Welcome to Control Group of TransportLab at the University of Sydney…

Cities are becoming smarter in ways that enable us to monitor, analyze, and improve the quality of life in real-time. Our research mainly targets addressing traffic operations in cities. Our multidisciplinary approach combines physics, engineering, and applied maths through data science, modeling mobility dynamics using relevant mathematical and physical tools, and advanced optimization and control techniques.

Group YouTube channel:



  • Guipeng Jiao – Ride sharing in competitive markets; 3/2021 – 2024
  • Yue Yang – Modeling and control of shared and automated e-hailing systems; 3/2021 – 2024
  • Linji Chen – Management of ride-sourcing systems; 3/2020 – 2023
  • Ye Li – Perimeter control and pricing using MFD; 3/2018 – 2022
  • Mengyuan (Derek) Zhu – Location-aware control of ride-sharing systems; 8/2020 – 2022
  • Amir Hosein Valadkhani – Dispatching and relocation in ride-sourcing systems; 2017 – 2021
  • Dong Zhao – Bus bunching modeling and control; 2017 – 2019
  • Jack Wang (The University of Sydney; with David Levinson); 2022 – 2025
  • Ang Ji (The University of Sydney; with David Levinson); 2018 – 2021
  • Mohammad Noaeen (University of Calgary; with Behrouz Far); 2016 – 2021


– Network modelling (MFD) and traffic control


– Modeling and control of point-to-point e-hailing services

  • Ramezani, M., Yang, Y., Elmasry, J., & Tang P. (2022). An empirical study on characteristics of supply in e-hailing markets: a clustering approach. Transportation Letters, DOI: 10.1080/19427867.2022.2079869. (PDF)
  • Chen, L., Valadkhani, A., & Ramezani, M. (2021). Decentralised cooperative cruising of autonomous ride-sourcing fleets. Transportation Research Part C, 131. (PDF) (Video abstract)
  • Nourinejad, M., & Ramezani, M. (2020). Ride-Sourcing modeling and pricing in non-equilibrium two-sided markets. Transportation Research Part B, 132, 340-357. (PDF)
  • Hamedmoghadam, H., Ramezani, M., & Saberi, M. (2019). Revealing latent characteristics of mobility networks with coarse-graining. Scientific reports9(1), 7545. (PDF)
  • Ramezani, M., & Nourinejad, M. (2018). Dynamic modeling and control of taxi services in large-scale urban networks: A macroscopic approach. Transportation Research Part C, 94, 203-219. (PDF)


– Electric, connected and automated vehicles

  • Mohajerpoor, R., & Ramezani, M. (2019). Mixed flow of autonomous and human-driven vehicles: Analytical headway modeling and optimal lane management. Transportation Research Part C, 109, 194-210. (PDF)
  • Ramezani, M., & Ye, E. (2019). Lane density optimization of automated vehicles for highway congestion control. Transportmetrica B: Transport Dynamics, 7(1), 1096-1116. (PDF)
  • Jing, W., Ramezani, M., An, K., & Kim, I. (2018). Congestion patterns of electric vehicles with limited battery capacity, PloS one, vol. 13, no. 3, e0194354. (PDF)
  • Jing, W., An, K., Ramezani, M. & Kim, I. (2017). Location design of electric vehicle charging facilities: A path-distance constrained stochastic user equilibrium approach, Journal of Advanced Transportation. (PDF)
  • Ramezani, M., & Geroliminis, N. (2015). Queue profile estimation in congested urban networks with probe data. Computer‐Aided Civil and Infrastructure Engineering30(6), 414-432. (PDF)
  • Ramezani, M., & Geroliminis, N. (2012). On the estimation of arterial route travel time distribution with Markov chains. Transportation Research Part B46(10), 1576-1590. (PDF)