mlsanityData to 14 Oct 2026

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

arXiv 1610.00081 ↗First posted 1 Oct 2016cs.AI cs.LGcited by 333

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Abstract (arXiv)

Forecasting the flow of crowds is of great importance to traffic management and public safety, yet a very challenging task affected by many complex factors, such as inter-region traffic, events and weather. In this paper, we propose a deep-learning-based approach, called ST-ResNet, to collectively forecast the in-flow and out-flow of crowds in each and every region through a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the framework of the residual neural networks to model the temporal closeness, period, and trend properties of the crowd traffic, respectively. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of the crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. We evaluate ST-ResNet based on two types of crowd flows in Beijing and NYC, finding that its performance exceeds six well-know methods.

Key facts. Cited by 333 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Oct 2016.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 54; 2022: 54; 2023: 74; 2024: 65; 2025: 58; 2026: 28.

333 papers.

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  1. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 287
  2. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
  3. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 210
  4. 2022SimVP: Simpler yet Better Video Predictioncited by 184
  5. 2021PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learningcited by 182
  6. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 157
  7. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 137
  8. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 110
  9. 2024UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Predictioncited by 87
  10. 2022Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learningcited by 86
  11. 2024UrbanGPT: Spatio-Temporal Large Language Modelscited by 81
  12. 2022Spatio-Temporal Self-Supervised Learning for Traffic Flow Predictioncited by 77
38 more
  1. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  2. 2022AirFormer: Predicting Nationwide Air Quality in China with Transformerscited by 73
  3. 2022A Survey on Graph Representation Learning Methodscited by 66
  4. 2021Encoding physics to learn reaction-diffusion processescited by 61
  5. 2022Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Versioncited by 59
  6. 2023OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learningcited by 54
  7. 2021Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Networkcited by 52
  8. 2023Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatmentcited by 47
  9. 2023PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Predictioncited by 44
  10. 2021TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learningcited by 42
  11. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 40
  12. 2022Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Predictioncited by 39
  13. 2024A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Datacited by 39
  14. 2023Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environmentcited by 37
  15. 2021When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?cited by 36
  16. 2022Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecastingcited by 36
  17. 2024VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecastingcited by 35
  18. 2022Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networkscited by 34
  19. 2024Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlookcited by 33
  20. 2022Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learningcited by 32
  21. 2023SwinLSTM:Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTMcited by 32
  22. 2022Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networkscited by 29
  23. 2022Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Modelscited by 29
  24. 2023INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Krigingcited by 27
  25. 2025Harnessing Vision Models for Time Series Analysis: A Surveycited by 26
  26. 2021Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecastingcited by 23
  27. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
  28. 2024Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Eracited by 23
  29. 2022SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learningcited by 22
  30. 2023MLPST: MLP is All You Need for Spatio-Temporal Predictioncited by 22
  31. 2021A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecastingcited by 21
  32. 2023LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Predictioncited by 20
  33. 2023Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networkcited by 20
  34. 2023PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Predictioncited by 20
  35. 2024Towards Explainable Traffic Flow Prediction with Large Language Modelscited by 20
  36. 2023Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecastingcited by 19
  37. 2023Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Predictioncited by 19
  38. 2025Spatiotemporal-aware Trend-Seasonality Decomposition Network for Traffic Flow Forecastingcited by 19

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