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Xiaolin is a Lecturer in Civil Engineering at The University of Queensland. Her research focuses on transport modelling, predictive analytics, and the integration of emerging vehicle technologies into urban mobility.

Her current research examines autonomous modular vehicles and the design of integrated transport systems. She develops modelling and optimisation methods for door-to-door feeder services connected to high-capacity modular transit corridors, with the aim of improving transport efficiency, accessibility, sustainability and equity.

Before joining UQ, Xiaolin was a Postdoctoral Associate at the Center for Interacting Urban Networks at New York University Abu Dhabi. She received her PhD in Transport Modelling from the University of Sydney and holds a Master’s degree in Transportation Systems from the Technical University of Munich.

Xiaolin also has industry experience at the BMW AG Research and Innovation Centre in Munich. She has published in leading transport journals, including Transportation Research Part B and Transportation Research Part E, and serves as a reviewer for Transportation Letters, Transportmetrica A and Transportmetrica B.

Dr. Mark Hickman is the TAP Chair and Professor of Transport Engineering within the School of Civil Engineering at the University of Queensland. Prof. Hickman has taught courses and performed research in public transit planning and operations, travel demand modelling, and traffic engineering. His areas of research interest and expertise include public transit planning and operations, urban transportation planning and modelling, and the development of sustainable transport innovations and policies.

Jiwon Kim is an Associate Professor in Transport Engineering and the Director of Higher Degree by Research in the School of Civil Engineering at the University of Queensland. She was a DECRA Fellow (2019-2022) sponsored by the Australian Research Council. She joined UQ in 2014 after completing her PhD research at Northwestern University. Prior to joining Northwestern, she worked at Samsung C&T (Engineering & Construction Group). She received Bachelor’s and Master’s degrees in civil engineering from Korea University.

Her research interests broadly encompass the application of Artificial Intelligence and Machine Learning (AI/ML) to enhance prediction, automation, and insight generation in transportation and urban mobility. She is passionate about developing intelligent autonomous systems that facilitate real-time traffic management and control, mobility service optimization, and traveller support. Her current research explores the potential of deep learning, reinforcement learning, and other cutting-edge AI/ML approaches to achieve these objectives.

Dr. Saeed Mohammadian is a postdoctoral research fellow in Transport Engineering. His primary research is on understanding freeway traffic flow dynamics and their safety implication with a special focus on the role of human factors. As part of his doctoral work, he has conducted critical meta-research on the state-of-the-art of macroscopic traffic flow modelling, from both theoretical and practical perspectives. The central part of his doctoral thesis develops unifiable mathematical models of traffic flow dynamics and rear-end crashes and presents new insight into the role of human factors on complex traffic phenomena and traffic safety.

His main work at UQ involves conducting research on driver interactions with connected and automated vehicles and studying the implications for both congestions and crashes. He is currently involved in several research projects related to freeway traffic flow modelling and control and safety assessment. His research outcomes can be explicitly utilized in traffic control research for moving towards faster and safer roads.

Dr Weiming Zhao is an ARC DECRA Fellow (2026 - 2029) in the School of Civil Engineering at the University of Queensland. His research sits at the intersection of robotics, human behaviour, and infrastructure, focusing on Intelligent Transportation Systems (ITS) and the interactions between Connected and Automated Vehicles (CAVs) and human drivers in mixed traffic. Using control theory and machine learning, including Model Predictive Control and Mathematica-based modelling, he develops trajectory planning solutions for CAVs at intersections and highway merging areas, aiming to improve safety, efficiency, and energy use in both urban and highway settings.

Dr Zhao completed his PhD at the University of Leeds, followed by a postdoctoral position at Aalto University. He joined UQ as a postdoc in 2022, before commencing his DECRA fellowship in 2026. His current DECRA project, Make Automated Vehicles Personalised and Socialised, investigates how control methods for automated vehicles can align with human driver behaviour to build trust.