Advanced Trip Planners for Transit Networks: Some Theoretical and Experimental Aspects of Pre-Trip Path Choice Modeling

  • Agostino Nuzzolo
  • Umberto Crisalli
  • Antonio Comi
  • Luca Rosati
Chapter
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 262)

Abstract

The chapter reports the first results of a research project for the definition of an advanced trip planner for transit networks. The project at the current stage has developed the module to support the user with personalized pre-trip information based on his/her preferences. The first part of the chapter describes the user needs and the logical architecture of the trip planner. The second part deals with the theoretical aspects of the path choice model used to support the path choice set individuation, the path utility calculation and the user preference learning procedure. In order to apply the theoretical framework and to show the benefits of the proposed approach, some experimental results of a test case on the transit system of the metropolitan area of Rome are presented.

Keywords

Transit Path choice models Schedule-based Personalized information Single-user Parameters estimation 

References

  1. 1.
    Caulfield B., O’Mahony M.: An examination of the Public Transport Information requirements of users. IEEE Trans. Intell. Transp. Syst. 8(1), 108–120 (2007)Google Scholar
  2. 2.
    Adbel-Aty M.A.: Using ordered probit modelling to study the effect of ATIS on transit ridership. Transp. Res. Part C 9, 265–277 (2001) ElsevierGoogle Scholar
  3. 3.
    Grotenhuis J.W., Wiegmans B.W., Rietveld P.: The desired quality of integrated multimodal travel information in public transport: Customer needs for time and effort savings. Transp. Policy 14(1), 27–38 (2007) ElsevierGoogle Scholar
  4. 4.
    Kenyon S., Lyons G.: The value of integrated multimodal traveller information and its potential contribution to modal change. Transp. Res. Part F 6, 1–21 (2003) ElsevierGoogle Scholar
  5. 5.
    Tang L., Thakuriah P.: Will the psychological effects of real-time transit information systems lead to ridership gain?. In: Proceedings of the Transportation Research Board Annual Meeting, Washington, USA (2011)Google Scholar
  6. 6.
    Zhang L., Li J., Zhou K., Gupta S.D., Li M., Zhang W.B., Miller M.A., Misener J.A.: Design and implementation of a traveller information tool with integrated real-time transit information and multi-modal trip planning. In: Proceedings of the Transportation Research Board Annual Meeting, Washington, USA (2011)Google Scholar
  7. 7.
    Arentze T.A.: Adaptive, personalized travel information systems: A Bayesian method to learn users’ personal preferences in multi-modal transport networks. In: Proceedings of the Transportation Research Board Annual Meeting, Washington, USA (2013)Google Scholar
  8. 8.
    Nuzzolo A., Crisalli U., Comi A., Rosati L.: An advanced pre-trip planner with personalized information on transit networks with ATIS. In: Proceedings of 16th International IEEE Conference on Intelligent Transport Systems, The Hague, The Netherlands (2013)Google Scholar
  9. 9.
    ARTIST: ARchitettura Telematica Italiana per il Sistema dei Trasporti Ministero delle Infrastrutture e dei trasporti, http://www.its-artist.rupa.it (2003)
  10. 10.
    Ben-Akiva, M., Lerman, S.: Discrete Choice Analysis. MIT Press, Cambridge (1985)Google Scholar
  11. 11.
    Nuzzolo A., Crisalli, U.: The schedule-based approach in dynamic transit modelling: a general overview. In: Wilson, N.H.M., Nuzzolo, A. (eds.) Schedule-Based Dynamic Transit Modeling. Theory and Applications, pp. 1–24. Kluwer, Dordrecht (2004)Google Scholar
  12. 12.
    Nuzzolo, A., Russo, F., Crisalli, U.: A doubly dynamic schedule-based assignment model for transit networks. Transp. Sci. 35, 268–285 (2001)CrossRefMATHGoogle Scholar
  13. 13.
    Nuzzolo A., Crisalli U., Rosati L.: A schedule-based assignment model with explicit capacity constraints for congested transit networks. Transp. Res. Part C 20(1), 16–33 (2012) Elsevier. doi: 10.1016/j.trc.2011.02.007
  14. 14.
    Chapman R.G.: An approach to estimating logit models of a single decision maker’s choice behavior. In: Kinnear T.C. (ed.) Advances in Consumer Research, vol. 11, pp. 656–661. Association for Consumer Research, Provo (1984)Google Scholar
  15. 15.
    Frischknecht B., Eckert C., Louviere J.: Simple ways to estimate choice models for single consumers. Centre for the Study of Choice (CenSoC), Working Paper Series, No. 11-006, University of Technology of Sydney (2011)Google Scholar
  16. 16.
    Hess S., Rose J.M.: Allowing for intra-respondent variations in coefficients estimated on repeated choice data, Transp. Res. Part B 43, 708–719 (2009) ElsevierGoogle Scholar
  17. 17.
    Hensher D.A., Greene W.H.: The Mixed Logit Model: The State of Practice, Transportation, vol. 30, pp. 133–176. Kluwer Academic Publishers, Boston (2003)Google Scholar
  18. 18.
    Molin E.J.E., Arentze, T.A.: Travelers’ preferences in multimodal networks: design and results of a comprehensive series of choice experiments. In: Proceedings of the Transportation Research Board Annual Meeting, Washington, USA (2013)Google Scholar
  19. 19.
    Lancsar E., Louviere J.: Estimating individual level discrete choice models and welfare measures using best worst choice experiments and sequential best worst MNL. Centre for the Study of Choice (CenSoC). University of Technology of Sydney, Sydney (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Agostino Nuzzolo
    • 1
  • Umberto Crisalli
    • 1
  • Antonio Comi
    • 1
  • Luca Rosati
    • 2
  1. 1.Department of Enterprise EngineeringTor Vergata University of RomeRomeItaly
  2. 2.Department of Civil Engineering and Computer Science EngineeringTor Vergata University of RomeRomeItaly

Personalised recommendations