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

TL;DR

This paper examines fairness issues in travel behavior prediction models, revealing prediction disparities across social groups, comparing deep neural networks and discrete choice models, and proposing a bias mitigation method to promote equitable transportation policies.

Contribution

It introduces the concept of computational fairness in travel behavior modeling, compares DNN and DCM prediction disparities, and proposes a regularization method to reduce bias.

Findings

Both DNN and DCM show prediction disparities for disadvantaged groups.

DNN outperforms DCM in reducing prediction disparities.

Bias mitigation methods can lessen prediction disparities despite model specifics.

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. 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.