Specification and Aggregate Calibration of a Quantum Route Choice Model from Traffic Counts

  • Massimo Di GangiEmail author
  • Antonino Vitetta
Part of the AIRO Springer Series book series (AIROSS, volume 1)


This paper analyses certain aspects related to the route choice model in transport systems. The effects of an interference term have been taken into consideration in addition to the effect of a traditional covariance term. Both the specification and calibration of an interference term in a quantum route choice model are shown in the context of an assignment model. An application to a real system is reported where the calibration of QUMs (Quantum Utility Models) was performed using traffic counts. Results are compared with traditional and consolidated models belonging to the Logit family. Based on the theoretical and numeric results, it is highlighted how the interference term and quantum model can consider other aspects (such as information) with respect to traditional RUMs (Random Utility Models).


Assignment Path choice Quantum 



Authors wish to thank the Municipality of Benevento for having made available the data of the urban traffic plan during the national research project PRIN 2009.


  1. 1.
    Ben-Akiva, M.E., Lerman, S.R.: Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Cambridge, MA (1985)Google Scholar
  2. 2.
    De Maio, M.L., Vitetta, A.: Route choice on road transport system: a fuzzy approach. J. Intell. Fuzzy Syst. 28(5), 2015–2027 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Ben-Akiva, M.E., Bierlaire, M.: Discrete choice methods and their applications to short term travel decisions. In: Hall, R.W. (ed.) Handbook of Transportation Science, pp. 5–34. Kluwer, Dordrecht, The Netherlands (1999)CrossRefGoogle Scholar
  4. 4.
    Cascetta, E., Nuzzolo, A., Russo, F., Vitetta, A.: A modified logit route choice model overcoming path overlapping problems: specification and some calibration results for interurban networks. In: Lesort, J.B. (ed.) Proceedings of the Thirteenth International Symposium on Transportation and Traffic Theory, pp. 697–711. Pergamon, Lyon, France (1996)Google Scholar
  5. 5.
    Ben-Akiva, M.E.: Structure of passenger travel demand models. Transp. Res. Rec. 526 (1974)Google Scholar
  6. 6.
    Sheffi, Y., Powell, W.B.: An algorithm for the equilibrium assignment problem with random link times. Networks 12, 191–207 (1982)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Cantarella, G.E., Binetti, M.G.: Stochastic assignment with Gammit path choice models. In: Patriksson, M. (ed.) Transportation Planning, pp. 53–67. Kluwer Academic Publisher (2002)Google Scholar
  8. 8.
    Cascetta, E., Cantarella, G.E.: A day-to-day and within-day dynamic stochastic assignment model. Transp. Res. Part A 25(5), 227–291 (1991)CrossRefGoogle Scholar
  9. 9.
    Watling, D., Hazelton, M.L.: The dynamics and equilibria of day-to-day assignment models. Netw. Spat. Econ. 3(3), 349–370 (2003)CrossRefGoogle Scholar
  10. 10.
    Busemeyer, J.R., Bruza, P.D.: Quantum Models of Cognition and Decision. Cambridge University Press (2012)Google Scholar
  11. 11.
    Vitetta, A.: A quantum utility model for route choice in transport systems. Travel Behav. Soc. 3, 29–37 (2016). Scholar
  12. 12.
    Cantarella, G.E.: A general fixed-point approach to multi-mode multi-user equilibrium assignment with elastic demand. Transp. Sci. 31, 107–128 (1997)CrossRefGoogle Scholar
  13. 13.
    Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22(3), 400–407 (1951)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Daganzo, C.: Stochastic network equilibrium with multiple vehicle types and asymmetric, indefinite arc cost jacobians. Transp. Sci. 17, 282–300 (1983)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Cascetta, E., Russo, F., Viola, F.A., Vitetta, A.: A model of route perception in urban road networks. Transp. Res. Part B 36(7), 577–592 (2002)CrossRefGoogle Scholar
  16. 16.
    Prato, C.G.: Route choice modeling: past, present and future research directions. J. Choice Model. 2(1), 65–100 (2009)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Daganzo, C., Sheffi, Y.: On stochastic models of traffic assignment. Transp. Sci. 11, 253–274 (1977)CrossRefGoogle Scholar
  18. 18.
    De La Barra, T., Perez, B., Anez, J.: Multidimensional path search and assignment. In: 21st PTRC Summer Annual Meeting, pp. 307–320 (1993)Google Scholar
  19. 19.
    Jiménez, A.B., Lazaro, J.L., Dorronsoro, J.R.: Finding optimal model parameters by discrete grid search. In: Corchado, E., et al. (eds.) Innovations in Hybrid Intelligent Systems, ASC, vol. 44, pp. 120–127. Springer, Berlin, Heidelberg (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Dipartimento di IngegneriaUniversità degli Studi di MessinaMessinaItaly
  2. 2.DIIESUniversità degli Studi Mediterranea di Reggio CalabriaReggio CalabriaItaly

Personalised recommendations