# From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Data

arXiv: https://arxiv.org/abs/2009.13794 · HTML: https://mlsanity.com/paper/2009.13794/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [ChatTraffic: Text-to-Traffic Generation via Diffusion Model](https://mlsanity.com/paper/2311.16203/) (2023)
- [BjTT: A Large-scale Multimodal Dataset for Traffic Prediction](https://mlsanity.com/paper/2403.05029/) (2024)
- [Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey](https://mlsanity.com/paper/2209.07272/) (2022)
- [From Twitter to Reasoner: Understand Mobility Travel Modes and Sentiment Using Large Language Models](https://mlsanity.com/paper/2411.02666/) (2024)
- [Cellular Traffic Prediction via Deep State Space Models with Attention Mechanism](https://mlsanity.com/paper/2506.15688/) (2025)
- [Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting](https://mlsanity.com/paper/2206.11078/) (2022)
- [MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction](https://mlsanity.com/paper/2505.21553/) (2025)
- [ST-former for short-term passenger flow prediction during COVID-19 in urban rail transit system](https://mlsanity.com/paper/2210.09043/) (2022)
- [Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports](https://mlsanity.com/paper/2403.17914/) (2024)

## Abstract (arXiv)

The effectiveness of traditional traffic prediction methods is often extremely limited when forecasting traffic dynamics in early morning. The reason is that traffic can break down drastically during the early morning commute, and the time and duration of this break-down vary substantially from day to day. Early morning traffic forecast is crucial to inform morning-commute traffic management, but they are generally challenging to predict in advance, particularly by midnight. In this paper, we propose to mine Twitter messages as a probing method to understand the impacts of people's work and rest patterns in the evening/midnight of the previous day to the next-day morning traffic. The model is tested on freeway networks in Pittsburgh as experiments. The resulting relationship is surprisingly simple and powerful. We find that, in general, the earlier people rest as indicated from Tweets, the more congested roads will be in the next morning. The occurrence of big events in the evening before, represented by higher or lower tweet sentiment than normal, often implies lower travel demand in the next morning than normal days. Besides, people's tweeting activities in the night before and early morning are statistically associated with congestion in morning peak hours. We make use of such relationships to build a predictive framework which forecasts morning commute congestion using people's tweeting profiles extracted by 5 am or as late as the midnight prior to the morning. The Pittsburgh study supports that our framework can precisely predict morning congestion, particularly for some road segments upstream of roadway bottlenecks with large day-to-day congestion variation. Our approach considerably outperforms those existing methods without Twitter message features, and it can learn meaningful representation of demand from tweeting profiles that offer managerial insights.