C3DE：用于长期城市客流预测的因果感知协同神经受控微分方程
C3DE: Causal-Aware Collaborative Neural Controlled Differential Equation for Long-Term Urban Crowd Flow Prediction
AI总结：
针对长期城市客流预测的累积采样误差及POI演化引发的异步动态与伪因果问题，提出因果感知协同神经受控微分方程C3DE，通过双路径NCDE和反事实动态校正机制实现高精度长期预测。
摘要：
长期城市客流预测因序列长度和采样间隔的增加而严重受累积采样误差影响，这促使我们利用神经受控微分方程（NCDEs）来缓解此问题。然而，鉴于兴趣点（POIs）演化对长期客流的关键影响，客流与POI分布间的多时间尺度异步动态，加上潜在的伪因果关系，对将NCDEs应用于长期城市客流预测提出了挑战。为此，我们提出因果感知协同神经CDE（C3DE）来建模客流的长期动态。具体而言，我们引入双路径NCDE作为骨干网络，以有效捕获跨多个时间尺度协同信号的异步演化。接着，我们设计了一种带有基于反事实因果效应估计器的动态校正机制，以量化POI对客流的因果影响并最小化伪相关性的累积。最后，我们利用预测器结合POI与客流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大量实验表明了C3DE的优越性能，尤其是在客流波动显著的城市中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
