Your browser does not support JavaScript!

access icon free Centralised versus decentralised signal control of large-scale urban road networks in real time: a simulation study

Recently, signal control strategies with decentralised logic have been developed to tackle the traffic congestion problems of urban road networks. Such strategies aim at network-wide traffic flow efficiency improvement via local actions, thus low design effort and infrastructure investment. This study presents, compares, and evaluates two such innovative approaches: the job scheduling algorithm comprising the local control component of the scalable urban traffic control (SURTRAC) system and the max- or back-pressure algorithm. The approaches are also compared against traffic-responsive urban control (TUC), a well-established strategy with centralised logic. Evaluation is based on the AIMSUN simulation model of the city centre of Chania, Greece. The study results indicate that the TUC and max-pressure retain performance independently of the prevailing traffic conditions, while also being computationally simpler than job scheduling. Both decentralised approaches require frequent (high-resolution) and relatively accurate measurements; on the other hand, TUC, although less demanding in this respect, calls for communication lines between the junction controllers and the central computer. Finally, compared with both decentralised approaches, the TUC provides a signal plan sequence with less excessive differences between each other, thus fewer disturbances to the common network users. Nevertheless, for more comprehensive conclusions, more investigations, including field trials, would be needed.


    1. 1)
      • 12. Smaragdis, E., Dinopoulou, V., Aboudolas, K., et al: ‘Application of the extended traffic signal control strategy TUC to the Southampton urban road network’. Preprints of the 10th IFAC Symp. on Control in Transportation Systems, Tokyo, Japan, 4–6 August 2003, pp. 2328.
    2. 2)
      • 5. Gregoire, J., Frazzoli, E., de La Fortelle, A., et al: ‘Back-pressure traffic signal control with unknown routing rates’. Preprints of the 19th World Congress, The International Federation of Automatic Control, Cape Town, South Africa, 24–29 August 2014, pp. 1133211337.
    3. 3)
      • 11. Kouvelas, A., Lioris, J., Fayazi, S., et al: ‘Max-pressure controller for stabilizing the queues in signalized arterial networks’, Transp. Res. Rec., 2014, 2421, pp. 133141.
    4. 4)
      • 6. Diakaki, C., Aboudolas, K., Papageorgiou, M.: ‘A multivariable regulator approach to traffic-responsive network-wide signal control’, J. Control Eng. Pract., 2002, 10, pp. 183195.
    5. 5)
      • 7. Diakaki, C., Dinopoulou, V., Aboudolas, K., et al: ‘Extensions and new applications of the traffic-responsive urban control strategy: coordinated signal control for urban networks’, Transp. Res. Rec., 2003, 1856, pp. 202211.
    6. 6)
      • 13. Kouvelas, A., Aboudolas, K., Kosmatopoulos, E.B., et al: ‘Adaptive performance optimization for large-scale traffic control systems’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (4), pp. 14341445.
    7. 7)
      • 14. Manolis, D., Diakaki, C., Papamichail, I., et al: ‘Simulation investigations of the coordinated traffic-responsive signal control strategy TUC with actuation at the local junction level’. Proc. 8th Int. Congress on Transportation Research in Greece: Transportation on 2030: Trends and Perspectives, Athens, Greece, 27–29 September 2017, paper no. 70.
    8. 8)
      • 15. Xie, X.-F., Smith, S.F., Barlow, G.J.: ‘Schedule-driven coordination for real-time traffic network control’. Proc. 22nd Int. Conf. on Automated Planning and Scheduling, Atibaia, Sao Paulo, Brazil, 25–29 June 2012, pp. 323331.
    9. 9)
      • 16. Zhang, L., Garoni, T.M., de Gier, J.: ‘A comparative study of macroscopic fundamental diagrams of arterial road networks governed by adaptive traffic signal systems’, Transp. Res. B, Methodol., 2013, 49, pp. 123.
    10. 10)
      • 10. Tassiulas, L., Ephremides, A.: ‘Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks’, IEEE Trans. Autom. Control, 1992, 37, (12), pp. 19361948.
    11. 11)
      • 3. Smith, S.F., Barlow, G.J., Xie, X.-F., et al: ‘Smart urban signal networks: initial application of the SURTRAC adaptive traffic signal control system’. Proc. 23rd Int. Conf. on Automated Planning and Scheduling, Rome, Italy, 10–14 June 2013, pp. 434442.
    12. 12)
      • 2. Xie, X.-F., Smith, S.F., Lu, L., et al: ‘Schedule-driven intersection control’, Transp. Res. C, Emerg. Technol., 2012, 24, pp. 168189.
    13. 13)
      • 9. Xie, X.-F., Smith, S.F., Lu, L., et al: ‘Coping with real-world challenges in real-time urban traffic control’. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington, D.C., USA, 12–16 January 2014, paper no. 14-2103.
    14. 14)
      • 8. Kouvelas, A., Aboudolas, K., Papageorgiou, M., et al: ‘A hybrid strategy for real-time traffic signal control of urban road networks’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (3), pp. 884894.
    15. 15)
      • 4. Varaiya, P.: ‘Max pressure control of a network of signalized intersections’, Transp. Res. C, Emerg. Technol., 2013, 36, pp. 177195.
    16. 16)
      • 1. Papageorgiou, M., Diakaki, C., Dinopoulou, V., et al: ‘Review of road traffic control strategies’, Proc. IEEE, 2003, 91, (12), pp. 20432067.

Related content

This is a required field
Please enter a valid email address