Skip to menu Skip to content Skip to footer
Professor Zuduo Zheng
Professor

Zuduo Zheng

Email: 
Phone: 
+61 7 344 31371

Overview

Background

Professor Zuduo Zheng is TAP Chair (Deputy) sponsored by Queensland Department of Transport and Main Roads, and Professor in the School of Civil Engineering, a current ARC Future Fellow, and a former ARC DECRA Research Fellow. He is currently a member of the College of Experts, the Australian Research Council.

His research primarily focuses on:

  1. traffic flow theory, modelling, simulation and optimisation;
  2. understanding emerging, disruptive, and intelligent mobility technologies’ impact on traffic efficiency, traffic safety, energy consumption, vehicle emissions, etc.;
  3. developing essential theories, the foundational algorithms and analytics that can seamlessly integrate future mobilities into the existing transportation systems;
  4. establishing a new breed of control strategies tailored to maximise the power of the connected environment and vehicle automation; and
  5. complex systems modeling and the design of adaptable, controllable, resilient, and sustainable infrastructure systems (intelligent transportation systems and smart city particularly in the context of the 2032 Olympic and Paralympic Games).

He has been listed as the Top 2% of Scientists in Logistics and Transportation by Scopus & Stanford University since 2020. He has won many prestigious awards, and serves/served as editor, guest editor or editorial board member of several prestigious journals, including Transportation Research Part B, Transportation Research Part C, Analytic Methods in Accident Research , IEEE Transactions on Intelligent Transportation Systems, etc.

More information about Professor Zheng's research activities and engagements can be found here.

Availability

Professor Zuduo Zheng is:
Available for supervision
Media expert

Qualifications

  • Doctor of Philosophy, Arizona State University

Research interests

  • Traffic flow theories and operations, with a focus on emerging technologies

  • Travel behaviour, strategic transport planning and modeling, and decision making

  • Traffic operation and management for big events (e.g., Olympic and Paralympic Games)

  • Traffic safety

  • Advanced data analysis techniques (e.g., mathematical modeling, econometrics, numerical optimization) in transport engineering

  • Research on research (or meta-research)

Works

Search Professor Zuduo Zheng’s works on UQ eSpace

130 works between 2006 and 2025

21 - 40 of 130 works

2024

Conference Publication

A novel feature-sharing auto-regressive neural network for enhanced car-following model calibration

Wang, Chengming, Jia, Dongyao, Zheng, Zuduo, Wang, Wei and Wang, Shangbo (2024). A novel feature-sharing auto-regressive neural network for enhanced car-following model calibration. 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), Edmonton, AB, Canada, 24-27 September 2024. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ITSC58415.2024.10920170

A novel feature-sharing auto-regressive neural network for enhanced car-following model calibration

2024

Journal Article

Building a less intimidating cycling environment for women: a structural equation modeling analysis based on a VR-based laboratory experiment

Khademi, Navid, Naeinizadeh, Mohammadamin, Firoozi Yeganeh, Sayna, Behmanesh, Arian, Ekramifard, Ali, Chaiyasarn, Krisada, Zheng, Zuduo, Arbabpour Bidgoli, Mohammad, Azarmi, Hossein, Tarvirdizadeh, Bahram and Hadi, Alireza (2024). Building a less intimidating cycling environment for women: a structural equation modeling analysis based on a VR-based laboratory experiment. Transportation Research Part F: Traffic Psychology and Behaviour, 100, 431-457. doi: 10.1016/j.trf.2023.12.001

Building a less intimidating cycling environment for women: a structural equation modeling analysis based on a VR-based laboratory experiment

2024

Journal Article

ABAFT: an adaptive weight-based fusion technique for travel time estimation using multi-source data with different confidence and spatial coverage

Respati, Sara, Chung, Edward, Zheng, Zuduo and Bhaskar, Ashish (2024). ABAFT: an adaptive weight-based fusion technique for travel time estimation using multi-source data with different confidence and spatial coverage. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 28 (6), 867-880. doi: 10.1080/15472450.2023.2228198

ABAFT: an adaptive weight-based fusion technique for travel time estimation using multi-source data with different confidence and spatial coverage

