# Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting

arXiv: https://arxiv.org/abs/2206.11078 · HTML: https://mlsanity.com/paper/2206.11078/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 63 references that are in the map.
- First posted on arXiv 19 Jun 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting](https://mlsanity.com/paper/1802.07007/) (2018)
- [Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction](https://mlsanity.com/paper/1701.04245/) (2017)
- [Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction](https://mlsanity.com/paper/1801.02143/) (2018)
- [Transferring Inductive Biases through Knowledge Distillation](https://mlsanity.com/paper/2006.00555/) (2020)
- [Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks](https://mlsanity.com/paper/1705.02699/) (2017)
- [Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values](https://mlsanity.com/paper/2005.11627/) (2020)
- [Deep Sequence Learning with Auxiliary Information for Traffic Prediction](https://mlsanity.com/paper/1806.07380/) (2018)
- [Learning dynamic and hierarchical traffic spatiotemporal features with Transformer](https://mlsanity.com/paper/2104.05163/) (2021)
- [A deep learning approach for detecting traffic accidents from social media data](https://mlsanity.com/paper/1801.01528/) (2018)
- [Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models](https://mlsanity.com/paper/1811.04745/) (2018)
- [From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Data](https://mlsanity.com/paper/2009.13794/) (2020)
- [Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images](https://mlsanity.com/paper/2005.04188/) (2020)
- [Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations](https://mlsanity.com/paper/2102.04064/) (2021)

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

- [Generative AI in Transportation Planning: A Survey](https://mlsanity.com/paper/2503.07158/) (2025)
- [GC-GRU-N for Traffic Prediction using Loop Detector Data](https://mlsanity.com/paper/2211.08541/) (2022)

## Abstract (arXiv)

With accurate and timely traffic forecasting, the impacted traffic conditions can be predicted in advance to guide agencies and residents to respond to changes in traffic patterns appropriately. However, existing works on traffic forecasting mainly relied on historical traffic patterns confining to short-term prediction, under 1 hour, for instance. To better manage future roadway capacity and accommodate social and human impacts, it is crucial to propose a flexible and comprehensive framework to predict physical-aware long-term traffic conditions for public users and transportation agencies. In this paper, the gap of robust long-term traffic forecasting was bridged by taking social media features into consideration. A correlation study and a linear regression model were first implemented to evaluate the significance of the correlation between two time-series data, traffic intensity and Twitter data intensity. Two time-series data were then fed into our proposed social-aware framework, Traffic-Twitter Transformer, which integrated Nature Language representations into time-series records for long-term traffic prediction. Experimental results in the Great Seattle Area showed that our proposed model outperformed baseline models in all evaluation matrices. This NLP-joined social-aware framework can become a valuable implement of network-wide traffic prediction and management for traffic agencies.