TrafFormer: A Transformer Model for Predicting Long-term Traffic

TL;DR

This paper introduces TrafFormer, a Transformer-based model designed for long-term traffic prediction up to 24 hours ahead, addressing the limitations of recurrent models and demonstrating superior performance.

Contribution

The paper presents a novel Transformer model for long-term traffic prediction, extending the prediction horizon and improving accuracy over existing recurrent-based models.

Findings

TrafFormer outperforms existing hybrid neural network models.

Transformer-based approach is more effective for long-term traffic forecasting.

Model demonstrates significant improvements in prediction accuracy.

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.