Node Connection Strength Matrix-Based Graph Convolution Network for Traffic Flow Prediction

Document Type

Article

Publication Title

IEEE Transactions on Vehicular Technology

Abstract

Traffic flow prediction plays an integral role in intelligent transport systems, helping to manage and control urban traffic and improving the operational efficiency of road networks. Although the current mainstream traffic flow prediction models have achieved good accuracy, they cannot effectively utilize the unique characteristics of the traffic network where the importance of a node in the traffic network is positively correlated with the traffic flow through the node. Actually, the historical traffic properties of nodes will have a great influence on the future. With this background, in this paper, we propose a node connection strength index by network representation learning to utilize the historical traffic attributes of nodes. Then, we design a graph convolution network based on the node connection strength matrix to predict the traffic flow of the node. A novel Dynamics Extractor is designed to learn the various characteristics of the traffic flow. Experimental results demonstrate that the proposed scheme has a better performance by comparison with baseline methods.

First Page

1

Last Page

13

DOI

10.1109/TVT.2023.3265300

Publication Date

4-6-2023

Keywords

Convolution, Deep learning, Feature extraction, Graph Convolution Network, Indexes, Network Representation Learning, Node Connection Strength, Predictive models, Representation learning, Task analysis, Traffic Flow Prediction

Comments

IR Deposit conditions:

OA version (pathway a) Accepted version

No embargo

When accepted for publication, set statement to accompany deposit (see policy)

Must link to publisher version with DOI

Publisher copyright and source must be acknowledged

Link to Full Text

Share

COinS