TrafFormer: A Transformer Model for Predicting Long-term Traffic
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 44 references that are in the map. First posted on arXiv 24 Feb 2023.
Builds on
12 of 44 references.
References · 12
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- 2017DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Predictioncited by 3
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.