# TrafFormer: A Transformer Model for Predicting Long-term Traffic

arXiv: https://arxiv.org/abs/2302.12388 · HTML: https://mlsanity.com/paper/2302.12388/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution](https://mlsanity.com/paper/2104.14917/) (2021)
- [Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks](https://mlsanity.com/paper/1705.02699/) (2017)
- [Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework](https://mlsanity.com/paper/1612.01022/) (2016)
- [DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting](https://mlsanity.com/paper/1709.09585/) (2017)
- [Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction](https://mlsanity.com/paper/1703.01006/) (2017)
- [A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction](https://mlsanity.com/paper/2205.15218/) (2022)
- [DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction](https://mlsanity.com/paper/1707.03213/) (2017)

## Built on by (most cited first, 2 of 2)

- [A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs](https://mlsanity.com/paper/2404.16921/) (2024)
- [Long-Horizon Traffic Forecasting via Incident-Aware Conformal Spatio-Temporal Transformers](https://mlsanity.com/paper/2603.16857/) (2026)

## 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.