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12 November 2024 Improving traffic speed prediction by embedding road dependence in the learning matrix of CNN
Haitao Wei, Jiali Zhang, Xiaojing Yao, Yanli Lv
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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 1339517 (2024) https://doi.org/10.1117/12.3048773
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
Due to the lack of sufficient exploration of global spatial-temporal characteristics of traffic dynamics, large-scale and longterm vehicle speed prediction problems have not been well solved. To this end, this study presents a 3-dimensional road matrix based deep learning model (3DM-DLM) to embed global similarities of road segments in constructing learning matrix. The global similarity is measured by sampling the correlation of vehicle speed, and the correlation aggregation of adjacent road segments in the matrix is realized by clustering and Z-order curve. The learning matrix is then used to train a deep neural network composed by convolution layers and residual units. We collected traffic speed data in Beijing to validate the 3DM-DLM. The results showed that compared with the baseline model, the prediction accuracy of the proposed model is improved by 8.05 % in the acceptable time, and it also proved the generalization ability of 3DM-DLM in specific cases.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Haitao Wei, Jiali Zhang, Xiaojing Yao, and Yanli Lv "Improving traffic speed prediction by embedding road dependence in the learning matrix of CNN", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 1339517 (12 November 2024); https://doi.org/10.1117/12.3048773
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KEYWORDS
Roads
Matrices
Education and training
Deep learning
Data modeling
Machine learning
Rain
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