# A deep learning approach for detecting traffic accidents from social media data

arXiv: https://arxiv.org/abs/1801.01528 · HTML: https://mlsanity.com/paper/1801.01528/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2018.

## Built on by (most cited first, 12 of 12)

- [Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models](https://mlsanity.com/paper/2109.12422/) (2021)
- [Association Rules Mining with Auto-Encoders](https://mlsanity.com/paper/2304.13717/) (2023)
- [Explainable, automated urban interventions to improve pedestrian and vehicle safety](https://mlsanity.com/paper/2110.11672/) (2021)
- [Traffic incident duration prediction via a deep learning framework for text description encoding](https://mlsanity.com/paper/2209.08735/) (2022)
- [Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting](https://mlsanity.com/paper/2206.11078/) (2022)
- [Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Models](https://mlsanity.com/paper/2306.00016/) (2023)
- [Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection](https://mlsanity.com/paper/2108.09506/) (2021)
- [Traffic Event Detection as a Slot Filling Problem](https://mlsanity.com/paper/2109.06035/) (2021)
- [Deriving the Traveler Behavior Information from Social Media: A Case Study in Manhattan with Twitter](https://mlsanity.com/paper/2101.11482/) (2021)
- [Know Unreported Roadway Incidents in Real-time: Early Traffic Anomaly Detection](https://mlsanity.com/paper/2412.10892/) (2024)
- [Learning physically grounded traffic accident reconstruction from public accident reports](https://mlsanity.com/paper/2605.00050/) (2026)
- [ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing](https://mlsanity.com/paper/2606.15315/) (2026)

## Abstract (arXiv)

This paper employs deep learning in detecting the traffic accident from social media data. First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan areas: Northern Virginia and New York City. Our results show that paired tokens can capture the association rules inherent in the accident-related tweets and further increase the accuracy of the traffic accident detection. Second, two deep learning methods: Deep Belief Network (DBN) and Long Short-Term Memory (LSTM) are investigated and implemented on the extracted token. Results show that DBN can obtain an overall accuracy of 85% with about 44 individual token features and 17 paired token features. The classification results from DBN outperform those of Support Vector Machines (SVMs) and supervised Latent Dirichlet allocation (sLDA). Finally, to validate this study, we compare the accident-related tweets with both the traffic accident log on freeways and traffic data on local roads from 15,000 loop detectors. It is found that nearly 66% of the accident-related tweets can be located by the accident log and more than 80% of them can be tied to nearby abnormal traffic data. Several important issues of using Twitter to detect traffic accidents have been brought up by the comparison including the location and time bias, as well as the characteristics of influential users and hashtags.