用因果协同神经微分方程预测城市人流，减少长期误差积累。
C3DE: Causal-Aware Collaborative Neural Controlled Differential Equation for Long-Term Urban Crowd Flow Prediction
- 双路径神经微分方程捕捉人流与兴趣点多尺度异步演化
- 基于反事实的因果估计器降低虚假相关性，提升长期预测精度
- 适合研究城市交通、人流预测的学者与智慧城市建设者
长期城市人流预测因序列过长和采样间隔增大，面临累积采样误差问题。为此，本文引入神经控制微分方程（NCDEs）缓解该问题。然而，兴趣点（POIs）演化对人流的复杂影响，以及人流与POI在多时间尺度上的异步动态和潜在虚假因果关系，给NCDE的应用带来挑战。为此，提出因果感知协同神经微分方程（C3DE），以建模长期人流动态。具体地，采用双路径NCDE作为主干，有效捕捉跨多时间尺度的协同信号异步演化；设计基于反事实的因果效应估计算法，量化POI对人流的真实影响，抑制虚假相关性的积累；最后通过融合的协同信号进行长期预测。在三个真实世界数据集上的实验表明，C3DE表现优异，尤其在人流波动显著的城市中优势明显。
原文摘要 · Abstract (English)
Long-term urban crowd flow prediction suffers significantly from cumulative sampling errors, due to increased sequence lengths and sampling intervals, which inspired us to leverage Neural Controlled Differential Equations (NCDEs) to mitigate this issue. However, regarding the crucial influence of Points of Interest (POIs) evolution on long-term crowd flow, the multi-timescale asynchronous dynamics between crowd flow and POI distribution, coupled with latent spurious causality, poses challenges to applying NCDEs for long-term urban crowd flow prediction. To this end, we propose Causal-aware Collaborative neural CDE (C3DE) to model the long-term dynamic of crowd flow. Specifically, we introduce a dual-path NCDE as the backbone to effectively capture the asynchronous evolution of collaborative signals across multiple time scales. Then, we design a dynamic correction mechanism with the counterfactual-based causal effect estimator to quantify the causal impact of POIs on crowd flow and minimize the accumulation of spurious correlations. Finally, we leverage a predictor for long-term prediction with the fused collaborative signals of POI and crowd flow. Extensive experiments on three real-world datasets demonstrate the superior performance of C3DE, particularly in cities with notable flow fluctuations.
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