mlsanityData to 14 Oct 2026

MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization

arXiv 2311.02880 ↗First posted 6 Nov 2023cs.LGcited by 13

Not on the map. Why?

Abstract (arXiv)

Traffic forecasting is a complex multivariate time-series regression task of paramount importance for traffic management and planning. However, existing approaches often struggle to model complex multi-range dependencies using local spatiotemporal features and road network hierarchical knowledge. To address this, we propose MultiSPANS. First, considering that an individual recording point cannot reflect critical spatiotemporal local patterns, we design multi-filter convolution modules for generating informative ST-token embeddings to facilitate attention computation. Then, based on ST-token and spatial-temporal position encoding, we employ the Transformers to capture long-range temporal and spatial dependencies. Furthermore, we introduce structural entropy theory to optimize the spatial attention mechanism. Specifically, The structural entropy minimization algorithm is used to generate optimal road network hierarchies, i.e., encoding trees. Based on this, we propose a relative structural entropy-based position encoding and a multi-head attention masking scheme based on multi-layer encoding trees. Extensive experiments demonstrate the superiority of the presented framework over several state-of-the-art methods in real-world traffic datasets, and the longer historical windows are effectively utilized. The code is available at https://github.com/SELGroup/MultiSPANS.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 25 of its 62 references that are in the map. First posted on arXiv 6 Nov 2023.

Builds on

25 of 62 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2017Decoupled Weight Decay Regularizationcited by 1,831
  5. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  6. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  7. 2020Simple and Deep Graph Convolutional Networkscited by 860
  8. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  9. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  10. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 443
  11. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 434
  12. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
13 more
  1. 2020Spatial-Temporal Transformer Networks for Traffic Flow Forecastingcited by 180
  2. 2019Deep Learning for Spatio-Temporal Data Mining: A Surveycited by 172
  3. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 146
  4. 2018Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Predictioncited by 122
  5. 2021STAN: Spatio-Temporal Attention Network for Next Location Recommendationcited by 102
  6. 2019Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecastingcited by 69
  7. 2022Structural Entropy Guided Graph Hierarchical Poolingcited by 62
  8. 2020Coupled Layer-wise Graph Convolution for Transportation Demand Predictioncited by 54
  9. 2023MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixingcited by 53
  10. 2023SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimizationcited by 35
  11. 2023Hierarchical State Abstraction Based on Structural Information Principlescited by 14
  12. 2023Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principlescited by 12
  13. 2023Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphscited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 5; 2026: 2.

13 papers.

Sort:

By area:

  1. 2024STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Modelcited by 24
  2. 2025Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learningcited by 14
  3. 2024Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysiscited by 11
  4. 2025Deep Learning for Time Series Forecasting: A Surveycited by 10
  5. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  6. 2024XXLTraffic: Expanding and Extremely Long Traffic forecasting beyond test adaptationcited by 6
  7. 2025ELSPR: Evaluator LLM Training Data Self-Purification on Non-Transitive Preferences via Tournament Graph Reconstructioncited by 5
  8. 2025Unsupervised Graph Clustering with Deep Structural Entropycited by 4
  9. 2024Embracing Large Language Models in Traffic Flow Forecastingcited by 3
  10. 2025STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputationcited by 2
  11. 2023Semi-Supervised Clustering via Structural Entropy with Different Constraintscited by 0
  12. 2026Targeted Adversarial Traffic Generation : Black-box Approach to Evade Intrusion Detection Systems in IoT Networkscited by 0
1 more
  1. 2026F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecastingcited by 0