Dynamic Causal Graph Convolutional Network for Traffic Prediction

Junpeng Lin, Ziyue Li, Zhishuai Li, Lei Bai, Rui Zhao +1 more
6/12/2023
cs.LGeess.SP

Official code: MonBG/DCGCN (16 stars, last commit 2023-09-06).

Abstract

Modeling complex spatiotemporal dependencies in correlated traffic series is essential for traffic prediction. While recent works have shown improved prediction performance by using neural networks to extract spatiotemporal correlations, their effectiveness depends on the quality of the graph structures used to represent the spatial topology of the traffic network. In this work, we propose a novel approach for traffic prediction that embeds time-varying dynamic Bayesian network to capture the fine spatiotemporal topology of traffic data. We then use graph convolutional networks to generate traffic forecasts. To enable our method to efficiently model nonlinear traffic propagation patterns, we develop a deep learning-based module as a hyper-network to generate stepwise dynamic causal graphs. Our experimental results on a real traffic dataset demonstrate the superior prediction performance of the proposed method. The code is available at https://github.com/MonBG/DCGCN.

View on arXivView PDF

Code Implementations(1)

MonBG/DCGCNOfficial100%
160PythonSep 5, 20233 years ago

Similar Papers

Causally-Aware Spatio-Temporal Multi-Graph Convolution Network for Accurate and Reliable Traffic Prediction

92% match1 repo22024

Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting

89% match2023

Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction

89% match1 repo222023

STEI-PCN: an efficient pure convolutional network for traffic prediction via spatial-temporal encoding and inferring

88% match2025

Spatial-Temporal Hypergraph Neural Network for Traffic Forecasting

88% match2023

STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction

87% match1 repo862023

Cite this paper

@article{lin2023dynamic,
  title  = {Dynamic Causal Graph Convolutional Network for Traffic Prediction},
  author = {Junpeng Lin and Ziyue Li and Zhishuai Li and Lei Bai and Rui Zhao and Chen Zhang},
  year   = {2023},
  eprint = {2306.07019},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.07019v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2306.07019.svg)](https://codeofpaper.com/paper/2306.07019)

Discussion