#5688. Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models

August 2026publication date
Proposal available till 23-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Civil and Structural Engineering;
Automotive Engineering;
Computer Science Applications;
Transportation;
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Abstract:
Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in machine learning algorithms. This study introduces an important missing dimension - computational fairness - to travel behavior analysis. It highlights the accuracy-fairness tradeoff instead of the single dimensional focus on prediction accuracy in the contexts of deep neural network (DNN) and discrete choice models (DCM). We first operationalize computational fairness by equality of opportunity, then differentiate between the bias inherent in data and the bias introduced by modeling. The models inheriting the inherent biases can risk perpetuating the existing inequality in the data structure, and the biases in modeling can further exacerbate it. We then demonstrate the prediction disparities in travel behavior modeling using the 20XX National Household Travel Survey (NHTS) and the 20XX–20XX My Daily Travel Survey in Chicago.
Keywords:
Deep neural network; Discrete choice models; Fairness in artificial intelligence; Machine learning; Travel behavior

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