TST-Trans: A Transformer Network for Urban Traffic Flow Prediction

Abstract

A critical challenge for predicting urban traffic flows is to simultaneously process time series and spatial features from heterogeneous traffic data collected by diverse Internet of Things (IoT) devices. Despite the advent of Transformer-based models with an advanced network structure and excellent prediction performance, standard Transformer models are still struggling to combine both spatial information and temporal relations of traffic flows. To address these challenges, we design a novel Transformer network, namely temporal-spatial traffic-flow Transformer (TST-Trans), for traffic flow prediction with high accuracy. In particular, we use learnable position encoders to replace traditional fixed position encoders. Meanwhile, we introduce a spatiotemporal embedding method that integrates temporal relationships and spatial information with external inputs, thereby capturing the spatiotemporal dependencies of traffic flows. Experiments with the real-world datasets demonstrate that our proposed TST-Trans achieves better prediction accuracy than state-of-the-art methods while requiring fewer parameters. The research results increased by more than 10% compared with Transformer. Compared to spatiotemporal deep hybrid neural network, there is a 2% to 10% improvement in performance on different datasets.