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2000
Volume 19, Issue 2
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

Abstract

Background

Traffic prediction is a key component of the intelligent transportation system for researchers and practitioners. It is extremely challenging because traffic flows typically exhibit complicated patterns, complex spatio-temporal correlations, and non-linearities.

Objective

Prediction of traffic density on the roads can help not only urban traffic management but also support for other road services such as path planning.

Methods

In this paper, we propose an attention-based graph convolutional network (AGCN) model to solve the traffic prediction problem. The primary focus of AGCN is on temporal, daily, and weekly dependencies of traffic periodicity. To efficiently capture the dynamic geographical and temporal correlations in traffic data, an attention-based spatial-temporal mechanism is employed. Additionally, standard convolutions are employed to extract temporal data and graph convolutional networks are used to capture spatial patterns.

Results

The final prediction results are generated by fusing the outputs of these components. California Transportation Agencies Performance Measurement System (CalTrans PeMS) dataset is used in this research to assess the performance. The proposed model has been validated using simulations that exhibit the viability of the method and show 4% increase in the accuracy of prediction.

Conclusion

For improved route planning and to arrive at the destination in the least amount of time, an efficient traffic pre- diction model is suggested. This enhances overall transportation system efficiency and aids in traffic control.

© 2026 Bentham Science Publishers
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Graph Convolutional Network and Attention for Traffic Prediction - A Deep Learning Approach
Recent Advances in Computer Science and Communications 19, E26662558351567 (2026); https://doi.org/10.2174/0126662558351567241029071333
/content/journals/rascs/10.2174/0126662558351567241029071333
/content/journals/rascs/10.2174/0126662558351567241029071333
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  • Article Type:     Research Article
Keyword(s): attention-based encoder-decoder; deep learning technique; graph convolutional network; Intelligent transportation system; spatiotemporal mechanism; traffic prediction

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