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Spatio-Temporal Partial Sensing Forecast for Long-term Traffic

arXiv 2408.02689 ↗First posted 2 Aug 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Traffic forecasting uses recent measurements by sensors installed at chosen locations to forecast the future road traffic. Existing work either assumes all locations are equipped with sensors or focuses on short-term forecast. This paper studies partial sensing forecast of long-term traffic, assuming sensors are available only at some locations. The problem is challenging due to the unknown data distribution at unsensed locations, the intricate spatio-temporal correlation in long-term forecasting, as well as noise to traffic patterns. We propose a Spatio-temporal Long-term Partial sensing Forecast model (SLPF) for traffic prediction, with several novel contributions, including a rank-based embedding technique to reduce the impact of noise in data, a spatial transfer matrix to overcome the spatial distribution shift from sensed locations to unsensed locations, and a multi-step training process that utilizes all available data to successively refine the model parameters for better accuracy. Extensive experiments on several real-world traffic datasets demonstrate its superior performance. Our source code is at https://github.com/zbliu98/SLPF

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 36 of its 53 references that are in the map. First posted on arXiv 2 Aug 2024.

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References · 36

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  2. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  3. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  6. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  7. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  8. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
  9. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 212
  10. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 209
  11. 2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 149
  12. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 135
24 more
  1. 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspectivecited by 126
  2. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 122
  3. 2022Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecastingcited by 117
  4. 2024TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignmentcited by 97
  5. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 93
  6. 2020Inductive Graph Neural Networks for Spatiotemporal Krigingcited by 90
  7. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  8. 2024Spatial-Temporal Large Language Model for Traffic Predictioncited by 81
  9. 2022Spatio-Temporal Self-Supervised Learning for Traffic Flow Predictioncited by 77
  10. 2022STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Predictioncited by 45
  11. 2024A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Datacited by 39
  12. 2022Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Citiescited by 38
  13. 2023Kriging Convolutional Networkscited by 30
  14. 2023INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Krigingcited by 27
  15. 2020Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecastingcited by 23
  16. 2024GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missingcited by 20
  17. 2023Graph Neural Processes for Spatio-Temporal Extrapolationcited by 18
  18. 2024TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Expertscited by 18
  19. 2022Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Predictioncited by 15
  20. 2023FRIGATE: Frugal Spatio-temporal Forecasting on Road Networkscited by 14
  21. 2023Dynamic Hypergraph Structure Learning for Traffic Flow Forecastingcited by 14
  22. 2023Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecastingcited by 11
  23. 2023Missing Value Imputation for Multi-attribute Sensor Data Streams via Message Propagation (Extended Version)cited by 6
  24. 2024Spatial-temporal Forecasting for Regions without Observationscited by 6

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