2023

Journal Article

Continuum modeling of freeway traffic flows: state-of-the-art, challenges and future directions in the era of connected and automated vehicles

Mohammadian, Saeed, Zheng, Zuduo, Haque, Md. Mazharul and Bhaskar, Ashish (2023). Continuum modeling of freeway traffic flows: state-of-the-art, challenges and future directions in the era of connected and automated vehicles. Communications in Transportation Research, 3 100107, 1-25. doi: 10.1016/j.commtr.2023.100107

Continuum modeling of freeway traffic flows: state-of-the-art, challenges and future directions in the era of connected and automated vehicles

2023

Journal Article

A hybrid modelling framework for the estimation of dynamic origin–destination flows

Kumarage, Sakitha, Yildirimoglu, Mehmet and Zheng, Zuduo (2023). A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological, 176 102804, 1-27. doi: 10.1016/j.trb.2023.102804

A hybrid modelling framework for the estimation of dynamic origin–destination flows

2023

Journal Article

Stability and extension of a car-following model for human-driven connected vehicles

Sun, Jie, Zheng, Zuduo, Sharma, Anshuman and Sun, Jian (2023). Stability and extension of a car-following model for human-driven connected vehicles. Transportation Research Part C: Emerging Technologies, 155 104317, 1-17. doi: 10.1016/j.trc.2023.104317

Stability and extension of a car-following model for human-driven connected vehicles

2023

Journal Article

Car-following crash risk analysis in a connected environment: a Bayesian non-stationary generalised extreme value model

Nazir, Faizan, Ali, Yasir, Sharma, Anshuman, Zheng, Zuduo and Haque, Md Mazharul (2023). Car-following crash risk analysis in a connected environment: a Bayesian non-stationary generalised extreme value model. Analytic Methods in Accident Research, 39 100278, 1-19. doi: 10.1016/j.amar.2023.100278

Car-following crash risk analysis in a connected environment: a Bayesian non-stationary generalised extreme value model

2023

Journal Article

NET-RAT: Non-equilibrium traffic model based on risk allostasis theory

Mohammadian, Saeed, Zheng, Zuduo, Haque, Mazharul and Bhaskar, Ashish (2023). NET-RAT: Non-equilibrium traffic model based on risk allostasis theory. Transportation Research Part A: Policy and Practice, 174 103731, 103731. doi: 10.1016/j.tra.2023.103731

NET-RAT: Non-equilibrium traffic model based on risk allostasis theory

2023

Journal Article

Demand and state estimation for perimeter control in large-scale urban networks

Kumarage, Sakitha, Yildirimoglu, Mehmet and Zheng, Zuduo (2023). Demand and state estimation for perimeter control in large-scale urban networks. Transportation Research Part C: Emerging Technologies, 153 104184, 104184. doi: 10.1016/j.trc.2023.104184

Demand and state estimation for perimeter control in large-scale urban networks

2023

Journal Article

Calibrating lane-changing models: two data-related issues and a general method to extract appropriate data

Ali, Yasir, Zheng, Zuduo and Bliemer, Michiel C.J. (2023). Calibrating lane-changing models: two data-related issues and a general method to extract appropriate data. Transportation Research Part C: Emerging Technologies, 152 104182, 104182. doi: 10.1016/j.trc.2023.104182

Calibrating lane-changing models: two data-related issues and a general method to extract appropriate data

2023

Conference Publication

Demand estimation for perimeter control in large-scale traffic networks

Kumarage, Sakitha, Yildirimoglu, Mehmet and Zheng, Zuduo (2023). Demand estimation for perimeter control in large-scale traffic networks. 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Nice, France, 14-16 June 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/mt-its56129.2023.10241660

Demand estimation for perimeter control in large-scale traffic networks

2023

Journal Article

Autonomous vehicle’s impact on traffic: empirical evidence from Waymo Open Dataset and implications from modelling

Hu, Xiangwang, Zheng, Zuduo, Chen, Danjue and Sun, Jian (2023). Autonomous vehicle’s impact on traffic: empirical evidence from Waymo Open Dataset and implications from modelling. IEEE Transactions on Intelligent Transportation Systems, 24 (6), 1-14. doi: 10.1109/tits.2023.3258145

