Working papers
An artificial neural network based method to uncover the Value-of-Travel-Time distribution
This study proposes a novel Artificial Neural Network (ANN) based method to derive the Value-of-Travel-Time (VTT) distribution. This highly flexible method complements recently proposed nonparametric methods
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A logistic regression based method to uncover the Value-of-Travel-Time distribution
This research note provides a new simple and transparent nonparametric method to uncover the value-of-time distribution
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A computational efficient way to compute P-RRM attribute vectors
This note presents a computationally efficient way to compute P-RRM attribute vectors. This method is especially useful in the context of large-scale applications. ...
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On the robustness of efficient experimental designs towards the underlying decision rule
This paper investigates the robustness of effcient experimental designs towards the underlying decision rule. We develop efficient designs based on RRM....
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Accounting for variation in choice set size in RRM models
This paper derives a trick to account for variation in choice set size in RRM models. In many choice situations the choice set size varies across choice observations. As in RRM models regret level differences increase with increasing choice set size, not accounting for variation in choice set size results in RRM models to predict ... Click here to go to the full text.
Large-scale transport model forecasts: does the decision rule matter?
This paper is the first to study to what extent decision rules, embedded in disaggregate discrete choice models, matter for large-scale, aggregate level mobility forecasts. ...
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