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

Ke Zhang, Hongjin Ren, Jinbiao Kang, Cai Guo, Weiming Chen, Ming Tao, Hong Ning Dai*, Shaohua Wan, Haiyong Bao

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

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 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 spatio-temporal embedding method that integrates temporal relationships and spatial information with external inputs, thereby capturing the spatio-temporal dependencies of traffic flows. Experiments with 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 tranformer. Compared to STD-NET, there is a 2% to 10% improvement in performance on different datasets.

Original languageEnglish
Article number10756574
Number of pages12
JournalIEEE Internet of Things Journal
DOIs
Publication statusE-pub ahead of print - 18 Nov 2024

Scopus Subject Areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

User-Defined Keywords

  • Internet of Things
  • Spatio-temporal data
  • Traffic prediction
  • Transformer

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