Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach
- Resource Type
- article
- Authors
- Xudong Qi; Junfeng Yao; Ping Wang; Tongtong Shi; Yajie Zhang; Xiangmo Zhao
- Source
- IET Intelligent Transport Systems, Vol 18, Iss 3, Pp 528-539 (2024)
- Subject
- management and control
traffic and demand managing
traffic information systems
traffic modelling
Transportation engineering
TA1001-1280
Electronic computers. Computer science
QA75.5-76.95
- Language
- English
- ISSN
- 1751-9578
1751-956X
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.