Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting

• URL: http://arxiv.org/abs/2305.18687v2
• Date: Thu, 1 Jun 2023 15:35:00 GMT
• Title: Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting
• Authors: Zibo Liu, Parshin Shojaee, Chandan K Reddy
• Abstract summary: Long-range traffic forecasting remains a challenging task due to the intricate and extensive-temporal correlations observed in traffic networks. In this paper, we propose a architecture called Graph-based Multi-ODE Neural Networks (GRAM-ODE) which is designed with multiple connective ODE-GNN modules to learn better representations. Our extensive set of experiments conducted on six real-world datasets demonstrate the superior performance of GRAM-ODE compared with state-of-the-art baselines.
• Score: 8.832864937330722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is a recent surge in the development of spatio-temporal forecasting models in the transportation domain. Long-range traffic forecasting, however, remains a challenging task due to the intricate and extensive spatio-temporal correlations observed in traffic networks. Current works primarily rely on road networks with graph structures and learn representations using graph neural networks (GNNs), but this approach suffers from over-smoothing problem in deep architectures. To tackle this problem, recent methods introduced the combination of GNNs with residual connections or neural ordinary differential equations (ODE). However, current graph ODE models face two key limitations in feature extraction: (1) they lean towards global temporal patterns, overlooking local patterns that are important for unexpected events; and (2) they lack dynamic semantic edges in their architectural design. In this paper, we propose a novel architecture called Graph-based Multi-ODE Neural Networks (GRAM-ODE) which is designed with multiple connective ODE-GNN modules to learn better representations by capturing different views of complex local and global dynamic spatio-temporal dependencies. We also add some techniques like shared weights and divergence constraints into the intermediate layers of distinct ODE-GNN modules to further improve their communication towards the forecasting task. Our extensive set of experiments conducted on six real-world datasets demonstrate the superior performance of GRAM-ODE compared with state-of-the-art baselines as well as the contribution of different components to the overall performance. The code is available at https://github.com/zbliu98/GRAM-ODE

Related papers

• DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning [38.53424185696828]
  The representation learning of Discrete-Time Dynamic Graphs (DTDGs) has been extensively applied to model the dynamics of temporally changing entities and their evolving connections. This paper introduces a novel representation learning method DTFormer for DTDGs, pivoting from the traditional GNN+RNN framework to a Transformer-based architecture.
  arXiv  Detail & Related papers  (2024-07-26T05:46:23Z)
• Mending of Spatio-Temporal Dependencies in Block Adjacency Matrix [3.529869282529924]
  We propose a novel end-to-end learning architecture designed to mend the temporal dependencies, resulting in a well-connected graph. Our methodology demonstrates superior performance on benchmark datasets, such as SurgVisDom and C2D2.
  arXiv  Detail & Related papers  (2023-10-04T06:42:33Z)
• Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting [35.0288931087826]
  Traffic flow forecasting aims to predict future traffic conditions on the basis of networks and traffic conditions in the past. The problem is typically solved by modeling complex-temporal correlations in traffic data using far-temporal neural networks (GNNs) Existing methods follow the paradigm of message passing that aggregates neighborhood information linearly. In this paper, we propose a model named Dynamic Hyper Structure Learning (DyHSL) for traffic flow prediction.
  arXiv  Detail & Related papers  (2023-09-21T12:44:55Z)
• Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation [67.26422477327179]
  Temporal graphs exhibit dynamic interactions between nodes over continuous time. We propose a novel method of temporal graph convolution with the whole neighborhood. Our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
  arXiv  Detail & Related papers  (2023-04-15T08:17:18Z)
• Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction [6.968068088508505]
  We present a new sequence-to-sequence architecture for graph neural networks (GNNs) We also present a new benchmark benchmark dataset of street-level segment data in Montreal, Canada. Our model improves performance by more than 7% for one-hour prediction compared to the baseline methods.
  arXiv  Detail & Related papers  (2022-09-08T14:56:29Z)
• Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis [94.64007376939735]
  We theoretically characterize the impact of connectivity patterns on the convergence of deep neural networks (DNNs) under gradient descent training. We show that by a simple filtration on "unpromising" connectivity patterns, we can trim down the number of models to evaluate.
  arXiv  Detail & Related papers  (2022-05-11T17:43:54Z)
• Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [65.18780403244178]
  We propose a continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE) Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing.
  arXiv  Detail & Related papers  (2022-02-17T02:17:31Z)
• Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting [75.10017445699532]
  Recent have shifted their focus towards formulating traffic forecasting as atemporal graph modeling problem. We propose a novel approach for accurate traffic forecasting on road networks over multiple future time steps.
  arXiv  Detail & Related papers  (2021-11-25T08:45:14Z)
• 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 Inception Graph Convolutional Networks for Skeleton-Based Action Recognition [126.51241919472356]
  We design a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition. Our network is constructed by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths.
  arXiv  Detail & Related papers  (2020-11-26T14:43:04Z)
• 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.