mlsanityData to 7 Oct 2026

STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

arXiv 1905.10069 ↗First posted 24 May 2019cs.LG cs.AI stat.MLcited by 59

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Abstract (arXiv)

Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a short-term encoder to derive the next-step prediction for generating multi-step prediction; 3) an attention-based output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2019.

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Citing papers per year: 2021: 14; 2022: 11; 2023: 14; 2024: 16; 2025: 2; 2026: 2.

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  1. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  2. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  3. 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
  4. 2024UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Predictioncited by 86
  5. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  6. 2022Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Versioncited by 59
  7. 2022Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfercited by 43
  8. 2022GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networkscited by 43
  9. 2022Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecastingcited by 36
  10. 2024Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlookcited by 33
  11. 2023MLPST: MLP is All You Need for Spatio-Temporal Predictioncited by 22
  12. 2023LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Predictioncited by 19
38 more
  1. 2023Building Transportation Foundation Model via Generative Graph Transformercited by 19
  2. 2021RNN with Particle Flow for Probabilistic Spatio-temporal Forecastingcited by 16
  3. 2023Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunitiescited by 16
  4. 2022Statistical Deep Learning for Spatial and Spatio-Temporal Datacited by 15
  5. 2023Graph Neural Rough Differential Equations for Traffic Forecastingcited by 14
  6. 2023Localised Adaptive Spatial-Temporal Graph Neural Networkcited by 14
  7. 2023Dynamic Hypergraph Structure Learning for Traffic Flow Forecastingcited by 14
  8. 2021STUaNet: Understanding uncertainty in spatiotemporal collective human mobilitycited by 11
  9. 2021Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregationcited by 11
  10. 2021SSMF: Shifting Seasonal Matrix Factorizationcited by 11
  11. 2022Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecastingcited by 11
  12. 2024STD-PLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal Data with PLMcited by 9
  13. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  14. 2023Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecastingcited by 8
  15. 2024Dynamic Multi-Network Mining of Tensor Time Seriescited by 8
  16. 2023Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecastingcited by 7
  17. 2021A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemescited by 6
  18. 2021Adversarial Diffusion Attacks on Graph-based Traffic Prediction Modelscited by 5
  19. 2021An Empirical Experiment on Deep Learning Models for Predicting Traffic Datacited by 4
  20. 2024Loss Shaping Constraints for Long-Term Time Series Forecastingcited by 4
  21. 2024Navigating Spatio-Temporal Heterogeneity: A Graph Transformer Approach for Traffic Forecastingcited by 3
  22. 2021DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Predictioncited by 2
  23. 2022Network Level Spatial Temporal Traffic State Forecasting with Hierarchical-Attention-LSTM (HierAttnLSTM)cited by 2
  24. 2022On the importance of stationarity, strong baselines and benchmarks in transport prediction problemscited by 2
  25. 2023Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspectivecited by 2
  26. 2023Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]cited by 2
  27. 2024ODMixer: Fine-grained Spatial-temporal MLP for Metro Origin-Destination Predictioncited by 2
  28. 2024Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networkscited by 2
  29. 2024Leveraging Intra-Period and Inter-Period Features for Enhanced Passenger Flow Prediction of Subway Stationscited by 2
  30. 2025Dynamic Localisation of Spatial-Temporal Graph Neural Networkcited by 2
  31. 2021ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecastingcited by 1
  32. 2021Networked Time Series Prediction with Incomplete Data via Generative Adversarial Networkcited by 1
  33. 2022Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamicscited by 1
  34. 2023Uncertainty Quantification for Image-based Traffic Prediction across Citiescited by 1
  35. 2024SSMT: Few-Shot Traffic Forecasting with Single Source Meta-Transfercited by 1
  36. 2026Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Predictioncited by 1
  37. 2021Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecastingcited by 0
  38. 2022Unsupervised Knowledge Adaptation for Passenger Demand Forecastingcited by 0

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