  Emerging transportation modes, including car-sharing, bike-sharing, and
ride-hailing, are transforming urban mobility but have been shown to reinforce
socioeconomic inequities. Spatiotemporal demand prediction models for these new
mobility regimes must therefore consider fairness as a first-class design
requirement. We present FairST, a fairness-aware model for predicting demand
for new mobility systems. Our approach utilizes 1D, 2D and 3D convolutions to
integrate various urban features and learn the spatial-temporal dynamics of a
mobility system, but we include fairness metrics as a form of regularization to
make the predictions more equitable across demographic groups. We propose two
novel spatiotemporal fairness metrics, a region-based fairness gap (RFG) and an
individual-based fairness gap (IFG). Both quantify equity in a spatiotemporal
context, but vary by whether demographics are labeled at the region level (RFG)
or whether population distribution information is available (IFG). Experimental
results on real bike share and ride share datasets demonstrate the
effectiveness of the proposed model: FairST not only reduces the fairness gap
by more than 80%, but can surprisingly achieve better accuracy than
state-of-the-art yet fairness-oblivious methods including LSTMs, ConvLSTMs, and
3D CNN.
