Control

The Control theme led by Dr. Mohsen Ramezani aims to control the transport system in real-time, thereby making efficient use of networks.

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 to address traffic operations in cities.
Our multidisciplinary approach combines physics, engineering, and applied math through data science, modeling mobility dynamics using relevant mathematical and physical tools, and advanced optimization and control techniques.

– Network Modelling (MFD) and Traffic Control

Publications:

  • Mohajerpoor, R., Saberi, M., Vu, H. L., Garoni, T. M., & Ramezani, M. (2019). H∞ robust perimeter flow control in urban networks with partial information feedback. Transportation Research Part B: Methodological. (PDF)
  • Yildirimoglu, M., & Ramezani, M. (2019). Demand management with limited cooperation among travellers: A doubly dynamic approach. Transportation Research Part B: Methodological. (PDF)
  • Mohajerpoor, R., Saberi, M., & Ramezani, M. (2019). Analytical derivation of the optimal traffic signal timing: Minimizing delay variability and spillback probability for undersaturated intersections. Transportation Research Part B: Methodological119, 45-68. (PDF)
  • Aalipour, A., Kebriaei, H., & Ramezani, M. (2018). Analytical Optimal Solution of Perimeter Traffic Flow Control Based on MFD Dynamics: A Pontryagin’s Maximum Principle Approach. IEEE Transactions on Intelligent Transportation Systems. (PDF)
  • Ramezani, M., de Lamberterie, N., Skabardonis, A., & Geroliminis, N. (2017). A link partitioning approach for real-time control of queue spillbacks on congested arterials. Transportmetrica B: transport dynamics5(2), 177-190. (PDF)
  • Ramezani, M., Haddad, J., & Geroliminis, N. (2015). Dynamics of heterogeneity in urban networks: aggregated traffic modeling and hierarchical control. Transportation Research Part B: Methodological74, 1-19. (PDF)
  • Yildirimoglu, M., Ramezani, M., & Geroliminis, N. (2015). Equilibrium analysis and route guidance in large-scale networks with MFD dynamics. Transportation Research Part C59, 404-420. (PDF)
  • Haddad, J., Ramezani, M., & Geroliminis, N. (2013). Cooperative traffic control of a mixed network with two urban regions and a freeway. Transportation Research Part B: Methodological54, 17-36. (PDF)
  • Geroliminis, N., Haddad, J., & Ramezani, M. (2013). Optimal perimeter control for two urban regions with macroscopic fundamental diagrams: A model predictive approach. IEEE Transactions on Intelligent Transportation Systems14(1), 348-359. (PDF)

– Ride-Sourcing and Ride-sharing

Publications:

  • Nourinejad, M., & Ramezani, M. (2019). Ride-Sourcing modeling and pricing in non-equilibrium two-sided markets. Transportation Research Part B: Methodological. (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: Emerging Technologies,94, 203-219. (PDF)

– Connected and Automated Vehicles for Traffic State Estimation and Control

Publications:

  • Ramezani, M., & Ye, E. (2019). Lane density optimization of automated vehicles for highway congestion control. Transportmetrica B: transport dynamics. (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 B: Methodological46(10), 1576-1590. (PDF)