  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. We first operationalize computational fairness by equality of
opportunity, then differentiate between the bias inherent in data and the bias
introduced by modeling. We then demonstrate the prediction disparities in
travel behavior modeling using the 2017 National Household Travel Survey (NHTS)
and the 2018-2019 My Daily Travel Survey in Chicago. Empirically, deep neural
network (DNN) and discrete choice models (DCM) reveal consistent prediction
disparities across multiple social groups: both over-predict the false negative
rate of frequent driving for the ethnic minorities, the low-income and the
disabled populations, and falsely predict a higher travel burden of the
socially disadvantaged groups and the rural populations than reality. Comparing
DNN with DCM, we find that DNN can outperform DCM in prediction disparities
because of DNN's smaller misspecification error. To mitigate prediction
disparities, this study introduces an absolute correlation regularization
method, which is evaluated with synthetic and real-world data. The results
demonstrate the prevalence of prediction disparities in travel behavior
modeling, and the disparities still persist regarding a variety of model
specifics such as the number of DNN layers, batch size and weight
initialization. Since these prediction disparities can exacerbate social
inequity if prediction results without fairness adjustment are used for
transportation policy making, we advocate for careful consideration of the
fairness problem in travel behavior modeling, and the use of bias mitigation
algorithms for fair transport decisions.