Autonomous vehicle’s impact on traffic: empirical evidence from Waymo Open Dataset and implications from modelling

2023

Journal Article

Assessing a connected environment's safety impact during mandatory lane-changing: a block maxima approach

Ali, Yasir, Haque, Md. Mazharul and Zheng, Zuduo (2023). Assessing a connected environment's safety impact during mandatory lane-changing: a block maxima approach. IEEE Transactions on Intelligent Transportation Systems, 24 (6), 6639-6649. doi: 10.1109/tits.2022.3147668

Assessing a connected environment's safety impact during mandatory lane-changing: a block maxima approach

2023

Journal Article

Guest Editorial Introduction to the Special Issue on Deployment of Connected and Automated Vehicles in Mixed Traffic Environment and the Implications on Traffic Safety and Efficiency

Farah, Haneen, Olstam, Johan and Zheng, Zuduo (2023). Guest Editorial Introduction to the Special Issue on Deployment of Connected and Automated Vehicles in Mixed Traffic Environment and the Implications on Traffic Safety and Efficiency. IEEE Transactions on Intelligent Transportation Systems, 24 (6), 6432-6435. doi: 10.1109/tits.2023.3276596

Guest Editorial Introduction to the Special Issue on Deployment of Connected and Automated Vehicles in Mixed Traffic Environment and the Implications on Traffic Safety and Efficiency

2023

Journal Article

A Bayesian hierarchical approach to the joint modelling of Revealed and stated choices

Li, Zili, Washington, Simon P., Zheng, Zuduo and Prato, Carlo G. (2023). A Bayesian hierarchical approach to the joint modelling of Revealed and stated choices. Journal of Choice Modelling, 47 100419, 100419. doi: 10.1016/j.jocm.2023.100419

A Bayesian hierarchical approach to the joint modelling of Revealed and stated choices

2023

Journal Article

Fifth-order A-WENO path-conservative central-upwind scheme for behavioral non-equilibrium traffic models

Chu, Shaoshuai, Kurganov, Alexander, Mohammadian, Saeed and Zheng, Zuduo (2023). Fifth-order A-WENO path-conservative central-upwind scheme for behavioral non-equilibrium traffic models. Communications in Computational Physics, 33 (3), 692-732. doi: 10.4208/cicp.OA-2022-0263

Fifth-order A-WENO path-conservative central-upwind scheme for behavioral non-equilibrium traffic models

2023

Journal Article

Stability evolution of car-following models considering asymmetric driving behavior

Sun, Jie, Zheng, Zuduo and Sun, Jian (2023). Stability evolution of car-following models considering asymmetric driving behavior. Transportation Research Record: Journal of the Transportation Research Board, 2677 (8), 361-371. doi: 10.1177/03611981231156584

Stability evolution of car-following models considering asymmetric driving behavior

2023

Journal Article

Space sharing between pedestrians and micro-mobility vehicles: a systematic review

Zhang, Cheng, Du, Bo, Zheng, Zuduo and Shen, Jun (2023). Space sharing between pedestrians and micro-mobility vehicles: a systematic review. Transportation Research Part D: Transport and Environment, 116 103629, 1-18. doi: 10.1016/j.trd.2023.103629

Space sharing between pedestrians and micro-mobility vehicles: a systematic review

2023

Journal Article

Estimating link flows in road networks with synthetic trajectory data generation: inverse reinforcement learning approach

Zhong, Miner, Kim, Jiwon and Zheng, Zuduo (2023). Estimating link flows in road networks with synthetic trajectory data generation: inverse reinforcement learning approach. IEEE Open Journal of Intelligent Transportation Systems, 4, 14-29. doi: 10.1109/ojits.2022.3233904

Estimating link flows in road networks with synthetic trajectory data generation: inverse reinforcement learning approach

2023

Journal Article

A learning-based discretionary lane-change decision-making model with driving style awareness

Zhang, Yifan, Xu, Qian, Wang, Jianping, Wu, Kui, Zheng, Zuduo and Lu, Kejie (2023). A learning-based discretionary lane-change decision-making model with driving style awareness. IEEE Transactions on Intelligent Transportation Systems, 24 (1), 68-78. doi: 10.1109/tits.2022.3217673

