Expressway traffic flow prediction based on MF-TAN and STSA.

ABSTRACT: Highly accurate traffic flow prediction is essential for effectively managing traffic congestion, providing real-time travel advice, and reducing travel costs. However, traditional traffic flow prediction models often fail to fully consider the correlation and periodicity among traffic state data and rely on static network topology graphs. To solve this problem, this paper proposes a expressway traffic flow prediction model based on multi-feature spatial-temporal adaptive periodic fused graph convolutional network (MFSTAPFGCN). First, we make fine preprocessing of the raw data to construct a complete and accurate dataset. Second, by deeply investigating the correlation properties among section speed, traffic flow, and section saturation rate, we incorporate these features into a multi-feat

SUBMITTER: Zhang X 

PROVIDER: S-EPMC10883582 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature