Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/180468
Title: Graph attention informer for long-term traffic flow prediction under the impact of sports events
Authors: Song, Yaofeng
Luo, Ruikang
Zhou, Tianchen
Zhou, Changgen
Su, Rong
Keywords: Engineering
Issue Date: 2024
Source: Song, Y., Luo, R., Zhou, T., Zhou, C. & Su, R. (2024). Graph attention informer for long-term traffic flow prediction under the impact of sports events. Sensors, 24(15), 4796-. https://dx.doi.org/10.3390/s24154796
Project: I1901E0046 
IAF-ICP 
Journal: Sensors
Abstract: Traffic flow prediction is one of the challenges in the development of an Intelligent Transportation System (ITS). Accurate traffic flow prediction helps to alleviate urban traffic congestion and improve urban traffic efficiency, which is crucial for promoting the synergistic development of smart transportation and smart cities. With the development of deep learning, many deep neural networks have been proposed to address this problem. However, due to the complexity of traffic maps and external factors, such as sports events, these models cannot perform well in long-term prediction. In order to enhance the accuracy and robustness of the model on long-term time series prediction, a Graph Attention Informer (GAT-Informer) structure is proposed by combining the graph attention layer and informer layer to capture the intrinsic features and external factors in spatial-temporal correlation. The external factors are represented as sports events impact factors. The GAT-Informer model was tested on real-world data collected in London, and the experimental results showed that our model has better performance in long-term traffic flow prediction compared to other baseline models.
URI: https://hdl.handle.net/10356/180468
ISSN: 1424-8220
DOI: 10.3390/s24154796
Schools: School of Electrical and Electronic Engineering 
Rights: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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