mlsanityData to 7 Oct 2026

Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction

arXiv 2306.09882 ↗First posted 16 Jun 2023cs.LG stat.ML stat.OTcited by 19

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Abstract (arXiv)

Understanding Origin-Destination (O-D) travel demand is crucial for transportation management. However, traditional spatial-temporal deep learning models grapple with addressing the sparse and long-tail characteristics in high-resolution O-D matrices and quantifying prediction uncertainty. This dilemma arises from the numerous zeros and over-dispersed demand patterns within these matrices, which challenge the Gaussian assumption inherent to deterministic deep learning models. To address these challenges, we propose a novel approach: the Spatial-Temporal Tweedie Graph Neural Network (STTD). The STTD introduces the Tweedie distribution as a compelling alternative to the traditional 'zero-inflated' model and leverages spatial and temporal embeddings to parameterize travel demand distributions. Our evaluations using real-world datasets highlight STTD's superiority in providing accurate predictions and precise confidence intervals, particularly in high-resolution scenarios.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 14 of its 36 references that are in the map. First posted on arXiv 16 Jun 2023.

Builds on

14 of 36 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  3. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  4. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
  5. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
  6. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  7. 2020Inductive Graph Neural Networks for Spatiotemporal Krigingcited by 90
  8. 2019Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Predictioncited by 34
  9. 2022Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networkscited by 29
  10. 2019Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional networkcited by 23
  11. 2021Low-Rank Hankel Tensor Completion for Traffic Speed Estimationcited by 22
  12. 2019Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filtercited by 13
2 more
  1. 2023Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networkscited by 12
  2. 2021CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecastingcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 9; 2025: 5; 2026: 3.

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  1. 2023Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphscited by 39
  2. 2024TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systemscited by 28
  3. 2024Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspectivecited by 24
  4. 2024Utilize the Flow before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuningcited by 10
  5. 2025Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Reviewcited by 10
  6. 2025GCoT: Chain-of-Thought Prompt Learning for Graphscited by 9
  7. 2023Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Predictioncited by 8
  8. 2024CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networkscited by 7
  9. 2024Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networkscited by 5
  10. 2024SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networkscited by 4
  11. 2024Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregationcited by 2
  12. 2025Alleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated Distributioncited by 2
7 more
  1. 2024Causally-Aware Spatio-Temporal Multi-Graph Convolution Network for Accurate and Reliable Traffic Predictioncited by 1
  2. 2024Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Networkcited by 0
  3. 2025CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guaranteecited by 0
  4. 2025Rethinking Graph Domain Adaptation: A Spectral Contrastive Perspectivecited by 0
  5. 2026Vessel Traffic Flow Prediction on Sparse Data via Spatio-Temporal Graph Neural Networks with a Learnable Tweedie Headcited by 0
  6. 2026Structure-Aware Graph Multi-Task Learning for Dynamic Sparse OD Demand Predictioncited by 0
  7. 2026Inferring Urban Mobility Interactions from Aggregated Dynamicscited by 0