Traffic flow prediction is the key to accurate urban traffic control and the basis for developing intelligent transportation systems. Recent studies have made substantial progress in traffic prediction by modelling complex spatiotemporal graph topology and considering sensors as road network nodes. However, the current spatiotemporal graph neural network model is limited by its structure. It can only utilize short-range traffic flow data and cannot effectively extract the long-term trend of complex traffic flow and periodic features in traffic patterns. To address the above problems, we propose a Transformer-based long-term traffic flow prediction framework, “Transformer-based spatiotemporal graph attention network”. First, the model utilizes the Transformer coding layer to learn compressed and context-rich subsequence temporal representations from long-term sequences. Then, the model designs a multi-scale gated temporal convolution module to identify and extract long-term trend features of traffic flow from the subsequence time representations. Next, the model constructs a multi-granularity random graph attention module to capture the periodic features of traffic flow from the subsequence time representations and extracts the short-term trend features present in the long-time series using the STGNN model. Finally, the model fuses the extracted long-term trends, periodic features and short-term trends to obtain the final prediction results. Experimental results on two real-world traffic flow datasets show that the model outperforms the baseline model and makes accurate long-term predictions.
Xiao et al. (Fri,) studied this question.