FairST: Equitable spatial and temporal demand prediction for new mobility systems

Abstract

We present a fairness-aware model for predicting demand for new mobility systems. Our approach, called FairST, consists of 1D, 2D and 3D convolutions to learn the spatial-temporal dynamics of a mobility system, and fairness regularizers that guide the model to make equitable predictions. We propose two fairness metrics, region-based fairness gap (RFG) and individual-based fairness gap (IFG), that measure equity gaps between social groups for new mobility systems. Experimental results on two real-world datasets demonstrate the effectiveness of the proposed model: FairST not only reduces the fairness gap by more than 80%, but achieves better accuracy than state-of-the-art but fairness-oblivious methods including LSTMs, ConvLSTMs, and 3D CNN.