mlsanityData to 7 Oct 2026

XXLTraffic: Expanding and Extremely Long Traffic forecasting beyond test adaptation

arXiv 2406.12693 ↗First posted 18 Jun 2024cs.LG cs.AIcited by 6

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Abstract (arXiv)

Traffic forecasting is crucial for smart cities and intelligent transportation initiatives, where deep learning has made significant progress in modeling complex spatio-temporal patterns in recent years. However, current public datasets have limitations in reflecting the distribution shift nature of real-world scenarios, characterized by continuously evolving infrastructures, varying temporal distributions, and long temporal gaps due to sensor downtimes or changes in traffic patterns. These limitations inevitably restrict the practical applicability of existing traffic forecasting datasets. To bridge this gap, we present XXLTraffic, largest available public traffic dataset with the longest timespan collected from Los Angeles, USA, and New South Wales, Australia, curated to support research in extremely long forecasting beyond test adaptation. Our benchmark includes both typical time-series forecasting settings with hourly and daily aggregated data and novel configurations that introduce gaps and down-sample the training size to better simulate practical constraints. We anticipate the new XXLTraffic will provide a fresh perspective for the time-series and traffic forecasting communities. It would also offer a robust platform for developing and evaluating models designed to tackle the extremely long forecasting problems beyond test adaptation. Our dataset supplements existing spatio-temporal data resources and leads to new research directions in this domain.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 30 of its 49 references that are in the map. First posted on arXiv 18 Jun 2024.

Builds on

30 of 49 references.

References · 30

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  4. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  5. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  6. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  7. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  8. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  9. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  10. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  11. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
  12. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
18 more
  1. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
  2. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 209
  3. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 169
  4. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 135
  5. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 122
  6. 2023LargeST: A Benchmark Dataset for Large-Scale Traffic Forecastingcited by 116
  7. 2023Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysiscited by 113
  8. 2022Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Versioncited by 59
  9. 2023DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Predictioncited by 49
  10. 2019Bayesian Temporal Factorization for Multidimensional Time Series Predictioncited by 47
  11. 2021TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learningcited by 42
  12. 2021Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecastingcited by 19
  13. 2021SSDNet: State Space Decomposition Neural Network for Time Series Forecastingcited by 15
  14. 2023MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimizationcited by 13
  15. 2023HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecastingcited by 12
  16. 2023Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)cited by 11
  17. 2019Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Unitscited by 5
  18. 2023Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecastingcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026From XXLTraffic to EvoXXLTraffic: Scaling Traffic Forecasting to Sensor-Evolving Networkscited by 1
  2. 2026Learning from Complexity: Exploring Dynamic Sample Pruning of Spatio-Temporal Trainingcited by 0
  3. 2026UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generationcited by 0
  4. 2026TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecastingcited by 0
  5. 2026A2TTA: Anchored-and-Agile Test-Time Adaptation for Evolving Traffic Sensor Networkscited by 0
  6. 2026RiskTraf: Risk-Extrapolated Residual Learning for Multi-Variate Traffic Flow Predictioncited by 0