Design of Priority Transportation Corridor Under Uncertainty

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)

Abstract

Network design is one of the crucial activity in transportation engineering whose goal is to determine an optimal solution to traffic network layout with respect to given objectives and technical and/or economic constraints. In most of the practical problem the input data are not always precisely known as well as the information is not available regarding certain input parameters that are part of a mathematical model. Also constraints can be stated in approximate or ambiguous way. Thus, starting data and/or the problem constraints can be affected by uncertainty. Uncertain values can be represented using of fuzzy values/constraints and then handled in the framework of fuzzy optimization theory. In this paper we present a fuzzy linear programming method to solve the optimal signal timing problem on congested urban. The problem is formulated as a fixed point optimization subject to fuzzy constraints. The method has been applied to a test network for the case of priority corridors that are used for improve transit and emergency services. A deep sensitivity analysis of the signal setting parameters is then provided. The method is compared to classical linear programming approach with crisp constraints.

Keywords

Network design Uncertainty Fuzzy sets Signal settings optimization Emergency corridors Fuzzy programming Flexible constraints 

References

  1. 1.
    Cascetta, E.: Transportation Systems Analysis: Models and Applications. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Meng, Q., Yang, H.: Benefit distribution and equity in road network design. Transp. Res. Part B 36, 19–35 (2002)CrossRefGoogle Scholar
  4. 4.
    Cantarella, G.E., Vitetta, A.: The multi-criteria road network design problem in an urban area. Transportation 33, 567–588 (2006)CrossRefGoogle Scholar
  5. 5.
    Paksoy, T., Özceylan, E., Weber, G.W.: A multi-objective mixed integer programming model for multi echelon supply chain network design and optimization. Syst. Res. Inf. Technol. 4, 47–57 (2010)Google Scholar
  6. 6.
    Zimmermann, H.J.: Fuzzy Set Theory and Its Applications. Kluwer Academic Publishers, Dordrecht/London (1996)CrossRefMATHGoogle Scholar
  7. 7.
    Luo, X., Lee, J.H., Leung, H., Jennings, N.R.: Prioritised fuzzy constraint satisfaction problems: axioms, instantiation and validation. Int. J. Fuzzy Sets Syst. 136(2), 155–188 (2003)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Teodorovic, D., Vukadinovic, K.: Traffic Control and Transport Planning: A Fuzzy Sets and Neural Networks Approach. Kluwer Academic Publishers, Boston (1998)Google Scholar
  9. 9.
    Das, S.K., Goswami, A., Alam, S.S.: Multiobjective transportation problem with interval cost, source and destination parameters. Eur. Jour. of Oper. Res. 117, 110–112 (1999)Google Scholar
  10. 10.
    Mudchanatongsuk, S., Ordóñez, F., Liu, J.: Robust solutions for network design under transportation cost and demand uncertainty. J. Oper. Res. Soc. 59, 652–662 (2008)CrossRefMATHGoogle Scholar
  11. 11.
    Selim, H., Ozkarahan, I.: A supply chain distribution network design model: an interactive fuzzy goal programming-based solution approach. Int. J. Adv. Manuf. Technol. 36(3), 401–418 (2008)CrossRefGoogle Scholar
  12. 12.
    Ghatee, M., Hashemi, S.M.: Application of fuzzy minimum cost flow problems to network design under uncertainty. Fuzzy sets syst. 160, 3263–3289 (2009)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Kikuchi, S., Kronprasert, N.: Constructing transit origin-destination tables from fragmented data. Transp. Res. Rec. 2196, 34–44 (2010)CrossRefGoogle Scholar
  14. 14.
    Caggiani, L., Ottomanelli, M., Sassanelli, D.: A fixed point approach to origin-destination matrices estimation using uncertain data and fuzzy programming on congested networks. Transport. Res. Part C 28, 130–141 (2013)Google Scholar
  15. 15.
    Caggiani, L., Ottomanelli, M.: Traffic equilibrium network design problem under uncertain constraints. Procedia: Soc. Behav. Sci. 20, 372–380 (2011)CrossRefGoogle Scholar
  16. 16.
    Marcianò, F.A., Musolino, G., Vitetta, A.: Signal setting design on a road network: application of a system of models in evacuation conditions. In: Brebbia C.A. (ed.) Proceedings of Risk Analysis VII & Brownfields V, pp. 443–454. WIT Press, Southampton (2010)Google Scholar
  17. 17.
    Schittkowski, K.: NLQPL: A FORTRAN-subroutine solving constrained nonlinear programming problems. Ann. Oper. Res. 5, 485–500 (1985)MathSciNetGoogle Scholar
  18. 18.
    Bonnans, J.F., Gilbert, J.C., Lemarechal, C., Sagastizábal, C.A.: Numerical Optimization: Theoretical and Practical Aspects. Springer, Heidelberg (2006)Google Scholar
  19. 19.
    Chen, A., Kim, J., Lee, S., Choi, J.: Models and algorithm for stochastic network designs. Tsinghua Sci. Technol. 14(3), 341–351 (2009)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Meng, Q., Lee, D.H., Yang, H., Huang, H.J.: Transportation network optimization problems with stochastic user equilibrium constraints. Transp. Res. Rec. 1882, 113–119 (2004)CrossRefGoogle Scholar
  21. 21.
    Cantarella, G.E.: A general fixed-point approach to multimode multi-user equilibrium assignment with elastic demand. Transp. Sci. 31(2), 107–128 (1997)CrossRefMATHGoogle Scholar
  22. 22.
    Yang, H., Meng, Q., Bell, M.G.H.: Simultaneous estimation of the origin-destination matrices and travel-cost coefficient for congested networks in a stochastic user equilibrium. Transp. Sci. 35, 107–123 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Politecnico di Bari—D.I.C.A.T.E.ChBariItaly

Personalised recommendations