Overview
Background
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.
Availability
- Professor Mark Hickman is:
- Available for supervision
- Media expert
Fields of research
Qualifications
- Bachelor of Science, Massachusetts Institute of Technology
- Masters (Coursework) of Science, Massachusetts Institute of Technology
- Doctor of Philosophy, Massachusetts Institute of Technology
Research interests
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Public transport
Operations and service planning of public transport services
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Travel modelling
Modelling of transport systems, transport supply and demand, traveller behaviour
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Sustainable transport engineering
Transport engineering methods to reduce greenhouse gas emissions and energy use, while also improving transport safety and efficiency
Works
Search Professor Mark Hickman’s works on UQ eSpace
2023
Conference Publication
Why is teamwork so hard to teach well at university?
Coulter, Beverly, Birkett, Greg, Boden, Marie, Chen, Shaun, Fialho Leandro Alves Teixeira, Fred, Fleming, Melanie, Hickman, Mark and Hope Borchardt, Lilly (2023). Why is teamwork so hard to teach well at university?. 34th Australasian Association For Engineering Education Conference, Gold Coast, QLD Australia, 3-6 December 2023.
2023
Journal Article
MSGNN: A Multi-structured Graph Neural Network model for real-time incident prediction in large traffic networks
Tran, Thanh, He, Dan, Kim, Jiwon and Hickman, Mark (2023). MSGNN: A Multi-structured Graph Neural Network model for real-time incident prediction in large traffic networks. Transportation Research Part C: Emerging Technologies, 156 104354. doi: 10.1016/j.trc.2023.104354
2023
Journal Article
Estimation of macroscopic fundamental diagram solely from probe vehicle trajectories with an unknown penetration rate
Saffari, Elham, Yildirimoglu, Mehmet and Hickman, Mark (2023). Estimation of macroscopic fundamental diagram solely from probe vehicle trajectories with an unknown penetration rate. IEEE Transactions on Intelligent Transportation Systems, 24 (12), 14970-14981. doi: 10.1109/tits.2023.3303439
2023
Conference Publication
MSGNN: a multi-structured graph neural network model for real-time incident prediction in large traffic networks
Tran, Thanh, He, Dan, Kim, Jiwon and Hickman, Mark (2023). MSGNN: a multi-structured graph neural network model for real-time incident prediction in large traffic networks. Transportation Research Board Annual Meeting, Washington, DC, United States, 8-12 January 2023.
2023
Other Outputs
Opportunities to Decarbonise Queensland’s Road Freight Task: developing a strategic roadmap to a smooth transition to low and zero emission technologies
Adhikari Smith, Dia and Hickman, Mark (2023). Opportunities to Decarbonise Queensland’s Road Freight Task: developing a strategic roadmap to a smooth transition to low and zero emission technologies. Transport Academic Partnership (TAP) Project: Opportunities to decarbonise Queensland’s road freight task Brisbane, QLD Australia: The University of Queensland.
2022
Conference Publication
Data in mobility as a service: a real-world trial in Queensland, Australia
Rahbar, Maisie, Lim, Kai Li, Whitehead, Jake and Hickman, Mark (2022). Data in mobility as a service: a real-world trial in Queensland, Australia. Australasian Transport Research Forum 2022 Proceedings, Adelaide, SA Australia, 28-30 September 2022. Canberra, ACT Australia: Australasian Transport Research Forum.
2022
Conference Publication
Online traffic incident prediction with hybrid graph-based neural network and continual learning
Tran, Thanh, He, Dan, Kim, Jiwon and Hickman, Mark (2022). Online traffic incident prediction with hybrid graph-based neural network and continual learning. Australasian Transport Research Forum, Adelaide, SA Australia, 28-30 September 2022.
