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Behavioural theories of dispersion and the mis-specification of travel demand models

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  • Williams, H. C. W. L.
  • Ortuzar, J. D.
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    Abstract

    Conventional of first generation transport models have for some time been heavily criticised for their lack of behavioural content and inefficient use of data; more recently second generation or disaggregate travel demand models based on a theory of choice between discrete alternatives have also been viewed critically. First, it has been argued that implemented structures--and particularly the Multinomial Logit model--have not been sufficiently general to accommodate the "interaction" between alternatives; and second, and perhaps more importantly, that the underpinning theory, involving a perfectly discriminating rational man (homo economicus), endowed with complete information is an unacceptable starting point for the analysis of behaviour. In this paper the potential errors in forecasting travel response arising from theoretical misrepresentation are investigated; more generally, the problems of inference and hypothesis testing in conjuction with cross-sectional models are noted. A framework is developed to examine the consequences of the divergence between the behaviour of individuals in a system, the observed, and that description of their behaviour (which is embedded in a forecasting model) imputed by an observer, the modeller. The extent of this divergence in the context of response to particular policy stimuli is examined using Monte Carlo simulation for the following examples: (i) alternative assumptions relating to the structure of models reflecting substitution between similar alternatives; (ii) alternative decision-making processes; (iii) limited information and "satisficing" behaviour; and (iv) existence of habit in choice modelling. The method has allowed particular conclusions to be nade about the importance of theoretical misrepresentation in the four examples. More generally, it highlights the problems of forecasting response with cross-sectional models and draws attention to the problem of validation which is all too often associated solely with the goodness of statistical fit of analytic functions to data patterns.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

    Volume (Year): 16 (1982)
    Issue (Month): 3 (June)
    Pages: 167-219

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    Handle: RePEc:eee:transb:v:16:y:1982:i:3:p:167-219

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    Cited by:
    1. Cascetta, Ennio & Russo, Francesco & Viola, Francesco A. & Vitetta, Antonino, 2002. "A model of route perception in urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 36(7), pages 577-592, August.
    2. Elisabetta Cherchi & Juan Dios Ortúzar, 2008. "Empirical Identification in the Mixed Logit Model: Analysing the Effect of Data Richness," Networks and Spatial Economics, Springer, vol. 8(2), pages 109-124, September.
    3. Basar, Gözen & Bhat, Chandra, 2004. "A parameterized consideration set model for airport choice: an application to the San Francisco Bay Area," Transportation Research Part B: Methodological, Elsevier, vol. 38(10), pages 889-904, December.
    4. Laurie Garrow & Tudor Bodea & Misuk Lee, 2010. "Generation of synthetic datasets for discrete choice analysis," Transportation, Springer, vol. 37(2), pages 183-202, March.
    5. Chorus, Caspar G. & Arentze, Theo A. & Molin, Eric J.E. & Timmermans, Harry J.P. & Van Wee, Bert, 2006. "The value of travel information: Decision strategy-specific conceptualizations and numerical examples," Transportation Research Part B: Methodological, Elsevier, vol. 40(6), pages 504-519, July.
    6. Ortúzar, Juan de Dios, 2001. "On the development of the nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(2), pages 213-216, February.
    7. Cherchi, Elisabetta & Guevara, Cristian Angelo, 2012. "A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance–covariance matrix," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 321-332.
    8. Cantillo, Víctor & Ortúzar, Juan de Dios, 2005. "A semi-compensatory discrete choice model with explicit attribute thresholds of perception," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 641-657, August.
    9. Cascetta, Ennio & Papola, Andrea, 2009. "Dominance among alternatives in random utility models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(2), pages 170-179, February.
    10. Raveau, Sebastián & Yáñez, María Francisca & Ortúzar, Juan de Dios, 2012. "Practical and empirical identifiability of hybrid discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1374-1383.
    11. Koppelman, Frank S. & Sethi, Vaneet, 2005. "Incorporating variance and covariance heterogeneity in the Generalized Nested Logit model: an application to modeling long distance travel choice behavior," Transportation Research Part B: Methodological, Elsevier, vol. 39(9), pages 825-853, November.
    12. Cantillo, Víctor & Heydecker, Benjamin & de Dios Ortúzar, Juan, 2006. "A discrete choice model incorporating thresholds for perception in attribute values," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 807-825, November.
    13. GRAMMIG, Joachim & HUJER, Reinhard & SCHEIDLER, Michael, 2001. "The econometrics of airline network management," CORE Discussion Papers 2001055, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Cantillo, Víctor & Amaya, Johanna & Ortúzar, J. de D., 2010. "Thresholds and indifference in stated choice surveys," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 753-763, July.

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