Spatial-temporal clustering enhanced multi-graph convolutional network for traffic flow prediction

Dynamics and uncertainty are the fundamental reasons for the difficulty in accurately predicting traffic flow. In recent years, graph convolutional networks have been widely used in traffic flow prediction because of their excellent dynamic feature mapping ability. However, the existing models usual...

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Vydané v:Applied intelligence (Dordrecht, Netherlands) Ročník 55; číslo 7; s. 445
Hlavní autori: Bao, Yinxin, Shen, Qinqin, Cao, Yang, Shi, Quan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.05.2025
Springer Nature B.V
Predmet:
Ablation
Algorithms
Artificial Intelligence
Artificial neural networks
Clustering
Computer Science
Convolution
Feature extraction
Flow mapping
Machines
Manufacturing
Mechanical Engineering
Modules
Nodes
Predictions
Processes
Source code
Traffic flow
External factors
Traffic prediction
Graph convolution
Spatial-temporal correlation
Clustering algorithm
ISSN:0924-669X, 1573-7497
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Popis
Shrnutí:Dynamics and uncertainty are the fundamental reasons for the difficulty in accurately predicting traffic flow. In recent years, graph convolutional networks have been widely used in traffic flow prediction because of their excellent dynamic feature mapping ability. However, the existing models usually overlook the correlations among the nodes and the complex impact of external factors on traffic flow, which make it challenging to explore the complex spatial-temporal features. To overcome these shortcomings, we propose a novel Spatial-temporal Clustering enhanced Multi-Graph Convolutional Network (SCM-GCN) for traffic flow prediction. First, a Spatial-Temporal Clustering (STS) module based on the improved adjacency matrix DBSCAN clustering algorithm is constructed, this module divides traffic nodes into multiple highly correlated clusters, each of which consists of multi-graph features and time-varying features. Then, a Multi-Graph Spatial Feature Extraction (MGSFE) module that integrates the graph convolution operation and attention mechanism is designed to extract dynamic spatial features of multi-graph and time-varying features. Next, the Time-Varying Feature Extraction (TVFE) module based on the dilated convolution and gated attention mechanism is constructed. It integrates the output of the MGSFE module to extract dynamic temporal features of time-varying features and output the predicted values. Finally, the comparison and ablation experiments on four datasets show that the proposed model performs better than state-of-the-art models. The key source code and data are available at https://github.com/Bounger2/SCMGCN .
Bibliografia:ObjectType-Article-1
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-025-06329-0