2022
Journal Article
Data fusion for estimating Macroscopic Fundamental Diagram in large-scale urban networks
Saffari, Elham, Yildirimoglu, Mehmet and Hickman, Mark (2022). Data fusion for estimating Macroscopic Fundamental Diagram in large-scale urban networks. Transportation Research Part C: Emerging Technologies, 137 103555, 103555. doi: 10.1016/j.trc.2022.103555
2022
Other Outputs
Planning a Transition to Low and Zero Emission Construction Machinery
Adhikari Smith, Dia, Whitehead, Jake and Hickman, Mark (2022). Planning a Transition to Low and Zero Emission Construction Machinery. Brisbane, QLD Australia: The University of Queensland. doi: 10.14264/93110de
2021
Other Outputs
A feasibility assessment of transitioning to low or zero emission truck technologies in Queensland
Adhikari Smith, Dia, Whitehead, Jake and Hickman, Mark (2021). A feasibility assessment of transitioning to low or zero emission truck technologies in Queensland. Transport Academic Partnership (TAP) Project: Opportunities to decarbonise Queensland’s road freight task Brisbane, QLD, Australia: The University of Queensland.
2021
Journal Article
Relationship between programmed heavy vehicle inspections and traffic safety
Assemi, Behrang, Hickman, Mark and Paz, Alexander (2021). Relationship between programmed heavy vehicle inspections and traffic safety. Transportation Research Record, 2675 (10) 03611981211016458, 1420-1430. doi: 10.1177/03611981211016458
2021
Conference Publication
Data-driven traffic incident prediction with hybrid graph-based neural network
Tran, Thanh, He, Dan, Kim, Jiwon and Hickman, Mark (2021). Data-driven traffic incident prediction with hybrid graph-based neural network. Australasian Transport Research Forum, Brisbane, QLD, Australia, 8-10 December 2021.
2021
Journal Article
Advanced systems in public transport, with a touch of data
Trépanier, Martin and Hickman, Mark (2021). Advanced systems in public transport, with a touch of data. Public Transport, 13 (3), 455-456. doi: 10.1007/s12469-021-00288-8
2020
Journal Article
A methodology for identifying critical links and estimating macroscopic fundamental diagram in large-scale urban networks
Saffari, Elham, Yildirimoglu, Mehmet and Hickman, Mark (2020). A methodology for identifying critical links and estimating macroscopic fundamental diagram in large-scale urban networks. Transportation Research Part C: Emerging Technologies, 119 102743, 102743. doi: 10.1016/j.trc.2020.102743
2020
Conference Publication
Enhancing GPS-Assisted Travel Data Collection through Smartphones
Assemi, Behrang, Safi, Hamid, Paz, Alexander, Mesbah, Mahmoud, Ferreira, Luis and Hickman, Mark (2020). Enhancing GPS-Assisted Travel Data Collection through Smartphones. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, 20-23 September 2020. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/ITSC45102.2020.9294423
2020
Journal Article
Modeling Mode Choice of Air Passengers’ Ground Access to Brisbane Airport
Pasha, Md Mosabbir, Hickman, Mark D. and Prato, Carlo G. (2020). Modeling Mode Choice of Air Passengers’ Ground Access to Brisbane Airport. Transportation Research Record, 2674 (11), 756-767. doi: 10.1177/0361198120949534
2020
Journal Article
Network design with elastic demand and dynamic passenger assignment to assess the performance of transit services
Ranjbari, Andisheh, Hickman, Mark and Chiu, Yi-Chang (2020). Network design with elastic demand and dynamic passenger assignment to assess the performance of transit services. Journal of Transportation Engineering, Part A: Systems, 146 (5) 04020030, 04020030. doi: 10.1061/jtepbs.0000326
2020
Journal Article
Improving alighting stop inference accuracy in the trip chaining method using neural networks
Assemi, Behrang, Alsger, Azalden, Moghaddam, Mahboobeh, Hickman, Mark and Mesbah, Mahmoud (2020). Improving alighting stop inference accuracy in the trip chaining method using neural networks. Public Transport, 12 (1), 89-121. doi: 10.1007/s12469-019-00218-9
2020
Journal Article
A network design problem formulation and solution procedure for intercity transit services
Ranjbari, Andisheh, Hickman, Mark and Chiu, Yi-Chang (2020). A network design problem formulation and solution procedure for intercity transit services. Transportmetrica A: Transport Science, 16 (3), 1156-1175. doi: 10.1080/23249935.2020.1719547
2020
Journal Article
Transport-related walking among young adults: when and why?
