Rate My Professor Mark Hickman

Mark Hickman's research focuses on public transit planning and operations, urban transportation planning and modelling, travel demand modelling, traffic engineering, and sustainable transport innovations and policies. His work addresses key challenges such as Mobility as a Service (MaaS), congestion pricing, electric vehicle adoption, traffic incident prediction, heavy vehicle inspections, smart card data applications in transit, origin-destination estimation, behavioral activity-travel simulation models, city logistics, and decarbonisation of road freight. He has contributed significantly to the field through high-impact publications, including the book chapter 'Transit origin-destination estimation' (2017) in Public Transport Planning with Smart Card Data, 'MSGNN: A Multi-structured Graph Neural Network model for real-time incident prediction in large traffic networks' (2023, Transportation Research Part C: Emerging Technologies), 'Data fusion for estimating Macroscopic Fundamental Diagram in large-scale urban networks' (2022, Transportation Research Part C), and 'Exploring the heterogeneity in subscription and travel behaviours of MaaS users: A segmentation approach and thematic analysis – A case study of University of Queensland, Australia' (2026, Journal of Public Transportation). His scholarship has accumulated over 6,000 citations on Google Scholar. Hickman leads major funded projects, including the Transport Academic Partnership (2020-2025) with the Queensland Department of Transport and Main Roads, ARC Linkage Project on real-time analytics of urban trajectory data (2019-2024), and iMove CRC initiatives on MaaS trials and low-emission vehicle strategies, advancing practical solutions for sustainable transport systems.