Traffic State Prediction based on Spatio-Temporal Graph Transformer Network
Pages 222 - 227
Abstract
The task of predicting spatio-temporal traffic states holds paramount importance in traffic management. However, the dynamic conditions on roads are influenced not only temporally, following sequential patterns, but also spatially by other road segments. Despite the development of several models to forecast traffic states, many struggle to effectively capture spatio-temporal correlations. This paper introduces the Spatio-Temporal Graph Transformer Network (STGTN), which aims to comprehensively model spatio-temporal information in road networks, thereby improving traffic prediction accuracy. Specifically, we propose a spatio-temporal attention module with learnable positional embeddings designed to capture dynamic spatio-temporal correlations. Subsequently, a fully connected layer is utilized for feature projection and output of predicted traffic states. Experiments on real traffic datasets highlight the superior performance of our proposed model compared to existing techniques.
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Published In
December 2023
371 pages
ISBN:9798400709203
DOI:10.1145/3639631
Copyright © 2023 ACM.
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Published: 16 February 2024
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- Transportation Science and Technology Project of Sichuan Province
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ACAI 2023
ACAI 2023: 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence
December 22 - 24, 2023
Sanya, China
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Overall Acceptance Rate 173 of 395 submissions, 44%
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