The Control theme led by Dr. Mohsen Ramezani aims to improve the efficiency of transport systems in real-time.

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.


  • 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 – 2021
  • Jiaqing Lu – Traffic Economics; 3/2021 – 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
  • 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 Ride-Sourcing and Ride-sharing Systems

  • Chen, L., Valadkhani, A., & Ramezani, M. (2021). Decentralised cooperative cruising of autonomous ride-sourcing fleets. Transportation Research Part C, 131. (PDF)
  • 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)


– Connected and Automated Vehicles for Traffic State Estimation and Control

  • 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)
  • 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)