Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach
Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach
- New search for: Xudong Qi
- New search for: Junfeng Yao
- New search for: Ping Wang
- New search for: Tongtong Shi
- New search for: Yajie Zhang
- New search for: Xiangmo Zhao
- New search for: Xudong Qi
- New search for: Junfeng Yao
- New search for: Ping Wang
- New search for: Tongtong Shi
- New search for: Yajie Zhang
- New search for: Xiangmo Zhao
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.
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Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach
2024
Article (Journal)
Electronic Resource
Unknown
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