. M., & Cai, C. (2018). Stochastic collective model of public transport passenger arrival process. IET Intelligent Transport Systems, 12 (9), 1027-1035. 10.1049/iet-its.2018.0085