Advertisement

Stochastic Traffic-Assignment with Multi-modes Based on Bounded Rationality

  • Zhi ZuoEmail author
  • Xiaofeng Pan
  • Lixiao Wang
  • Tao Feng
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 127)

Abstract

This paper proposes a stochastic traffic assignment model with multi-modes incorporating the concept of bounded rationality. Multi-criteria decision is considered using TODIM (which stands for “multi-criteria, interactive decision making” in Portuguese) method to generate variable demands, route uncertainty is taken into account based on cumulative prospect theory to measure route choice behavior. A numerical example is used to verify the validity of the new model. The sensitivity of the scaling parameters for the mode and route choice is also analyzed. Results confirmed the model’s applicability and showed that travelers’ preferences on different routes are reference dependent. Two scaling parameters have a significant influence on the final results and must be estimated very carefully from real data.

Keywords

Travel behavior Bounded rationality Variable demands Cumulative prospect theory TODIM method 

Notes

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Number 71861032), National Natural Science Foundation of Xinjiang (Number 2018D01C071) and the doctoral research fund of Xinjiang University.

Ethical Approval.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent.

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Ben-Akiva, M.E., Lerman, S.R.: Discrete Choice Analysis: Theory and Application to Travel Demand, vol. 9. MIT Press, Cambridge (1985)Google Scholar
  2. 2.
    Yai, T., Iwakura, S., Morichi, S.: Multinomial probit with structured covariance for route choice behavior. Transp. Res. B Methodol. 31, 195–207 (1997)CrossRefGoogle Scholar
  3. 3.
    Vovsha, P., Bekhor, S.: Link-nested logit model of route choice: overcoming route overlapping problem. Transp. Res. Rec. 1645, 133–142 (1998)CrossRefGoogle Scholar
  4. 4.
    Bhat, C.R.: Analysis of travel mode and departure time choice for urban shopping trips. Transp. Res B Methodol. 32, 361–371 (1998)CrossRefGoogle Scholar
  5. 5.
    Simon, H.A.: A behavioral model of rational choice. Q. J. Econ. 69, 99–118 (1955)CrossRefGoogle Scholar
  6. 6.
    Nakayama, S., Kitamura, R., Fujii, S.: Drivers’ learning and network behavior: dynamic analysis of the driver-network system as a complex system. Transp. Res. Rec. 1676, 30–36 (1999)CrossRefGoogle Scholar
  7. 7.
    Nakayama, S., Kitamura, R., Fujii, S.: Drivers’ route choice rules and network behavior: do drivers become rational and homogeneous through learning? Transp. Res. Rec. 1752, 62–68 (2001)CrossRefGoogle Scholar
  8. 8.
    Gifford, J.L., Checherita, C.: Bounded rationality and transportation behavior: lessons for public policy. In: TRB 86th Annual Meeting Compendium of Papers CD-Rom (No. 07-2451), Washington, DC (2007)Google Scholar
  9. 9.
    Allais, M.: Le comportement de L’Homme rationnel devant le risque: critique des postulats et axiomes de l’école américaine. Econometrica J. Econometric Soc. 20, 503–546 (1953)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Ellsberg, D.: Risk, ambiguity, and the savage axioms. Q. J. Econ. 75, 643–669 (1961)CrossRefGoogle Scholar
  11. 11.
    Simon, H.A.: Bounded Rationality. Utility and Probability. Palgrave Macmillan, London (1987)Google Scholar
  12. 12.
    Gigerenzer, G., Goldstein, D.G.: Reasoning the fast and frugal way: models of bounded rationality. Psychol. Rev. 103, 650–669 (1996)CrossRefGoogle Scholar
  13. 13.
    Conlisk, J.: Why bounded rationality? J. Econ. Lit. 34, 669–700 (1996)Google Scholar
  14. 14.
    Kahneman, D.: A perspective on judgment and choice: mapping bounded rationality. Am. Psychol. 58, 697–720 (2003)CrossRefGoogle Scholar
  15. 15.
    Tang, T., Huang, H., Shang, H.: Influences of the driver’s bounded rationality on micro driving behavior, fuel consumption and emissions. Transp. Res. D 41, 423–432 (2015)CrossRefGoogle Scholar
  16. 16.
    Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica 47, 263–291 (1979)CrossRefGoogle Scholar
  17. 17.
    Tversky, A., Kahneman, D.: Advances in prospect theory: cumulative representation of uncertainty. J. Risk Uncertainty 5, 297–323 (1992)CrossRefGoogle Scholar
  18. 18.
    Rieger, M.O., Wang, M.: Cumulative prospect theory and the St. Petersburg paradox. Econ. Theory 28, 665–679 (2006)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Dhami, S., Al-Nowaihi, A.: Why do people pay taxes? Prospect theory versus expected utility theory. J. Econ. Behav. Organ. 64, 171–192 (2007)CrossRefGoogle Scholar
