Iterative tuning strategy for setting phase splits with anticipation of traffic demand in urban traffic network

Iterative tuning strategy for setting phase splits with anticipation of traffic demand in urban traffic network

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Facing large-scale urban traffic network, countless effort has been made toward intelligent and efficient urban traffic control system to better use existing traffic infrastructures. Recently, a novelty pre-timed traffic signal control strategy known as iterative tuning (IT) has been developed by exploiting repetitive characteristic of junction's vehicle throughput on working days, which is sufficiently efficient in under-saturated traffic conditions. This study further improves IT strategy in saturated traffic conditions with consideration of traffic demand including vehicle throughput and residual queued vehicles. Unlike conventional pre-timed strategies, preparation of signal schedules is not required in IT strategy and fine-tuning process executes iteratively and automatically. This study proposes a generalised traffic model to describe urban network dynamics and explicit split tuning algorithm. Without specific control trajectories, rigorous analysis generates sufficient condition for guaranteeing the convergence of IT strategy globally over repetitions. The robustness of IT controller to variations of traffic flow patterns and errors of initial conditions is also analysed in details. Commonwealth Avenue, an area with heavy traffic in Singapore, is demonstrated in simulations and simulation results indicate the effectiveness and robustness of IT strategy.


    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • 5. Festin, S.M.: ‘Summary of national and regional travel trends 1970–1995’, Tech. Rep.1996.
    6. 6)
      • 6. Roess, R.P., Prassas, E.S., McShane, W.R.: ‘Traffic engineering’ (Prentice-Hall, 2004, 3rd edn.).
    7. 7)
      • 7. Wang, Y., Wang, D., Yang, C., et al: ‘Phase-based repetitiveness and pattern classification of urban traffic flow data’, 14th ITS Asia Pacific Forum, 2015.
    8. 8)
      • 8. Webster, F.V.: ‘Traffic signal settings’, Department of Scientific and Industrial Research Road Research Laboratory, Tech. Rep., 1958.
    9. 9)
      • 9. Hauser, T., Scherer, W.T.: ‘Data mining tools for real-time traffic signal decision support & maintenance’, IEEE Int. Conf. on Systems, Man, and Cybernetics, 2001, vol. 3, 2001, pp. 14711477.
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
      • 19. Huang, W., Viti, F., Tampere, C.M.: ‘An iterative learning approach for signal control in urban traffic networks’, Intelligent Transportation Systems Conf. (ITSC), 2013.
    20. 20)
      • 20. Wang, Y., Wang, D., Xiao, N., et al: ‘Iterative tuning strategy for setting phase splits in traffic signal control’, IEEE 17th Int. Conf. on Intelligent Transportation Systems (ITSC), 2014, 2014, pp. 24532458.
    21. 21)
      • 21. Oliver, P.J., Shakiban, C.: ‘Applied linear algebra’ (Prentice-Hall, Upper Saddle River, NJ, 2006).
    22. 22)
      • 22. Bert, E.: ‘Dynamic urban origin-destination matrix estimation methodology’, Tech. Rep., 2009.
    23. 23)
      • 23. Harville, D.A.: ‘Matrix algebra from a statistician's perspective’ (Springer, 2008).

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