Associate Professor Zuduo Zheng

Associate Professor Transport Eng

School of Civil Engineering
Faculty of Engineering, Architecture and Information Technology
zuduo.zheng@uq.edu.au
+61 7 344 31371

Overview

Associate Professor Zuduo Zheng is an Associate Professor in the School of Civil Engineering, the University of Queensland, and a DECRA Research Fellow sponsored by the Australian Research Council. His research interests lie primarily in the areas of traffic flow modelling, travel behaviour and decision making, advanced data analysis techniques (e.g., mathematical modelling, econometrics, numerical optimisation) in transport engineering, and meta-research. He is on the editorial advisory boards of several transport journals including Transportation Research Part B, Transportation Research Part C, Heliyon, and International Journal of Intelligent Transportation Systems Research.

Research Interests

  • Traffic flow theories and operations
  • ravel behaviour, strategic transport planning and modeling, and decision making
  • Advanced data analysis techniques (e.g., mathematical modeling, econometrics, numerical optimization) in transport engineering
  • Traffic safety
  • Research on research (or meta-research)

Qualifications

  • Doctor of Philosophy, Arizona State University

Publications

View all Publications

Grants

View all Grants

Supervision

  • Doctor Philosophy

  • Doctor Philosophy

  • Doctor Philosophy

View all Supervision

Available Projects

  • Connected vehicles communicate with neighboring vehicles (V2V) and infrastructure (V2I), and automated vehicles drive themselves without the need for human intervention for some of or all the driving tasks. Recent and rapid technology advancements are transporting the concept of CAVs from science fiction to scientific fact. While the valuable information collected and communicated by CAVs provides unprecedented opportunities for optimising traffic flows, the lack of a robust, theory-based operational plan for mixed traffic flow will lead to more chaotic roads. Firstly, much of our modelling of the traffic flow and traffic operations of traditional vehicles will be obsolete at best, and dangerously misleading at worst. Secondly, for connected vehicles, driver’s response and compliance to information received is critical, e.g., the total ignorance of a driver to the information would render connectivity useless; and for automated vehicles, depending on the level of automation, drivers need to frequently switch between two different roles: as a driver to execute driving tasks, and as a supervisor to monitor the driving environment, and when needed, resume vehicular control. Previous studies have reported that automation may lead to overreliance, erratic workload, skill degradation, and reduced situation awareness. And finally, the impact of CAVs on transport systems, while revolutionary, is also evolutionary. For the foreseeable future, traditional vehicles will need to co-exist with CAVs in a mixed traffic flow, which is likely to be more dynamic and volatile, posing serious operational, control, and safety challenges.

    This project addresses this knowledge deficit, and develops an analytical tool with the capability of accurately modelling mixed traffic flow. This new knowledge and model are prerequisites to effective operation and control of traffic flow of traditional, connected, and automated vehicles .

View all Available Projects

Publications

Book

Journal Article

Conference Publication

  • Sharma, Anshuman, Ali, Yasir, Saifuzzaman, Mohammad, Zheng, Zuduo and Haque, Mazharul Md. (2017). Human factors in modelling mixed traffic of traditional, connected, and automated vehicles. In: Daniel N. Cassenti, Proceedings of the AHFE 2017 International Conference on Human Factors in Simulation and Modeling. AHFE 2017 International Conference on Human Factors in Simulation and Modeling, Los Angeles, United States, (262-273). 17-21 July 2017. doi:10.1007/978-3-319-60591-3_24

  • Lee, Jinwoo (Brian), Zheng, Zuduo, Kashfi, Syeed, Chia, Jason and Yi, Rong (2015). Observation of bus ridership in the aftermath of the 2011 Floods in Southeast Queensland, Australia. In: Paul H. Barnes and Ashantha Goonetilleke, 9th Annual International Conference of the International Institute for Infrastructure Renewal and Reconstruction. 9th Annual International Conference of the International Institute for Infrastructure Renewal and Reconstruction, Brisbane, Australia, (382-390). 8-11 July 2013.

  • Wang, Zhe and Zheng, Zuduo (2014). "Privilege to Kill" phenomenon on developing countries' roads: a preliminary case study of China. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, (1602-1607). 8-11 October 2014. doi:10.1109/ITSC.2014.6957922

  • Dollisson, John, Cox, William and Zheng, Zuduo (2013). Comparison of motorists' and cyclists’ perception of bicycle safety. In: 36th Australasian Transport Research Forum (2013 ATRF). 36th Australasian Transport Research Forum (2013 ATRF), Brisbane, Australia, (). 2-4 October 2013.

  • Liu, Chuanli and Zheng, Zuduo (2013). Public acceptance towards congestion charge: a case study of Brisbane. In: Lei Zhang, Heng Wei, Zhiheng Li and Yi Zhang, Intelligent and Integrated Sustainable Multimodal Transportation Systems: Proceedings From the 13Th Cota International Conference of Transportation Professionals (CICTP2013). 13th COTA International Conference of Transportation Professionals (CICTP 2013), Shenzhen, China, (2811-2822). Aug 13-16, 2013. doi:10.1016/j.sbspro.2013.08.314

  • Zheng, Zuduo (2012). Empirical analysis on relationship between traffic conditions and crash occurrences. In: Baohua Mao, Zongzhong Tian, Haijun Huang and Ziyou Gao, Proceedings of the 3rd International Conference on Traffic and Transportation Studies (ICTTS 2002). 8th International Conference on Traffic and Transportation Studies (ICTTS 2012), Beijing, China, (302-312). 1-3 August 2012. doi:10.1016/j.sbspro.2012.04.103

  • Su, Dongcai, Dong, Junwei and Zheng, Zuduo (2009). Shrinking neighborhood evolution-A novel stochastic algorithm for numerical optimization. In: IEEE Congress on Evolutionary Computation, Trondheim Norway, (3300-3305). May 18-21, 2009. doi:10.1109/CEC.2009.4983363

Grants (Administered at UQ)

PhD and MPhil Supervision

Current Supervision

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Connected vehicles communicate with neighboring vehicles (V2V) and infrastructure (V2I), and automated vehicles drive themselves without the need for human intervention for some of or all the driving tasks. Recent and rapid technology advancements are transporting the concept of CAVs from science fiction to scientific fact. While the valuable information collected and communicated by CAVs provides unprecedented opportunities for optimising traffic flows, the lack of a robust, theory-based operational plan for mixed traffic flow will lead to more chaotic roads. Firstly, much of our modelling of the traffic flow and traffic operations of traditional vehicles will be obsolete at best, and dangerously misleading at worst. Secondly, for connected vehicles, driver’s response and compliance to information received is critical, e.g., the total ignorance of a driver to the information would render connectivity useless; and for automated vehicles, depending on the level of automation, drivers need to frequently switch between two different roles: as a driver to execute driving tasks, and as a supervisor to monitor the driving environment, and when needed, resume vehicular control. Previous studies have reported that automation may lead to overreliance, erratic workload, skill degradation, and reduced situation awareness. And finally, the impact of CAVs on transport systems, while revolutionary, is also evolutionary. For the foreseeable future, traditional vehicles will need to co-exist with CAVs in a mixed traffic flow, which is likely to be more dynamic and volatile, posing serious operational, control, and safety challenges.

    This project addresses this knowledge deficit, and develops an analytical tool with the capability of accurately modelling mixed traffic flow. This new knowledge and model are prerequisites to effective operation and control of traffic flow of traditional, connected, and automated vehicles .