STGIN: A Spatial Temporal Graph-Informer Network for Long Sequence Traffic Speed Forecasting

• URL: http://arxiv.org/abs/2210.01799v1
• Date: Sat, 1 Oct 2022 05:58:22 GMT
• Title: STGIN: A Spatial Temporal Graph-Informer Network for Long Sequence Traffic Speed Forecasting
• Authors: Ruikang Luo, Yaofeng Song, Liping Huang, Yicheng Zhang and Rong Su
• Abstract summary: This study proposes a new spatial-temporal neural network architecture to handle the long-term traffic parameters forecasting issue. The attention mechanism potentially guarantees long-term prediction performance without significant information loss from distant inputs.
• Score: 8.596556653895028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate long series forecasting of traffic information is critical for the development of intelligent traffic systems. We may benefit from the rapid growth of neural network analysis technology to better understand the underlying functioning patterns of traffic networks as a result of this progress. Due to the fact that traffic data and facility utilization circumstances are sequentially dependent on past and present situations, several related neural network techniques based on temporal dependency extraction models have been developed to solve the problem. The complicated topological road structure, on the other hand, amplifies the effect of spatial interdependence, which cannot be captured by pure temporal extraction approaches. Additionally, the typical Deep Recurrent Neural Network (RNN) topology has a constraint on global information extraction, which is required for comprehensive long-term prediction. This study proposes a new spatial-temporal neural network architecture, called Spatial-Temporal Graph-Informer (STGIN), to handle the long-term traffic parameters forecasting issue by merging the Informer and Graph Attention Network (GAT) layers for spatial and temporal relationships extraction. The attention mechanism potentially guarantees long-term prediction performance without significant information loss from distant inputs. On two real-world traffic datasets with varying horizons, experimental findings validate the long sequence prediction abilities, and further interpretation is provided.

Related papers

• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• Graph Pruning Based Spatial and Temporal Graph Convolutional Network with Transfer Learning for Traffic Prediction [0.0]
  This study proposes a novel Spatial-temporal Convolutional Network (TL-GPSTGN) based on graph pruning and transfer learning framework. The results demonstrate the exceptional predictive accuracy of TL-GPSTGN on a single dataset, as well as its robust migration performance across different datasets.
  arXiv  Detail & Related papers  (2024-09-25T00:59:23Z)
• A novel hybrid time-varying graph neural network for traffic flow forecasting [3.6623539239888556]
  Real-time and precise traffic flow prediction is vital for the efficiency of intelligent transportation systems. Traditional graph neural networks (GNNs) are used to describe spatial correlations among traffic nodes in urban road networks. We have proposed a novel hybrid time-varying graph neural network (HTVGNN) for traffic flow prediction.
  arXiv  Detail & Related papers  (2024-01-17T07:21:36Z)
• 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)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• Scalable Spatiotemporal Graph Neural Networks [14.415967477487692]
  Graph neural networks (GNNs) are often the core component of the forecasting architecture. In most pretemporal GNNs, the computational complexity scales up to a quadratic factor with the length of the sequence times the number of links in the graph. We propose a scalable architecture that exploits an efficient encoding of both temporal and spatial dynamics.
  arXiv  Detail & Related papers  (2022-09-14T09:47:38Z)
• SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network [2.524966118517392]
  We have designed a simplified S-temporal GNN(SST-GNN) that effectively encodes the dependency by separately aggregating different neighborhood. We have shown that our model has significantly outperformed the state-of-the-art models on three real-world traffic datasets.
  arXiv  Detail & Related papers  (2021-03-31T18:28:44Z)
• Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks [110.80088437391379]
  A graph-based framework called SMART is proposed to model and keep track of the statistics of vehicle-to-temporal (V2I) communication latency across a large geographical area. We develop a graph reconstruction-based approach using a graph convolutional network integrated with a deep Q-networks algorithm. Our results show that the proposed method can significantly improve both the accuracy and efficiency for modeling and the latency performance of large vehicular networks.
  arXiv  Detail & Related papers  (2021-03-13T06:56:29Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

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.