Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction

• URL: http://arxiv.org/abs/2305.00985v1
• Date: Mon, 1 May 2023 00:58:48 GMT
• Title: Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction
• Authors: Weiheng Zhong, Hadi Meidani, Jane Macfarlane
• Abstract summary: We propose attention-based graph neural ODE (AST) that explicitly learns the dynamics of the traffic system. Our model aggregates traffic patterns of different periods and has satisfactory performance on two real-world traffic data sets.
• Score: 3.4806267677524896
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Traffic forecasting is an important issue in intelligent traffic systems (ITS). Graph neural networks (GNNs) are effective deep learning models to capture the complex spatio-temporal dependency of traffic data, achieving ideal prediction performance. In this paper, we propose attention-based graph neural ODE (ASTGODE) that explicitly learns the dynamics of the traffic system, which makes the prediction of our machine learning model more explainable. Our model aggregates traffic patterns of different periods and has satisfactory performance on two real-world traffic data sets. The results show that our model achieves the highest accuracy of the root mean square error metric among all the existing GNN models in our experiments.

Related papers

• A Multi-Graph Convolutional Neural Network Model for Short-Term Prediction of Turning Movements at Signalized Intersections [0.6215404942415159]
  This study introduces a novel deep learning architecture, referred to as the multigraph convolution neural network (MGCNN) for turning movement prediction at intersections. The proposed architecture combines a multigraph structure, built to model temporal variations in traffic data, with a spectral convolution operation to support modeling the spatial variations in traffic data over the graphs. The model's ability to perform short-term predictions over 1, 2, 3, 4, and 5 minutes into the future was evaluated against four baseline state-of-the-art models.
  arXiv  Detail & Related papers  (2024-06-02T05:41:25Z)
• Gradient Transformation: Towards Efficient and Model-Agnostic Unlearning for Dynamic Graph Neural Networks [66.70786325911124]
  Graph unlearning has emerged as an essential tool for safeguarding user privacy and mitigating the negative impacts of undesirable data. With the increasing prevalence of DGNNs, it becomes imperative to investigate the implementation of dynamic graph unlearning. We propose an effective, efficient, model-agnostic, and post-processing method to implement DGNN unlearning.
  arXiv  Detail & Related papers  (2024-05-23T10:26:18Z)
• 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)
• STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction [9.467593700532401]
  This paper provides a systematic review of graph learning strategies and commonly used graph convolution algorithms. We then conduct a comprehensive analysis of the strengths and weaknesses of recently proposed spatial-temporal graph network models. We build a study called STG4Traffic using the deep learning framework PyTorch to establish a standardized and scalable benchmark on two types of traffic datasets.
  arXiv  Detail & Related papers  (2023-07-02T06:56:52Z)
• Dynamic Causal Graph Convolutional Network for Traffic Prediction [19.759695727682935]
  We propose an approach for predicting traffic that embeds time-varying dynamic network to capture finetemporal patterns of traffic data. We then use graph convolutional networks to generate traffic forecasts. Our experimental results on a real traffic dataset demonstrate the superior prediction performance of the proposed method.
  arXiv  Detail & Related papers  (2023-06-12T10:46:31Z)
• Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting [60.03169701753824]
  We propose a novel Dynamic Diffusion-al Graph Neural Network (DVGNN) fortemporal forecasting. The proposed DVGNN model outperforms state-of-the-art approaches and achieves outstanding Root Mean Squared Error result.
  arXiv  Detail & Related papers  (2023-05-16T11:38:19Z)
• 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)
• A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism [0.0]
  Short-term traffic flow prediction is a vital branch of the Intelligent Traffic System (ITS) and plays an important role in traffic management. Graph convolution network (GCN) is widely used in traffic prediction models to better deal with the graphical structure data of road networks. To deal with this drawback, this paper proposes a novel location graph convolutional network (Location-GCN)
  arXiv  Detail & Related papers  (2022-05-10T09:19:12Z)
• Predicting traffic signals on transportation networks using spatio-temporal correlations on graphs [56.48498624951417]
  This paper proposes a traffic propagation model that merges multiple heat diffusion kernels into a data-driven prediction model to forecast traffic signals. We optimize the model parameters using Bayesian inference to minimize the prediction errors and, consequently, determine the mixing ratio of the two approaches. The proposed model demonstrates prediction accuracy comparable to that of the state-of-the-art deep neural networks with lower computational effort.
  arXiv  Detail & Related papers  (2021-04-27T18:17:42Z)
• Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs [77.33781731432163]
  We learn dynamic graph representation in hyperbolic space, for the first time, which aims to infer node representations. We present a novel Hyperbolic Variational Graph Network, referred to as HVGNN. In particular, to model the dynamics, we introduce a Temporal GNN (TGNN) based on a theoretically grounded time encoding approach.
  arXiv  Detail & Related papers  (2021-04-06T01:44:15Z)
• 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)

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.