FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems
FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems
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DOI：
10.1145/3347146.3359380
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
An Yan;B. Howe
中科院分区：
文献类型：
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作者：
An Yan;B. Howe
我们提出了一个公平意识模型预测新的移动系统的需求。我们的方法称为FairST，由1D，2D和3D卷积组成，用于学习移动系统的时空动态，以及指导模型进行公平预测的公平正则化器。我们提出了两个公平性指标，基于区域的公平差距（RFG）和基于个人的公平差距（IFG），衡量社会群体之间的公平差距的新的移动系统。在两个真实世界数据集上的实验结果证明了所提出的模型的有效性：FairST不仅将公平性差距减少了80%以上，而且比最先进的但公平性不明显的方法（包括LSTM，ConvLSTM和3D CNN）具有更好的准确性。
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.