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