  Short-term demand forecasting for on-demand ride-hailing services is one of
the fundamental issues in intelligent transportation systems. However, previous
travel demand forecasting research predominantly focused on improving
prediction accuracy, ignoring fairness issues such as systematic
underestimations of travel demand in disadvantaged neighborhoods. This study
investigates how to measure, evaluate, and enhance prediction fairness between
disadvantaged and privileged communities in spatial-temporal demand forecasting
of ride-hailing services. A two-pronged approach is taken to reduce the demand
prediction bias. First, we develop a novel deep learning model architecture,
named socially aware neural network (SA-Net), to integrate the
socio-demographics and ridership information for fair demand prediction through
an innovative socially-aware convolution operation. Second, we propose a
bias-mitigation regularization method to mitigate the mean percentage
prediction error gap between different groups. The experimental results,
validated on the real-world Chicago Transportation Network Company (TNC) data,
show that the de-biasing SA-Net can achieve better predictive performance in
both prediction accuracy and fairness. Specifically, the SA-Net improves
prediction accuracy for both the disadvantaged and privileged groups compared
with the state-of-the-art models. When coupled with the bias mitigation
regularization method, the de-biasing SA-Net effectively bridges the mean
percentage prediction error gap between the disadvantaged and privileged
groups, and also protects the disadvantaged regions against systematic
underestimation of TNC demand. Our proposed de-biasing method can be adopted in
many existing short-term travel demand estimation models, and can be utilized
for various other spatial-temporal prediction tasks such as crime incidents
predictions.
