A Deep Learning Approach for Long-Term Traffic Flow Prediction With Multifactor Fusion Using Spatiotemporal Graph Convolutional Network

As a vital research subject in the field of intelligent transportation systems (ITSs), traffic flow prediction using deep learning methods has attracted much attention in recent years. However, numerous existing studies mainly focus on short-term traffic flow predictions and fail to consider the inf...

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Veröffentlicht in:IEEE transactions on intelligent transportation systems Jg. 24; H. 8; S. 1 - 14
Hauptverfasser: Qi, Xiaoyu, Mei, Gang, Tu, Jingzhi, Xi, Ning, Piccialli, Francesco
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Artificial neural networks
Atmospheric models
Convolution
Data models
Deep learning
external factor
Flow stability
Forecasting
Intelligent transportation systems
Intelligent transportation systems (ITS)
Logic gates
long-term traffic flow prediction
Predictive models
spatiotemporal graph convolutional network
Spatiotemporal phenomena
Traffic flow
Transportation networks
Weather
ISSN:1524-9050, 1558-0016
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Zusammenfassung:As a vital research subject in the field of intelligent transportation systems (ITSs), traffic flow prediction using deep learning methods has attracted much attention in recent years. However, numerous existing studies mainly focus on short-term traffic flow predictions and fail to consider the influence of external factors. Effective long-term traffic flow prediction has become a challenging issue. As a solution to these challenges, this paper proposes a deep learning approach based on a spatiotemporal graph convolutional network for long-term traffic flow prediction with multiple factors. In the proposed method, our innovative idea is to introduce an attribute feature unit (AF-unit) to fuse external factors into a spatiotemporal graph convolutional network. The proposed method consists of (1) constructing a weighted adjacency matrix using Gaussian similarity functions; (2) assembling a feature matrix to store time-series traffic flow; (3) building an external attribute matrix composed of external factors, including temperature, visibility, and weather conditions; and (4) building a spatiotemporal graph convolutional network based on a deep learning architecture (i.e., T-GCN). The experimental results indicate that (1) the performance of our method considering spatiotemporal dependence has better prediction capability than baseline models; (2) the fusion of meteorological factors can reduce the inaccuracy of traffic prediction; and (3) our method has high accuracy and stability in long-term traffic flow prediction.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3201879