Estimation of Travel Demand Flows

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


Analysis and design of transportation systems require, respectively, the estimation of present demand and the forecasting of (hypothetical) future demand. These can be obtained by using different sources of information and statistical procedures.


Demand Model Fractional Factorial Design Generalize Little Square Multinomial Logit Model Link Cost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Reference Notes

  1. [229]
    Road Research Laboratory (RRL) (1965). Research on road traffic. RRL, London.Google Scholar
  2. [111]
    Environmental Protection Agency (EPA) (1996). Travel Survey Manual. Department of Transportation, Washington D.C.Google Scholar
  3. [89]
    Cochran W.G. (1963). Sampling techniques. John Wiley, New York.Google Scholar
  4. [270]
    Yates F. (1981). Sampling methods for censuses and surveys. Griffin, London.Google Scholar
  5. [239]
    Smith M.J. (1979). The Existence, Uniqueness and Stability of Traffic Equilibrium Transportation Research 13B: 295–304.Google Scholar
  6. [45]
    Brog W., Ampt E., (1982). State of the art in the collection of travel behaviour data. in Travel Behaviour for the 1980’s, Special Report 201, National Research Council, Washington, DC.Google Scholar
  7. [216]
    Ortuzar D., and L.G. Willumsen (1994). Modelling Transport John Wiley and Sons, 2nd edition.Google Scholar
  8. [109]
    Domencich T. A., and D. McFadden (1975). Urban travel demand: a behavioural analysis. American Elsevier, New York.Google Scholar
  9. [140]
    Horowitz J.L. (1981). Identification and diagnosis of specification errors in the multinominal logit model Transportation Research 15B: 345–360.Google Scholar
  10. [141]
    Horowitz J.L. (1982). Air quality analysis for urban transportation planning. MIT Press, Cambridge, Mass.Google Scholar
  11. [179]
    Manski C. (1977). The structure of random utility models Theory and Decision 8: 229–254.Google Scholar
  12. [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
  13. [130]
    Gunn H., J. and J.J. Bates (1982). Statistical aspects of travel demand modelling. Transportation Research 16A: 371–382.Google Scholar
  14. [181]
    Manski C.F., and Lerman S.R. (1977). The estimation of probabilities from choice-based samples Econometrica 45.Google Scholar
  15. [19]
    Ben Akiva M., and S. Lerman (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Cambridge, Mass.Google Scholar
  16. [136]
    Hensher D.A., Barnard P.O., and Truong T.P. (1988), The role of Stated Preference methods in studies of travel choice Journal of Transport Economics and Policy 22.Google Scholar
  17. [170]
    Louviere J.J. (1988), Conjoint analysis modelling of Stated Preferences Journal of Transport Economics and Policy 22.Google Scholar
  18. [215]
    Ortuzar J. De D. (1992), Stated Preference in travel demand modelling 6th World Conference on Transportation Research, Lyon.Google Scholar
  19. [222]
    Pearmin D., Swanson J., Kroes E., and Bradley M. (1991). Stated Preferences Techniques: a guide to practice. Steer Davies Gleave and Hague Consulting Group, London.Google Scholar
  20. [39]
    Box G., Hunter W., and Hunter G. (1978), Statistics for experiment, John Wiley and S.Google Scholar
  21. [24]
    Ben-Akiva M., and Morikawa T. (1990). Estimation of switching models from Revealed Preference and Stated Intention Transportation Research 24 A.Google Scholar
  22. [43]
    Bradley M.A., and Daly A.J. (1992). Estimation of logit choice models using mixed Stated Preference and Revealed Preference information 20`h PTRC Sam, Manchester.Google Scholar
  23. [47]
    Cantarella G. E., E. Cascetta, V. Adamo, and V. Astarita (1999). A doubly dynamic traffic assignment model for planning applications. Proceedings of the 14th International Symposium on Transportation and Traffic Theory, Jerusalem, Israel.Google Scholar
  24. [57]
    Cascetta E. (1986). A class of travel demand estimators using traffic flows. CRT Publication 375, Université de Montreal, Montreal, Canada.Google Scholar
  25. [245]
    Van Zuylen J.H., and Willumsen L.G. (1980). The most likely trip matrix estimated from traffic counts Transportation Research 14B: 281–293.Google Scholar
  26. [177]
    Maher. M. J. (1983). Inferences on trip matrices from observation on link volumes: a statistical approach. Transportation Research 7B: 435–447.Google Scholar
  27. [56]
    Cascetta E. (1984). Estimation of trip matrices from traffic counts and survey data: a generalized least squares estimator Transportation Research 8B: 289–299.Google Scholar
  28. [106]
    Di Gangi M. (1988). Una valutazione delle prestazioni statistiche degli estimatori della matrice the combinano i risultati di indagini e/o modelli con i conteggi di flussi di traffico Ricerca Operativa 51: 23–59.Google Scholar
  29. [266]
    Wu.J. H., Y. Chen, and M. Florian (1995). The continuous dynamic network loading problem: a mathematical formulation and solution method. Transportation Research 32B: 173–187.Google Scholar
  30. [268]
    Yang H., and S. Yagar (1995). Traffic assignment and signal control in saturated road networks Transportation Research 29 A: 125–139.Google Scholar
  31. [54]
    Cantarella G.E., and M. Binetti (2000). Stochastic Assignment with Gamma Distributed Perceived Costs. Proceedings of the 6th Meeting of the EURO Working Group on Transportation. Gothenburg, and Sweden, and September 1998, forthcoming.Google Scholar
  32. [91]
    Cremer M. and Keller H. (1987). A new class of dynamic methods for the identification of Origin-Destination flows Transportation Research 21B: 117–132.Google Scholar
  33. [201]
    Nguyen S., and S. Pallottino (1986). Assegnazione dei passeggeri ad un sistema di linee urbane: determinazione degli ipercammini minimi. Ricerca Operativa 39: 207–230.Google Scholar
  34. [213]
    Okutani I., Stephanades Y. (1984). Dynamic prediction of traffic volume through Kalman Filtering theory. Transportation Research 18B: 1–11.Google Scholar
  35. [9]
    Ashok K. and Ben-Akiva M. (1993) Dynamic Origin-destination Matrix Estimation and Prediction for Real Time-Traffic Management Systems. in C. F. Daganzo editor, International Symposium on Transportation and Traffic Theory, Elsevier Science Pub. Co.: 465–484.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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