Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graph

• URL: http://arxiv.org/abs/2501.08653v2
• Date: Sun, 19 Jan 2025 08:45:34 GMT
• Title: Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graph
• Authors: Wang-Tao Zhou, Zhao Kang, Sicong Liu, Lizong Zhang, Ling Tian,
• Abstract summary: We propose a novel Graph Spatio-Temporal Point ( GSTPP) model for fine-grained event prediction. It adopts an encoder-coder architecture that jointly models the state dynamics of spatially localized regions. The proposed GSTPP model greatly improves the accuracy of fine-grained event prediction.
• Score: 8.435985634889285
• License:
• Abstract: Event prediction tasks often handle spatio-temporal data distributed in a large spatial area. Different regions in the area exhibit different characteristics while having latent correlations. This spatial heterogeneity and correlations greatly affect the spatio-temporal distributions of event occurrences, which has not been addressed by state-of-the-art models. Learning spatial dependencies of events in a continuous space is challenging due to its fine granularity and a lack of prior knowledge. In this work, we propose a novel Graph Spatio-Temporal Point Process (GSTPP) model for fine-grained event prediction. It adopts an encoder-decoder architecture that jointly models the state dynamics of spatially localized regions using neural Ordinary Differential Equations (ODEs). The state evolution is built on the foundation of a novel Self-Adaptive Anchor Graph (SAAG) that captures spatial dependencies. By adaptively localizing the anchor nodes in the space and jointly constructing the correlation edges between them, the SAAG enhances the model's ability of learning complex spatial event patterns. The proposed GSTPP model greatly improves the accuracy of fine-grained event prediction. Extensive experimental results show that our method greatly improves the prediction accuracy over existing spatio-temporal event prediction approaches.

Related papers

• Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting [16.782154479264126]
  Predicting backbone-temporal traffic flow presents challenges due to complex interactions between temporal factors. Existing approaches address these dimensions in isolation, neglecting their critical interdependencies. In this paper, we introduce Sanonymous-Temporal Unitized Unitized Cell (ASTUC), a unified framework designed to capture both spatial and temporal dependencies.
  arXiv  Detail & Related papers  (2024-11-14T07:34:31Z)
• DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs [59.434893231950205]
  Dynamic graph learning aims to uncover evolutionary laws in real-world systems. We propose DyG-Mamba, a new continuous state space model for dynamic graph learning. We show that DyG-Mamba achieves state-of-the-art performance on most datasets.
  arXiv  Detail & Related papers  (2024-08-13T15:21:46Z)
• Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction [16.530361912832763]
  We propose a temporal graph neural point process framework, named STNPP, for traffic congestion event prediction. Our method achieves superior performance in comparison to existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-11-15T01:22:47Z)
• PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction [33.08230699138568]
  We introduce a neural network named PArallel Spatio with spatial auto-correlation gating. The components in our approach include spatial auto-correlation gating, multi-scale residual block, and temporal attention gating module.
  arXiv  Detail & Related papers  (2023-10-02T14:10:42Z)
• SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data [55.609946936979036]
  We propose a novel spatial event ranking approach named SpatialRank. We show that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents.
  arXiv  Detail & Related papers  (2023-09-30T06:20:21Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)
• Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility [5.864710987890994]
  Deep GA-vLS assumes data to be of fixed and regular shaped tensor shaped and face challenges of handling irregular data. We present a novel geo-aware enabled learning operation based on a novel data structure for dependencies while maintaining the recurrent mechanism.
  arXiv  Detail & Related papers  (2022-11-27T16:51:28Z)
• CEP3: Community Event Prediction with Neural Point Process on Graph [59.434777403325604]
  We propose a novel model combining Graph Neural Networks and Marked Temporal Point Process (MTPP) Our experiments demonstrate the superior performance of our model in terms of both model accuracy and training efficiency.
  arXiv  Detail & Related papers  (2022-05-21T15:30:25Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction [5.346782918364054]
  We propose a novel CNN-based spatial-temporal graph framework GraphCNT to support more efficient and accurate trajectory predictions. In contrast to conventional models, both the spatial and temporal modeling of our model are computed within each local time window. Our model achieves better performance in terms of both efficiency and accuracy as compared with state-of-the-art models on various trajectory prediction benchmark datasets.
  arXiv  Detail & Related papers  (2020-03-16T12:56:12Z)
• A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction [74.00750936752418]
  We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC) First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
  arXiv  Detail & Related papers  (2020-03-13T04:35:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.