TrafFormer: A Transformer Model for Predicting Long-term Traffic

David Alexander Tedjopurnomo; Farhana M. Choudhury; A. K. Qin

TrafFormer: A Transformer Model for Predicting Long-term Traffic

Machine Learning 2023-03-06 v3

Authors: David Alexander Tedjopurnomo , Farhana M. Choudhury , A. K. Qin

Abstract

Traffic prediction is a flourishing research field due to its importance in human mobility in the urban space. Despite this, existing studies only focus on short-term prediction of up to few hours in advance, with most being up to one hour only. Long-term traffic prediction can enable more comprehensive, informed, and proactive measures against traffic congestion and is therefore an important task to explore. In this paper, we explore the task of long-term traffic prediction; where we predict traffic up to 24 hours in advance. We note the weaknesses of existing models--which are based on recurrent structures--for long-term traffic prediction and propose a modified Transformer model "TrafFormer". Experiments comparing our model with existing hybrid neural network models show the superiority of our model.

Keywords

traffic forecasting trajectory prediction traffic signal control

Cite

@article{arxiv.2302.12388,
  title  = {TrafFormer: A Transformer Model for Predicting Long-term Traffic},
  author = {David Alexander Tedjopurnomo and Farhana M. Choudhury and A. K. Qin},
  journal= {arXiv preprint arXiv:2302.12388},
  year   = {2023}
}

Comments

14 pages, 6 figures

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