A learning-based discretionary lane-change decision-making model with driving style awareness

Funding

Current funding

  • 2026 - 2029
    Shaping net-zero cities with safe and efficient micromobility solutions
    ARC Discovery Projects
    Open grant
  • 2026 - 2030
    From error-prone human driving to error-free superhuman-like self-driving
    ARC Future Fellowships
    Open grant
  • 2024 - 2028
    Safe and efficient eco-driving using connected and automated vehicles
    ARC Discovery Projects
    Open grant

Past funding

  • 2023 - 2025
    A real-time traffic signal system for safe and efficient intersections (ARC Linkage Project administered by Queensland University of Technology)
    Queensland University of Technology
    Open grant
  • 2023
    Design of a driving simulation experiment for mixed traffic using SimCCAD - Experiment A
    University of Massachusetts Lowell
    Open grant
  • 2023
    Design of a driving simulation experiment for mixed traffic using SimCCAD - Experiment B
    University of Massachusetts Lowell
    Open grant
  • 2022 - 2024
    Framework and Conceptual Solution for Integrating Road Network Operations Planning into Real-time Traffic Management
    iMove Cooperative Research Centre
    Open grant
  • 2022 - 2024
    DynaMix-FM, dynamic mixed reality environment for future mobility (ARC LIEF application led by Queensland University of Technology)
    Queensland University of Technology
    Open grant
  • 2021 - 2023
    Urban Freight Shifts
    iMove Cooperative Research Centre
    Open grant
  • 2021 - 2024
    Unifying Traffic Modelling and Safety Management for Safer and Faster Roads
    ARC Discovery Projects
    Open grant
  • 2020 - 2025
    Transport Academic Partnership 2020-2025
    Queensland Department of Transport and Main Roads
    Open grant
  • 2020 - 2025
    Transport Innovation and Research Hub
    Brisbane City Council
    Open grant
  • 2018 - 2019
    Modelling mixed traffic traditional, connected and automated vehicles
    ARC Discovery Early Career Researcher Award
    Open grant

Supervision

Availability

Professor Zuduo Zheng is:
Available for supervision

Looking for a supervisor? Read our advice on how to choose a supervisor.

Available projects

  • Fundamental issues in calibrating and validating traffic flow models

  • An integrated platform of traffic flow simulation, communication simulation, and driving simulator

  • Modelling transportation systems with traditional, connected and autonomous vehicles

    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 .

Supervision history

Current supervision

  • Doctor Philosophy

    SEEN ¿ A Sustainable Energy-Efficient Novel Driver Behaviour Model for the Future of Transport: Development, Assessment, and Application

    Principal Advisor

    Other advisors: Associate Professor Jiwon Kim

  • Doctor Philosophy

    Dynamic lane-changing behavior modeling framework on urban arterials using deep reinforcement learning

    Principal Advisor

    Other advisors: Dr Mehmet Yildirimoglu

  • Doctor Philosophy

    Fundamental Issues in Calibrating and Validating Microscopic Traffic Dynamics of Automated and Human-driven Vehicles

    Principal Advisor

    Other advisors: Dr Mehmet Yildirimoglu

  • Doctor Philosophy

    Motorized Two-wheeler safety - a comparative risk study of the occupants in Indian and Australian context

    Principal Advisor

  • Doctor Philosophy

    Improving Traffic Dynamics Simulation Quality for Enhanced Safety and Efficiency: Evaluation, Modelling, and Application

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Crash Data Analytics Based Roadside Risk Modelling Oriented Towards Practical Roadside and Associated Road Geometric Design Using Machine Learning

    Associate Advisor

    Other advisors: Professor Mark Hickman

  • Doctor Philosophy

    Traffic modelling and control in next-generation cities

    Associate Advisor

    Other advisors: Dr Mehmet Yildirimoglu

Completed supervision

Media

Enquiries

Contact Professor Zuduo Zheng directly for media enquiries about:

  • connected and automated vehicles
  • Traffic congestion

Need help?

For help with finding experts, story ideas and media enquiries, contact our Media team:

communications@uq.edu.au