Short-term traffic flow prediction based on spatial–temporal attention time gated convolutional network with particle swarm optimization

Recently, the surge in vehicle ownership has led to a corresponding increase in the complexity of traffic data. Consequently, accurate traffic flow prediction has become crucial for effective traffic management. While the advancements in intelligent transportation system (ITS) and internet of things (IoT) technology have facilitated traffic flow prediction, many existing methods overlook the influence of the training process on model accuracy. Traditional approaches often fail to account for this critical aspect. Hence, a new approach to traffic flow prediction is introduced in this paper: a spatial–temporal attention time-gated convolutional network based on particle swarm optimization (PSO-STATG). This method uses the particle swarm algorithm to dynamically optimize the learning rate and epoch parameters throughout the training process. Firstly, spatial–temporal correlations are extracted through spatial map convolution and time-gated convolution, facilitated by an attention mechanism. Subsequently, the learning rate and epoch parameters are dynamically adjusted during the training phase via the particle swarm optimization algorithm. Finally, experiments are conducted with real-world datasets, and the results are compared with those from several existing methods. The experimental results indicate that the accuracy and stability of our proposed model in predicting traffic flow are superior.