Dynamic Spatial–Temporal Self-Attention Network for Traffic Flow Prediction

• Published in: Future internet Vol. 16; no. 6; p. 189
• Main Authors: Wang, Dong, Yang, Hongji, Zhou, Hua
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Approximation; Artificial intelligence; attention mechanism; Computational linguistics; Fourier transforms; Geospatial data; graph convolution network; Innovations; Intelligent transportation systems; Language processing; Modelling; Modules; Natural language interfaces; Neural networks; Propagation; Queuing theory; Spatial dependencies; Spatiotemporal data; spatiotemporal dependence; Time series; time series prediction; Traffic flow; traffic flow prediction; Traffic information; Transportation networks
• ISSN: 1999-5903; 1999-5903
• DOI: 10.3390/fi16060189

Traffic flow prediction is considered to be one of the fundamental technologies in intelligent transportation systems (ITSs) with a tremendous application prospect. Unlike traditional time series analysis tasks, the key challenge in traffic flow prediction lies in effectively modelling the highly complex and dynamic spatiotemporal dependencies within the traffic data. In recent years, researchers have proposed various methods to enhance the accuracy of traffic flow prediction, but certain issues still persist. For instance, some methods rely on specific static assumptions, failing to adequately simulate the dynamic changes in the data, thus limiting their modelling capacity. On the other hand, some approaches inadequately capture the spatiotemporal dependencies, resulting in the omission of crucial information and leading to unsatisfactory prediction outcomes. To address these challenges, this paper proposes a model called the Dynamic Spatial–Temporal Self-Attention Network (DSTSAN). Firstly, this research enhances the interaction between different dimension features in the traffic data through a feature augmentation module, thereby improving the model’s representational capacity. Subsequently, the current investigation introduces two masking matrices: one captures local spatial dependencies and the other captures global spatial dependencies, based on the spatial self-attention module. Finally, the methodology employs a temporal self-attention module to capture and integrate the dynamic temporal dependencies of traffic data. We designed experiments using historical data from the previous hour to predict traffic flow conditions in the hour ahead, and the experiments were extensively compared to the DSTSAN model, with 11 baseline methods using four real-world datasets. The results demonstrate the effectiveness and superiority of the proposed approach.