Traffic sensor location approach for flow inference

Traffic sensor location approach for flow inference

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Traffic sensors serve an important function in obtaining traffic information. In this paper, a novel traffic sensor location approach is proposed to determine the maximum number of traffic flows by considering the time-spatial correlation. The problem is formulated as three 0–1 programming models to maximise the number of obtained flows under different cases. To solve these novel sensor location problems, an ant colony optimisation algorithm with a local search procedure is designed. Numerical experiments are conducted in both a simulated network and in the Sioux–Falls network. Results demonstrate the effectiveness and robustness of the proposed algorithm, which is believed to possess potential applicability in real surveillance network design.


    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
      • 13. Thomas, G.B.: ‘The relationship between detector location and travel characteristics on arterial streets’. Proc. Transportation Frontiers for the Next Millennium: 69th Annual Meeting of the Institute of Transportation Engineers, 1999.
    14. 14)
    15. 15)
    16. 16)
      • 16. Zhu, N., Liu, Y., Ma, S.F., He, Z.B.: ‘Mobile traffic sensor routing in dynamic transportation systems’, IEEE Trans. Intell. Transp. Syst., in press,
    17. 17)
      • 17. Wang, X., Juan, Z., Liu, M., Sun, Y.: ‘The application of nonparametric regressive algorithm for short-term traffic flow forecast’. ETCS'09: Proc. 2009 First Int. Workshop on Education Technology and Computer Science, IEEE, 2009, pp. 767770.
    18. 18)
      • 18. Zhang, T., Hu, L., Liu, Z., Zhang, Y.: ‘Nonparametric regression for the short-term traffic flow forecasting’. Proc. 2010 Int. Conf. Mechanic Automation and Control Engineering (MACE), IEEE, 2010, pp. 28502853.
    19. 19)
    20. 20)
    21. 21)
      • 21. Blum, C.: ‘Ant colony optimization’. Proc. 11th Annual Conf. Companion on Genetic and Evolutionary Computation Conf.: Late Breaking Papers, ACM, 2009, pp. 28252852.
    22. 22)
      • 22. Dorigo, M., Stützle, T.: ‘Ant colony optimization: overview and recent advances’, in Gendrea, M., Potvin, J.-Y., (Eds.): ‘Handbook of metaheuristics’ (Springer, 2010), pp. 227263.
    23. 23)
    24. 24)
    25. 25)
      • 25. Dorigo, M., Birattari, M.: ‘Ant colony optimization’, in Sammut, C., Geoffrey, I. (Eds.): ‘Encyclopedia of machine learning’ (Springer, USA, 2010), pp. 3639.
    26. 26)
      • 26. Birattari, M., Stützle, T., Paquete, L., et alA racing algorithm for configuring metaheuristics’. GECCO, 2002, vol. 2, pp. 1118.
    27. 27)
      • 27. Birattari, M.: ‘F-race for tuning metaheuristics’, in Birattari, M. (Ed.): ‘Tuning metaheuristics’ (Springer, Berlin, Heidelberg, 2009), pp. 85115.
    28. 28)

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