Understanding the link between urban planning and commuting flows is crucial
for guiding urban development and policymaking. This research, bridging
computer science and urban studies, addresses the challenge of integrating
these fields with their distinct focuses. Traditional urban studies methods,
like the gravity and radiation models, often underperform in complex scenarios
due to their limited handling of multiple variables and reliance on overly
simplistic and unrealistic assumptions, such as spatial isotropy. While deep
learning models offer improved accuracy, their black-box nature poses a
trade-off between performance and explainability -- both vital for analyzing
complex societal phenomena like commuting flows. To address this, we introduce
TransFlower, an explainable, transformer-based model employing flow-to-flow
attention to predict urban commuting patterns. It features a geospatial encoder
with an anisotropy-aware relative location encoder for nuanced flow
representation. Following this, the transformer-based flow predictor enhances
this by leveraging attention mechanisms to efficiently capture flow
interactions. Our model outperforms existing methods by up to 30.8% Common Part
of Commuters, offering insights into mobility dynamics crucial for urban
planning and policy decisions.

通过引入 TransFlower，一种可解释的、基于 Transformer 的模型，使用流量 - 流量注意力来预测城市通勤模式，我们的研究在运动力学方面取得了 30.8% 的改进，为城市规划和政策决策提供了重要的移动动态洞察。