Over the past few years, machine-learning techniques have expanded enormously. These approaches are increasingly being applied to traffic and transportation problems formerly reserved for formal statistical approaches such as discrete choice models. Part of the reason for this has to do with research trends, but there are some potential advantages associated with such techniques, including the ability to model nonlinear systems; the ease with which symbolic, nominal, or categorical variables can be included; and the ability of these methods to deal with noisy data. The use of two modeling techniques, the nested logit model and the multilayer perceptron artificial neural network, was investigated in terms of their applicability to the household vehicle choice problem. Both methods generated strong results, although the multilayer perceptron artificial neural network yielded better predictive potential.


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    Title :

    Nested Logit Models and Artificial Neural Networks for Predicting Household Automobile Choices: Comparison of Performance


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    2002-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Nested Logit Models and Artificial Neural Networks for Predicting Household Automobile Choices: Comparison of Performance

    Mohammadian, A. / Miller, E. J. / Transportation Research Board | British Library Conference Proceedings | 2002




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