  Accurate traffic Flow Prediction can assist in traffic management, route
planning, and congestion mitigation, which holds significant importance in
enhancing the efficiency and reliability of intelligent transportation systems
(ITS). However, existing traffic flow prediction models suffer from limitations
in capturing the complex spatial-temporal dependencies within traffic networks.
In order to address this issue, this study proposes a multi-segment fusion
tensor graph convolutional network (MS-FTGCN) for traffic flow prediction with
the following three-fold ideas: a) building a unified spatial-temporal graph
convolutional framework based on Tensor M-product, which capture the
spatial-temporal patterns simultaneously; b) incorporating hourly, daily, and
weekly components to model multi temporal properties of traffic flows,
respectively; c) fusing the outputs of the three components by attention
mechanism to obtain the final traffic flow prediction results. The results of
experiments conducted on two traffic flow datasets demonstrate that the
proposed MS-FTGCN outperforms the state-of-the-art models.
