mlsanityData to 7 Oct 2026

Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks

arXiv 2208.05908 ↗First posted 11 Aug 2022cs.LG stat.MLcited by 29

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Abstract (arXiv)

Origin-Destination (O-D) travel demand prediction is a fundamental challenge in transportation. Recently, spatial-temporal deep learning models demonstrate the tremendous potential to enhance prediction accuracy. However, few studies tackled the uncertainty and sparsity issues in fine-grained O-D matrices. This presents a serious problem, because a vast number of zeros deviate from the Gaussian assumption underlying the deterministic deep learning models. To address this issue, we design a Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) to quantify the uncertainty of the sparse travel demand. It analyzes spatial and temporal correlations using diffusion and temporal convolution networks, which are then fused to parameterize the probabilistic distributions of travel demand. The STZINB-GNN is examined using two real-world datasets with various spatial and temporal resolutions. The results demonstrate the superiority of STZINB-GNN over benchmark models, especially under high spatial-temporal resolutions, because of its high accuracy, tight confidence intervals, and interpretable parameters. The sparsity parameter of the STZINB-GNN has physical interpretation for various transportation applications.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 21 of its 47 references that are in the map. First posted on arXiv 11 Aug 2022.

Builds on

21 of 47 references.

References · 21

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  3. 2016Variational Graph Auto-Encoderscited by 1,373
  4. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
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  6. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 504
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  9. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
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9 more
  1. 2019Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Predictioncited by 34
  2. 2018Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problemscited by 32
  3. 2021Spatial Aggregation and Temporal Convolution Networks for Real-time Krigingcited by 27
  4. 2019Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional networkcited by 23
  5. 2021Low-Rank Hankel Tensor Completion for Traffic Speed Estimationcited by 22
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 10; 2024: 5; 2025: 9; 2026: 4.

29 papers.

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  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2024Uncertainty in Graph Neural Networks: A Surveycited by 20
  3. 2023Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Predictioncited by 19
  4. 2023Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysiscited by 13
  5. 2023Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networkscited by 12
  6. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  7. 2023Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?cited by 8
  8. 2023Deep trip generation with graph neural networks for bike sharing system expansioncited by 8
  9. 2023ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networkscited by 8
  10. 2023Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Predictioncited by 8
  11. 2025UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Predictioncited by 8
  12. 2026HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Predictioncited by 8
17 more
  1. 2025Uncertainty-aware Predict-Then-Optimize Framework for Equitable Post-Disaster Power Restorationcited by 7
  2. 2024Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networkscited by 5
  3. 2024SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networkscited by 4
  4. 2023XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatilitycited by 3
  5. 2024Conditional Prediction ROC Bands for Graph Classificationcited by 3
  6. 2025Topology-Aware Conformal Prediction for Stream Networkscited by 3
  7. 2022Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Networkcited by 2
  8. 2025Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Studycited by 2
  9. 2025Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volumecited by 2
  10. 2026EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Predictioncited by 1
  11. 2023Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systemscited by 0
  12. 2025Spatio-temporal Prediction of Fine-Grained Origin-Destination Matrices with Applications in Ridesharingcited by 0
  13. 2025MSRFormer: Road Network Representation Learning using Multi-scale Feature Fusion of Heterogeneous Spatial Interactionscited by 0
  14. 2025Learning Multimodal Embeddings for Traffic Accident Prediction and Causal Estimationcited by 0
  15. 2025Harnessing Rich Multi-Modal Data for Spatial-Temporal Homophily-Embedded Graph Learning Across Domains and Localitiescited by 0
  16. 2026Vessel Traffic Flow Prediction on Sparse Data via Spatio-Temporal Graph Neural Networks with a Learnable Tweedie Headcited by 0
  17. 2026Inferring Urban Mobility Interactions from Aggregated Dynamicscited by 0