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2023 | OriginalPaper | Chapter

APADGCN: Adaptive Partial Attention Diffusion Graph Convolutional Network for Traffic Flow Forecasting

Authors : Bowen Zhang, Bohan Li, Jinzhan Wei, Hao Wen

Published in: Spatial Data and Intelligence

Publisher: Springer Nature Switzerland

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Abstract

Traffic flow forecasting is a core task of urban governance and plays a vital role in the development of ITS. Because of the complexity and uncertainty of traffic patterns, it is of great challenge to capture spatial-temporal correlations. Recent researches mainly focus on the pre-defined adjacency matrix based on prior knowledge as the basis of spatial-temporal correlation modeling, but the fixed graph structure cannot adequately describe the dependency between traffic sensors. To tackle this issue, a novel deep learning model framework is proposed in this paper: Adaptive Partial Attention Diffusion Graph Convolutional Network(APADGCN), which consists of three main parts: 1) the Multi-Component module that divides the historical traffic flow into recent, daily-periodic, and weekly-periodic, to capture the traffic patterns of different periodic; 2) the spatial correlation modeling which can dynamically capture node relationships and model spatial dependency, and enhance the aggregation ability of low-order information; 3) the temporal correlation modeling which models long-term time dependencies using convolution and gating. The final result is obtained by the weighted fusion of the results of the multi-components. We compared our APADGCN with various baseline models in the four real datasets from the Caltrans Performance Measurement System (PeMS). The experimental results show that the prediction accuracy of APADGCN is better than that of the baseline models.

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Metadata
Title
APADGCN: Adaptive Partial Attention Diffusion Graph Convolutional Network for Traffic Flow Forecasting
Authors
Bowen Zhang
Bohan Li
Jinzhan Wei
Hao Wen
Copyright Year
2023
Book
Spatial Data and Intelligence

Print ISBN: 978-3-031-32909-8

Electronic ISBN: 978-3-031-32910-4

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-32910-4_1

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