  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.
