MFMA–Informer: A Short-Term Traffic Flow Prediction Model Incorporating Spatiotemporal Features

In intelligent transport systems (ITS), accurate and real-time traffic flow prediction is essential to reduce congestion and manage transport. Numerous existing studies have used hybrid models to extract various intrinsic patterns of traffic flow data, but their noise is not treated when extracting spatial features. Different from the existing studies, this document proposes a new hybrid model called “MFMA–Informer (Multiscale Fusion and Multiscale Attention–Informer),” which aims to reduce noise interference while deeply mining the intrinsic model of spatiotemporal information. The model consists of two parts: feature extraction and feature fusion. First, in the spatial feature extractor model, the MFMA model is proposed, which uses a multiscale temporal convolutional network (TCN) and long short-term memory (LSTM) to obtain multiscale spatial features and reduces noise by computing attention at different scales. Second, in the temporal properties model, the longtime dependency of traffic flow data has been fully achieved because the Informer model has the advantage of encoding temporal data. In addition, in the feature fusion module, interdependencies are constructed on the extracted spatiotemporal features through cross-attention to further enhance the consistency of spatiotemporal feature expression. Finally, to prove the effectiveness of the framework, numerous trials were performed on four different real-world datasets, which showed that MFMA–Informer had excellent forecasting and generalization capabilities.