ADFormer通过差异注意力捕捉需求时空关联，提升预测精度。
ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting
- 用差异注意力捕获原始空间相关性，实现注意力去噪。
- 设计时空差异化聚合策略，统一低层与高层关联。
- 在出租车与共享单车数据集上表现优异，适合交通调度应用。
乘客需求预测有助于优化车辆调度，提升城市运行效率。近年来，基于注意力的方法被用于捕捉时空数据的动态特性。然而，依赖启发式掩码策略的现有方法难以充分适应复杂的时空相关性，导致模型无法聚焦于正确上下文，且忽视了现实世界中的高层关联。有效融合高层关联与原始关联至关重要。为此，我们提出聚合差异注意力变换器（ADFormer），为需求预测提供新思路。具体而言，利用差异注意力捕捉原始空间相关性并实现注意力去噪；基于时空特性设计不同的聚合策略；将原始关联与高层关联统一，使模型能够捕捉整体时空关系。在出租车和共享单车数据集上的实验验证了该模型的有效性与高效性，展示了其实际价值。代码已公开于 https://github.com/decisionintelligence/ADFormer。
原文摘要 · Abstract (English)
Passenger demand forecasting helps optimize vehicle scheduling, thereby improving urban efficiency. Recently, attention-based methods have been used to adequately capture the dynamic nature of spatio-temporal data. However, existing methods that rely on heuristic masking strategies cannot fully adapt to the complex spatio-temporal correlations, hindering the model from focusing on the right context. These works also overlook the high-level correlations that exist in the real world. Effectively integrating these high-level correlations with the original correlations is crucial. To fill this gap, we propose the Aggregation Differential Transformer (ADFormer), which offers new insights to demand forecasting promotion. Specifically, we utilize Differential Attention to capture the original spatial correlations and achieve attention denoising. Meanwhile, we design distinct aggregation strategies based on the nature of space and time. Then, the original correlations are unified with the high-level correlations, enabling the model to capture holistic spatio-temporal relations. Experiments conducted on taxi and bike datasets confirm the effectiveness and efficiency of our model, demonstrating its practical value. The code is available at https://github.com/decisionintelligence/ADFormer.
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