Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting

This research introduces integrated spatio-temporal graph convolutional networks (ISTGCN), designed to capture complex spatiotemporal traffic data patterns. The proposed model integrates multi-layer graph convolutional networks (GCNs) to address dependencies in temporal and spatial traffic dynamics....

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Bibliographic Details
Published in:Applied sciences 2024-12, Vol.14 (24), p.11477
Main Authors: Singh, Vandana, Sahana, Sudip Kumar, Bhattacharjee, Vandana
Format: Article
Language:English
Subjects:
Analysis
Deep learning
Forecasting
Geospatial data
graph convolutional neural network
Graph representations
Internet of Things
Land use
Machine learning
Neural networks
Public transportation
Roads & highways
Sensors
Smart cities
Social networks
Spatial data
spatio-temporal data
time series analysis
Traffic flow
traffic forecasting
Transportation planning
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Summary:This research introduces integrated spatio-temporal graph convolutional networks (ISTGCN), designed to capture complex spatiotemporal traffic data patterns. The proposed model integrates multi-layer graph convolutional networks (GCNs) to address dependencies in temporal and spatial traffic dynamics. Specifically, ISTGCN integrates graph convolutional layers and convolutional sequence learning layers within multiple spatiotemporal convolutional blocks. For capturing the temporal aspect, predictive graph modeling for road network traffic at particular time stamps is performed. To integrate the spatial information, graph convolution operations are applied. The proposed model was validated on real-life datasets, and the experimental results demonstrate that ISTGCN achieves significantly lower error values across key metrics—RMSE, MAE, and MAPE.
ISSN:2076-3417
2076-3417
DOI:10.3390/app142411477