DOAJ Open Access 2024

Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach

Xudong Qi Junfeng Yao Ping Wang Tongtong Shi Yajie Zhang +1 lainnya

Abstrak

Abstract Accurate traffic flow forecasting is a critical component in intelligent transportation systems. However, most of the existing traffic flow prediction algorithms only consider the prediction under normal conditions, but not the influence of weather attributes on the prediction results. This study applies a hybrid deep learning model based on multi feature fusion to predict traffic flow considering weather conditions. A comparison with other representative models validates that the proposed spatial‐temporal fusion graph convolutional network (STFGCN) can achieve better performance.

Topik & Kata Kunci

Transportation engineering Electronic computers. Computer science

Penulis (6)

Xudong Qi

Junfeng Yao

Ping Wang

Tongtong Shi

Yajie Zhang

Xiangmo Zhao

Format Sitasi

APA MLA BibTeX

Qi, X., Yao, J., Wang, P., Shi, T., Zhang, Y., Zhao, X. (2024). Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach. https://doi.org/10.1049/itr2.12401

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Informasi Jurnal

Tahun Terbit: 2024
Sumber Database: DOAJ
DOI: 10.1049/itr2.12401
Akses: Open Access ✓