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Discrete Choice Analysis

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Analytical Decision-Making Methods for Evaluating Sustainable Transport in European Corridors

Part of the book series: SxI - Springer for Innovation / SxI - Springer per l'Innovazione ((SXIINNO,volume 11))

Abstract

This chapter gives an overview of discrete choice analysis techniques. First we present a reflection about the meaning of the words ‘discrete’ and ‘choice’. Then we provide an overview of the sorts of choices in passenger and freight transport that have been treated as discrete choice problems. The next section presents the basic random utility theory, upon which most discrete choice models have been based. Different types of discrete choice models are then discussed: the workhorse of discrete choice modelling—the multinomial logit model (MNL), the nested logit and other Generalised Extreme Value (GEV) models, the probit model, the mixed logit and latent class models, ordered response models and aggregate logit models. Then we briefly discuss the estimation of discrete choice models and their application for demand forecasting and for policy simulation. The last section contains a summary and conclusions and a discussion on the future research directions.

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Notes

  1. 1.

    There are also some examples of discrete-continuous choices in transport (e.g. Train [35], de Jong [13], Bhat [4]) and of duration modeling (see de Jong [14], Bhat [5]).

  2. 2.

    Non-linear specifications of the utility function, such as functions with logarithmic or Box-Cox transformations (e.g. [28]), are also possible.

  3. 3.

    Strictly speaking there is also a ‘scale’ parameter, which reflects the variance of the random component of utility and is used for normalising the model. It is called ‘scale’ parameter, because it scales the β parameters in (8.2a)–(8.2c); a higher random variance leads to lower estimated values of the β’ s.

  4. 4.

    There are other reasons in the discrete choice literature for including the error terms.

  5. 5.

    The name ‘logit’ comes from the fact that in the model presented above we are actually looking at differences between utility functions and the difference of two Gumbel distributions follows a logistic distribution. A ‘logistic’ or ‘logit’ model, that was invoked by assuming a logistic distribution for the error terms, had been around long before the above RUM-based model was first presented by especially McFadden (see [10]).

  6. 6.

    The same logsum variable also has a meaning in welfare analysis: this logsum can be converted into money units (e.g. using a costs coefficient) and then the difference between the monetised logsum for a project alternative (with some infrastructure project) and the one for a reference case (without the project) can be used as the change in consumer surplus as a result of the project [15].

  7. 7.

    Yet this is not the same model as the ordered response model, that is described in Sect. 8.8 of this chapter. OGEV is a generalisation of MNL, whereas ordered response models assume the same (non-RUM) response mechanism behind all ordered categories.

  8. 8.

    An exception, in passenger transport, is the SILVESTER model for Stockholm [23].

  9. 9.

    For the latter problem an alternative category of models is formed by the count data models, such as the Poisson regression model (see [7]).

  10. 10.

    In practice it is often even difficult to obtain plausible transport time and costs coefficients when estimating on aggregate data. Prof. Moshe Ben-Akiva once suggested here to assume a value of time distribution to allow for heterogeneity between shipments.

  11. 11.

    An alternative for the difference form is the ‘ratio form’ where the right-hand side has Pi/Pj and xiw/xjw, which has the disadvantage that the choice of the base mode (in the denominator of the dependent variable) affects the estimation results and the elasticities from the model. The difference form does not have this disadvantage.

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Author information

Authors and Affiliations

  1. Significance and ITS University of Leeds, Koninginnegracht 23, 2514 AB, The Hague, The Netherlands

    Gerard de Jong

  2. Significance and VU University of Amsterdam, Koninginnegracht 23, 2514 AB, The Hague, The Netherlands

    Eric Kroes

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  1. Gerard de Jong
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  2. Eric Kroes
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Correspondence to Gerard de Jong .

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Editors and Affiliations

  1. Regional and Urban Studies and Planning, Politecnico di Torino, Torino, Torino, Italy

    Isabella M. Lami

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de Jong, G., Kroes, E. (2014). Discrete Choice Analysis. In: Lami, I. (eds) Analytical Decision-Making Methods for Evaluating Sustainable Transport in European Corridors. SxI - Springer for Innovation / SxI - Springer per l'Innovazione, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-04786-7_8

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