Generating realistic human flows across regions is essential for our understanding of urban structures and population activity patterns, enabling important applications in the fields of urban planning and management. However, a notable shortcoming of most existing mobility generation methodologies is neglect of prediction fairness, which can result in underestimation of mobility flows across regions with vulnerable population groups, potentially resulting in inequitable resource distribution and infrastructure development. To overcome this limitation, our study presents a novel, fairness-aware deep learning model, FairMobi-Net, for inter-region human flow prediction. The FairMobi-Net model uniquely incorporates fairness loss into the loss function and employs a hybrid approach, merging binary classification and numerical regression techniques for human flow prediction. We validate the FairMobi-Net model using comprehensive human mobility datasets from four U.S. cities, predicting human flow at the census-tract level. Our findings reveal that the FairMobi-Net model outperforms state-of-the-art models (such as the DeepGravity model) in producing more accurate and equitable human flow predictions across a variety of region pairs, regardless of regional income differences. The model maintains a high degree of accuracy consistently across diverse regions, addressing the previous fairness concern. Further analysis of feature importance elucidates the impact of physical distances and road network structures on human flows across regions. With fairness as its touchstone, the model and results provide researchers and practitioners across the fields of urban sciences, transportation engineering, and computing with an effective tool for accurate generation of human mobility flows across regions.

翻译：生成跨区域真实人流对于理解城市结构与人口活动模式至关重要，可为城市规划与管理领域的重要应用提供支持。然而，现有大多数移动性生成方法的一个显著缺陷是忽视预测公平性，这可能导致对弱势群体所在区域的人流量低估，进而造成资源分配与基础设施发展不均衡。为克服这一局限，本研究提出了一种新颖的面向公平性深度学习模型FairMobi-Net，用于跨区域人流预测。该模型创新性地将公平性损失函数纳入损失函数，并采用混合方法融合二分类与数值回归技术进行人流预测。我们利用美国四个城市的综合人类移动性数据集进行验证，在人口普查区层面预测人流。研究发现，无论区域收入差异如何，FairMobi-Net模型在各类区域对间生成更精准且更公平的人流预测方面均优于现有最优模型（如DeepGravity模型）。该模型在不同区域间始终保持高度准确性，解决了先前存在的公平性担忧。进一步的特征重要性分析揭示了物理距离与道路网络结构对跨区域人流的影响。以公平性为基准，该模型与研究成果为城市科学、交通工程与计算科学领域的研究者及从业者提供了跨区域人流精准生成的有效工具。