  20. 20.
    Pan, X.F., Zuo, Z.: A stochastic user equilibrium model and optimal congestion pricing with prospect theory. Procedia Soc. Behav. Sci. 138, 127–136 (2014)CrossRefGoogle Scholar
  21. 21.
    Senbil, M., Kitamura, R.: Reference points in commuter departure time choice: a prospect theoretic test of alternative decision frames. J. Intell. Transp. Syst. 8, 19–31 (2004)CrossRefGoogle Scholar
  22. 22.
    Jou, R.C., Kitamura, R., Weng, M.C., Chen, C.C.: Dynamic commuter departure time choice under uncertainty. Transp. Res. A Policy Pract. 42, 774–783 (2008)CrossRefGoogle Scholar
  23. 23.
    Liu, D., Lam, W.H.: Modeling the effects of population density on prospect theory-based travel mode-choice equilibrium. J. Intell. Transp. Syst. 18, 379–392 (2014)CrossRefGoogle Scholar
  24. 24.
    Connors, R.D., Sumalee, A.: A network equilibrium model with travellers’ perception of stochastic travel times. Transp. Res. B 43, 614–624 (2009)CrossRefGoogle Scholar
  25. 25.
    Xu, H., Lou, Y., Yin, Y., Zhou, J.: A prospect-based user equilibrium model with endogenous reference points and its application in congestion pricing. Transp. Res. B Methodol. 45, 311–328 (2011)CrossRefGoogle Scholar
  26. 26.
    Avineri, E., Bovy, P.: Identification of parameters for a prospect theory model for travel choice analysis. Transp. Res. Rec. 2082, 141–147 (2008)CrossRefGoogle Scholar
  27. 27.
    Jou, R.C., Chen, K.H.: An application of cumulative prospect theory to freeway drivers’ route choice behaviours. Transp. Res. A Policy Pract. 49, 123–131 (2013)CrossRefGoogle Scholar
  28. 28.
    Nwogugu, M.: Towards multi-factor models of decision making and risk: a critique of prospect theory and related approaches, Part I. J. Risk Finance 6, 150–162 (2005)CrossRefGoogle Scholar
  29. 29.
    Nwogugu, M.: Towards multi-factor models of decision making and risk: A critique of prospect theory and related approaches, Part II. J. Risk Finance 6, 163–173 (2005)CrossRefGoogle Scholar
  30. 30.
    Nwogugu, M.: Towards multi-factor models of decision making and risk: a critique of prospect theory and related approaches, Part III. J. Risk Finance 6, 267–274 (2005)CrossRefGoogle Scholar
  31. 31.
    Timmermans, H.: On the (ir)relevance of prospect theory in modelling uncertainty in travel decisions. EJTIR 4, 368–384 (2010)Google Scholar
  32. 32.
    Gomes, L., Lima, M.: TODIM: basics and application to multicriteria ranking of projects with environmental impacts. Found. Comput. Decis. Sci. 16, 113–127 (1992)zbMATHGoogle Scholar
  33. 33.
    Gomes, L., Lima, M., Maranhão, F.: Multicriteria analysis of natural gas destination in Brazil: an application of the TODIM method. Math. Comput. Modell. 50, 92–100 (2009)CrossRefGoogle Scholar
  34. 34.
    Tseng, M.L., Lin, Y.H., Tan, K., Chen, R.H., Chen, Y.H.: Using TODIM to evaluate green supply chain practices under uncertainty. Appl. Math. Modell. 38, 2983–2995 (2014)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Giulio, E.C., Armando, C., Stefanode, L.: Stochastic equilibrium assignment with variable demand: theoretical and implementation issues. Eur. J. Oper. Res. 241, 330–347 (2015)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Tversky, A., Kahneman, D.: Loss aversion in riskless choice: a reference-dependent model. Q. J. Econ. 106, 1039–1061 (1991)CrossRefGoogle Scholar
  37. 37.
    Prelec, D.: The probability weighting function. Econometrica 66, 497–527 (1998)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Ingersoll, J.: Non-monotonicity of the Tversky-Kahneman probability-weighting function: a cautionary Note. Eur. Finan. Manag. 14, 385–390 (2008)CrossRefGoogle Scholar
  39. 39.
    Zuo, Z., Pan, X.F., Liu, K.: Parameter calibration in cumulative prospect theory for travelers’ route choice behavior. In: 15th COTA International Conference of Transportation Professionals (CICTP 2015), pp. 2696–2708. American Society of Civil Engineers, Reston (2015)Google Scholar
  40. 40.
    Gomes, L.: An application of the TODIM method to the multicriteria rental evaluation of residential properties. Eur. J. Oper. Res. 193, 204–211 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Civil and Architectural EngineeringXinjiang UniversityUrumqiChina
  2. 2.Urban Planning Group, Department of the Built EnvironmentEindhoven University of TechnologyEindhovenNetherlands

Personalised recommendations