Behavioural theories of dispersion and the mis-specification of travel demand models
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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Volume (Year): 16 (1982)
Issue (Month): 3 (June)
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