Control

Welcome to the 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:

https://www.youtube.com/channel/UC1VIPFbdw0cRK8J06DLaHEA

Team

Postdoc

• Dr. Hongjun Yu; 2022 – 2023
• Dr. Reza Mohajerpoor; 2018 – 2020

PhD

• Elnaz Emami; 10/2022 – 2026
• Alireza Soltani; 10/2022 – 2026
• Amin Shaer; 10/2022 – 2026
• 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
• Dr. Ye Li – Perimeter control and pricing using MFD; 3/2018 – 2022

MPhil

• Ruihao Zeng; 09/2022 – 2023
• 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

Co-supervision

• Abdullah Zare Andaryan (The University of Sydney; with Mike Bell); 2023 – 2026
• Jack Wang (The University of Sydney; with David Levinson); 2022 – 2025
• Abebe Dress Beza (The University of Sydney; with David Levinson); 2022 – 2025
• Dr. Ang Ji (The University of Sydney; with David Levinson); 2018 – 2021
• Dr. Mohammad Noaeen (University of Calgary; with Behrouz Far); 2016 – 2021

 

– Network modeling (MFD) and traffic control

• Li, Y., & Ramezani, M. (2022). Quasi revenue-neutral congestion pricing in cities: Crediting drivers to avoid city centers. Transportation Research Part C, 145. (PDF) (Video abstract)
• Mohajerpoor, R., Cai, C., & Ramezani, M. (2022). Optimal traffic signal control of isolated oversaturated intersections using predicted demand. IEEE Transactions on Intelligent Transportation Systems. (PDF)
• Noaeen, M., Mohajerpoor, R., Far, B., & Ramezani, M. (2021). Real-time decentralized traffic signal control for congested urban networks considering queue spillbacks. Transportation Research Part C, 133. (PDF)
• Yildirimoglu, M., Ramezani, M., & Amirgholy, M. (2021). Staggered work schedules for congestion mitigation: A morning commute problem. Transportation Research Part C, 132. (PDF)
• Li, Y., Mohajerpoor, R., & Ramezani, M. (2021). Perimeter control with real-time location-varying cordon. Transportation Research Part B, 150, 101-120. (PDF) (Video abstract)
• Li, Y., Yildirimoglu, M., & Ramezani, M. (2021). Robust perimeter control with cordon queues and heterogeneous transfer flows. Transportation Research Part C, 126. (PDF) (Video abstract)
• Kumarage, S., Yildirimoglu, M., Ramezani, M., & Zheng, Z. (2021). Schedule-constrained demand management in two-region urban networks. Transportation Science, 55(4), 857-882. (PDF)
• Han, Y., Ramezani, M., Hegyi, A., Yuan, Y., & Hoogendoorn, S. (2020). Hierarchical ramp metering in freeways: An aggregated modeling and control approach. Transportation Research Part C, 110, 1-19. (PDF)
• Mohajerpoor, R., Saberi, M., Vu, H. L., Garoni, T. M., & Ramezani, M. (2020). H∞ robust perimeter flow control in urban networks with partial information feedback. Transportation Research Part B, 137, 47-73. (PDF)
• Yildirimoglu, M., & Ramezani, M. (2020). Demand management with limited cooperation among travellers: A doubly dynamic approach. Transportation Research Part B, 132, 267-284. (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, 119, 45-68. (PDF)
• Aalipour, A., Kebriaei, H., & Ramezani, M. (2019). Analytical Optimal Solution of Perimeter Traffic Flow Control Based on MFD Dynamics: A Pontryagin’s Maximum Principle Approach. IEEE Transactions on Intelligent Transportation Systems, 20(9), 3224-3234. (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 Dynamics, 5(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, 74, 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 C, 59, 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, 54, 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 Systems, 14(1), 348-359. (PDF)

 

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

• Yang, Y., & Ramezani, M. (2022). A learning method for real-time repositioning in e-hailing services. IEEE Transactions on Intelligent Transportation Systems. (PDF) (Video abstract)
• Jiao, G., & Ramezani, M. (2022). Incentivizing shared rides in e-hailing markets: Dynamic discounting. Transportation Research Part C, 144. (PDF) (Video abstract)
• 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) (Video abstract)
• 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 reports, 9(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

• Ji, A., Ramezani, M., & Levinson, D. (2023). Pricing lane changes. Transportation Research Part C, 149. (PDF)
• 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 Engineering, 30(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, 46(10), 1576-1590. (PDF)