An urban road traffic flow prediction method based on multi-information fusion

Accurate traffic flow prediction not only relies on historical traffic flow information, but also needs to take into account the influence of a variety of external factors such as weather conditions and the distribution of neighbouring POIs. However, most of the existing studies have used historical data to predict future traffic flows for short periods of time. Spatio-Temporal Graph Neural Networks (STGNN) solves the problem of combining temporal properties and spatial dependence, but does not extract long-term trends and cyclical features of historical data. Therefore, this paper proposes a MIFPN (Multi information fusion prediction network) traffic flow prediction method based on the long and short-term features in the historical traffic flow data and combining with external information. First, a subsequence converter is utilised to allow the model to learn the temporal relationships of contextual subsequences from long historical sequences that incorporate external information. Then, a superimposed one-dimensional inflated convolutional layer is used to extract long-term trends, a dynamic graph convolutional layer to extract periodic features, and a short-term trend extractor to learn short-term temporal features. Finally, long-term trends, cyclical features and short-term features are fused to obtain forecasts. Experiments on real datasets show that the MIFPN model improves by an average of 11.2% over the baseline model in long term predictions up to 60 min ago.