mlsanityData to 14 Oct 2026

C3DE: Causal-Aware Collaborative Neural Controlled Differential Equation for Long-Term Urban Crowd Flow Prediction

arXiv 2509.12289 ↗First posted 15 Sep 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

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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References · 19

  1. 2018Neural Ordinary Differential Equationscited by 3,779
  2. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
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  10. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 137
  11. 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
  12. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 123
7 more
  1. 2022Multivariate Time Series Forecasting with Dynamic Graph Neural ODEscited by 72
  2. 2020Learning Continuous System Dynamics from Irregularly-Sampled Partial Observationscited by 61
  3. 2023DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Predictioncited by 49
  4. 2021Human Trajectory Prediction via Counterfactual Analysiscited by 42
  5. 2024Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equationscited by 11
  6. 2024Temporal Graph ODEs for Irregularly-Sampled Time Seriescited by 11
  7. 2024Frequency Enhanced Pre-training for Cross-city Few-shot Traffic Forecastingcited by 4

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