Fairness-Aware Demand Prediction for New Mobility-猫眼课题宝

DOI：

10.1609/aaai.v34i01.5458

发表时间：

2020-04

期刊：

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

An Yan;Bill Howe

新兴的交通模式，包括汽车共享、自行车共享和叫车，正在改变城市交通，但也被证明会加剧社会经济不平等。这些服务依赖于准确的需求预测，但这些模型所训练的需求数据反映了人口统计学，社会经济条件和根深蒂固的地理模式的偏见。为了解决这些偏见，提高公平性，我们提出了FairST，一个公平意识的需求预测模型时空城市应用，重点是新的流动性。我们使用1D（时变，空间常数），2D（空间变化，时间常数）和3D（时间和空间变化）卷积分支来整合异构特征，同时将公平性指标作为一种正则化形式，以提高人口统计群体的公平性。我们提出了两个时空公平性指标，基于区域的公平差距（RFG），适用于人口统计信息作为一个常数提供的区域，和基于个人的公平差距（IFG），适用于人口统计信息的连续分布时。在自行车共享和乘车共享数据集上的实验结果表明，FairST可以减少多个敏感属性（即种族，年龄和教育水平）的需求预测不公平，同时实现比最先进的公平遗忘方法更好的准确性。

Emerging transportation modes, including car-sharing, bike-sharing, and ride-hailing, are transforming urban mobility yet have been shown to reinforce socioeconomic inequity. These services rely on accurate demand prediction, but the demand data on which these models are trained reflect biases around demographics, socioeconomic conditions, and entrenched geographic patterns. To address these biases and improve fairness, we present FairST, a fairness-aware demand prediction model for spatiotemporal urban applications, with emphasis on new mobility. We use 1D (time-varying, space-constant), 2D (space-varying, time-constant) and 3D (both time- and space-varying) convolutional branches to integrate heterogeneous features, while including fairness metrics as a form of regularization to improve equity across demographic groups. We propose two spatiotemporal fairness metrics, region-based fairness gap (RFG), applicable when demographic information is provided as a constant for a region, and individual-based fairness gap (IFG), applicable when a continuous distribution of demographic information is available. Experimental results on bike share and ride share datasets show that FairST can reduce inequity in demand prediction for multiple sensitive attributes (i.e. race, age, and education level), while achieving better accuracy than even state-of-the-art fairness-oblivious methods.

Fairness-Aware Demand Prediction for New Mobility

文献摘要

相似文献