Assemi, Behrang, Zahnow, Renee, Zapata-Diomedi, Belen, Hickman, Mark and Corcoran, Jonathan (2020). Transport-related walking among young adults: when and why?. BMC Public Health, 20 (1) 244, 244. doi: 10.1186/s12889-020-8338-0
Funding
Current funding
Supervision
Availability
- Professor Mark Hickman is:
- Available for supervision
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Supervision history
Current supervision
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Master Philosophy
Visualisation of passenger and freight transport flows
Principal Advisor
Other advisors: Associate Professor Jiwon Kim
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Doctor Philosophy
Crash Data Analytics Based Roadside Risk Modelling Oriented Towards Practical Roadside and Associated Road Geometric Design Using Machine Learning
Principal Advisor
Other advisors: Professor Zuduo Zheng
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Doctor Philosophy
Methods for siting low and zero emission vehicle refueling infrastructure
Principal Advisor
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Doctor Philosophy
Methods for Public Transport Operations Planning and Management
Principal Advisor
Other advisors: Associate Professor Jiwon Kim
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Doctor Philosophy
Equitable policies for a just transition to electric vehicle fleets: Exploring market mechanisms and revenue-based targets for full electrification of ride-hailing and delivery services in Delhi by 2030
Principal Advisor
Other advisors: Dr Kai Li Lim
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Master Philosophy
Deep Representation Learning of Spatio-Temporal Trajectory Data
Associate Advisor
Other advisors: Associate Professor Jiwon Kim
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Doctor Philosophy
Graph-based Learning Platform for Real-time Traffic Incident Prediction and Management
Associate Advisor
Other advisors: Associate Professor Jiwon Kim
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Doctor Philosophy
An efficient traffic modelling framework for large-scale networks
Associate Advisor
Other advisors: Dr Mehmet Yildirimoglu
Completed supervision
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2023
Doctor Philosophy
Pedestrian and Cyclist Interaction with Autonomous Vehicles
Principal Advisor
Other advisors: Honorary Professor Carlo Prato
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2023
Master Philosophy
Estimating Transit Travel Time Reliability in Mixed Traffic Environments
Principal Advisor
Other advisors: Dr Mehmet Yildirimoglu
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2021
Doctor Philosophy
Modelling Air Passengers' Ground Access to and from Brisbane Airport
Principal Advisor
Other advisors: Honorary Professor Carlo Prato
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2020
Doctor Philosophy
A Bayesian model to improve transit assignment using smart card data
Principal Advisor
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2019
Doctor Philosophy
Modelling Influence of a Bus Rapid Transit System on Urban Land Use: A Case Study of Brisbane
Principal Advisor
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2019
Doctor Philosophy
Strategic Approach In Analysing The Demand For Park-and-Ride Facilities
Principal Advisor
Other advisors: Honorary Professor Carlo Prato
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2018
Doctor Philosophy
Leveraging Connected Vehicle Technology and Model Predictive Control to Improve Traffic Network Performance
Principal Advisor
Other advisors: Associate Professor Jiwon Kim
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2018
Doctor Philosophy
Contributions to Behavioural Freight Transport Modelling
Principal Advisor
Other advisors: Honorary Professor Carlo Prato
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2022
Doctor Philosophy
Towards an Autonomous World: Vehicle Users' Preferences Regarding Autonomous Driving
Associate Advisor
Other advisors: Professor Zuduo Zheng
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2022
Doctor Philosophy
Estimating the Macroscopic Fundamental Diagram (MFD) in Large-Scale Urban Networks
Associate Advisor
Other advisors: Dr Mehmet Yildirimoglu
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2020
Master Philosophy
Automatic detection and analysis of long-term changes in travel patterns of public transport passengers using Smart-Card data
Associate Advisor
Other advisors: Associate Professor Jiwon Kim
Media
Enquiries
Contact Professor Mark Hickman directly for media enquiries about:
- Public transport
- Traffic engineering
- Transport planning
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