# FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems

arXiv: https://arxiv.org/abs/1907.03827 · HTML: https://mlsanity.com/paper/1907.03827/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 13 of 13)

- [Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification](https://mlsanity.com/paper/2101.07361/) (2021)
- [SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models](https://mlsanity.com/paper/2401.15270/) (2024)
- [Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications](https://mlsanity.com/paper/2507.00914/) (2025)
- [Referee-Meta-Learning for Fast Adaptation of Locational Fairness](https://mlsanity.com/paper/2402.13379/) (2024)
- [Survey of Federated Learning Models for Spatial-Temporal Mobility Applications](https://mlsanity.com/paper/2305.05257/) (2023)
- [Improving the Fairness of Deep-Learning, Short-term Crime Prediction with Under-reporting-aware Models](https://mlsanity.com/paper/2406.04382/) (2024)
- [Adapting to Skew: Imputing Spatiotemporal Urban Data with 3D Partial Convolutions and Biased Masking](https://mlsanity.com/paper/2301.04233/) (2023)
- [Analysing Fairness of Privacy-Utility Mobility Models](https://mlsanity.com/paper/2304.06469/) (2023)
- [HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecasting](https://mlsanity.com/paper/2412.00316/) (2024)
- [Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks](https://mlsanity.com/paper/2412.16523/) (2024)
- [Fragments to Facts: Partial-Information Fragment Inference from LLMs](https://mlsanity.com/paper/2505.13819/) (2025)
- [Mind the Gaps: Auditing and Reducing Group Inequity in Large-Scale Mobility Prediction](https://mlsanity.com/paper/2510.26940/) (2025)
- [Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks](https://mlsanity.com/paper/2606.23257/) (2026)

## Abstract (arXiv)

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.