GMHANN: A Novel Traffic Flow Prediction Method for Transportation Management Based on Spatial-Temporal Graph Modeling

Traffic flow prediction significantly affects the intelligent transportation for digitized urban transportation management and urban traffic control. Considering the complexity and strong non-linearity shown by traffic flow data, the establishment of model regarding spatial correlations as well as time dynamics can remarkably help to accurately predict traffic flow. A lot of current methods are mainly focused on using the historical time series information of observations to extract sequence features. Such forecasting will cause the lack of information and lead to poor accuracy of the forecast results. Although some studies applied spatial-temporal information, but they are not very accurate. In network-based problems, we would consider the constraint of road networks. Specifically, intersection flows, road speed and travel time are related to road networks. Also, they restrict the long-term prediction of traffic flow. For addressing above issues, a graph multi-head attention neural network (GMHANN) is proposed for the purpose of traffic flow prediction. In design, the GMHANN has an encoder-decoder structure. By the encoder, the data are compressed into a hidden space representation, which, relying on the decoder, is reconstructed as output. Furthermore, we put forward a novel gated recurrent unit (GRU) module (AGRU) based on multi-head attention for the effective extraction of the spatial and temporal features exhibited by traffic flow data. Other state-of-the-art methods are employed for evaluating four public datasets, which reveals that our proposed method outperforms others.

Automated summary

Traffic flow prediction is crucial for digitized urban transportation management and control. The complexity and non-linearity of traffic flow data can be addressed by establishing models with spatial correlations and time dynamics. Current methods mainly rely on historical time series information, but this approach lacks information and leads to poor accuracy. To solve these issues, this study proposes a graph multi-head attention neural network (GMHANN) that compresses data into a hidden space representation and reconstructs the output using a decoder. Additionally, a novel gated recurrent unit (AGRU) based on multi-head attention is introduced for effective spatial and temporal feature extraction. Experimental results show that the proposed method outperforms state-of-the-art techniques.