Random Utility Theory

  • Ennio Cascetta
Part of the Applied Optimization book series (APOP, volume 49)


In Chapter 1 it was stated that transport demand flows result from the aggregation of individual trips. Each trip is the result of several choices made by the users: travelers in passenger transportation or operators (manufacturers, shippers, carriers) in goods transport. Some traveler choices are made infrequently, such as where to reside and work and whether to own a vehicle or not. Other choices are made for each trip, these include whether to make a trip for a certain purpose at what time to what destination, with what mode, using what route. Each choice context, defined by available alternatives, evaluation factors and decision procedures, is usually known as a “choice dimension”. Also, in most cases, choices concerning transport demand are made among a finite number of discrete alternatives.


Intermediate Node Generalize Extreme Value Choice Probability Systematic Utility Multinomial Logit Model 


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Reference Notes

  1. [109]
    Domencich T. A., and D. McFadden (1975). Urban travel demand: a behavioural analysis. American Elsevier, New York.Google Scholar
  2. [258]
    Williams H.C.W.L. (1977). On the formation of travel demand models and economic evaluation measures of user benefit Environment and Planning A: 285–344.Google Scholar
  3. [179]
    Manski C. (1977). The structure of random utility models Theory and Decision 8: 229–254.Google Scholar
  4. [180]
    Manski C.F., and D. McFadden (1981). Alternative estimators and sample designs for discrete choice analysis. in Structural Analysis of discrete data with Econometric Applications, MIT Press, Cambridge, Mass.Google Scholar
  5. [19]
    Ben-Akiva M., and S. Lerman (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Cambridge, Mass.Google Scholar
  6. [259]
    Williams H.C.W.L., and J. de D. Ortùzar (1982). Behavioural theories of dispersion and the mis-specification of travel demand models. Transportation Research 16B: 167–219.Google Scholar
  7. [144]
    Horowitz J.L. (1985). Travel and location behaviour: state of the art and research opportunities Transportation Research 19A: 441–454.Google Scholar
  8. [11]
    Bath C. (1997). Recent Methodological Advances Relevant to Activity and Travel Behavior Analysis. Proceedings of the V III IATBR conference, Resource papers, Austin, Texas.Google Scholar
  9. [102]
    Daly A.J., and S. Zachary (1978), Improved multiple choice models. in D.A. Hensher and M.Q. Dalvi (eds.), Determinants of Travel Choice. Saxon House, Westmead.Google Scholar
  10. [99]
    Daganzo C.F., and M. Kusnic (1992). Another look at the nested logit model. Technical Report UCB-ITS-RTR 92–2, Institute of Transportation Studies, University of California, Berkeley.Google Scholar
  11. [186]
    McFadden, D. (1978). Modeling the choice of residential location. in A.K.(ed.), Spatial interaction theory and residential location: 75–96, North-Holland, Amsterdam.Google Scholar
  12. [238]
    Small, and K. (1987). A discrete choice model for ordered alternatives Econometrica 55: 409–424.Google Scholar
  13. [247]
    Vovsha P. (1997). The Cross-Nested Logit Model: Application to Mode Choice in the Tel-Aviv Metropolitan Area Proceedings of the 76`h TRB Meeting.Google Scholar
  14. [248]
    Vovsha P. S. Bekor (1998). The link-Nested Logit Model of Route Choice: Overcoming the Route Overlapping Problem Proceedings of the 77th TRB Meeting.Google Scholar
  15. [69]
    Cascetta E., and A. Papola (2000). Implicit availability/perception models for the simulation of travel demand Transportation Research C, forthcoming.Google Scholar
  16. [17]
    Ben-Akiva M., and B. Francois (1983). p Homogeneous Generalized Extreme Value Model. Working paper, Department of Civil Engineering, MIT Cambridge, Mass.Google Scholar
  17. [218]
    Papola A. (1996). I modelli di Valore Estremo Generalizzato (GEV) per la simulazione della domanda di trasporto. Internal report. Department of Transportation Engineering, University of Naples “Federico II”.Google Scholar
  18. [97]
    Daganzo C.F. (1979). Multinomial probit: the theory and its application to demand forecasting. Academic press, New York.MATHGoogle Scholar
  19. [95]
    Dafermos S.C. (1982). The general multimodal network equilibrium problem with elastic demand Networks 12: 57–72.Google Scholar
  20. [161]
    Langdon M.G. (1984). Improved algorithms for estimating choice probabilities in the multinomial probit model Transportation Science 18: 267–299.Google Scholar
  21. [26]
    Ben-Akiva, M., and M. Bierlaire (1999). Discrete choice methods and their application to short term travel decisions. Handbook of Transportation Science, R.W. Hall ed., Kluwer Academic Publishers: 5–33.Google Scholar
  22. [216]
    Ortuzar J.de D., and L.G. Willumsen (1994). Modelling Transport John Wiley and Sons, 2nd edition.Google Scholar
  23. [18]
    Ben-Akiva M., and D. Bolduc (1996). Multinomial Probit with a Logit Kernel and a General Parametric Specification of the Covariance Structure. Working Paper, Department of Economics, MIT, Boston, Mass.Google Scholar
  24. [35]
    Bolduc, D., B. Fortin, and M.A. Fournier (1996). “The Impact of Incentive Policies to Influence Practice Location of General Practitioners: A Multinomial Probit Analysis”, Journal of Labor Economics 14: 703–732.Google Scholar
  25. [16]
    Ben-Akiva M., and B. Boccara (1995). Discrete choice models with latent choice sets. International Journal of Research in Marketing 12: 9–24.Google Scholar
  26. [68]
    Cascetta E., and A. Papota (2000). A joint mode-run choice model to simulate the schedule influence at a regional level. Proceedings of the 9th IATBR conference, Sidney, Australia.Google Scholar
  27. [97]
    Daganzo C.F. (1979). Multinomial probit: the theory and its application to demand forecasting. Academic press, New York.MATHGoogle Scholar
  28. [49]
    Cantarella G.E. (1997). A General Fixed-Point Approach to Multi-Mode Multi-User Equilibrium Assignment with Elastic Demand Transportation Science 31: 107–128.Google Scholar
  29. [159]
    Koppelman F.S. (1989). Multidimensional Model System for Intercity Travel Choice Behaviour Transportation Research Records 1241: 1–8.Google Scholar
  30. [20]
    Ben-Akiva M., and T. Atherton (1977). Methodology for Short-Range Travel Demand Predictions Journal of Transport Economics and Policy 11: 224–261.Google Scholar
  31. [252]
    Watanatada T., and M. Ben-Akiva (1979). Forecasting urban travel demand for quick policy analysis with disaggregate choice model: a Monte-Carlo simulation approach. Transportation Research 13A: 241–248.Google Scholar
  32. [130]
    Gunn H., J. and J.J. Bates (1982). Statistical aspects of travel demand modelling. Transportation Research 16A: 371–382.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Ennio Cascetta
    • 1
  1. 1.Dipartimento di Ingegneria dei Trasporti “Luigi Tocchetti”Università degli Studi di Napoli “Federico II”NapoliItaly

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