# A Countrywide Traffic Accident Dataset

arXiv: https://arxiv.org/abs/1906.05409 · HTML: https://mlsanity.com/paper/1906.05409/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 18 of 18)

- [A Survey on Evaluation of Out-of-Distribution Generalization](https://mlsanity.com/paper/2403.01874/) (2024)
- [TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance](https://mlsanity.com/paper/2209.12386/) (2022)
- [Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation](https://mlsanity.com/paper/2205.05197/) (2022)
- [CrashFormer: A Multimodal Architecture to Predict the Risk of Crash](https://mlsanity.com/paper/2402.05151/) (2024)
- [Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools](https://mlsanity.com/paper/2103.11824/) (2021)
- [Traffic incident duration prediction via a deep learning framework for text description encoding](https://mlsanity.com/paper/2209.08735/) (2022)
- [Predicting Accident Severity: An Analysis Of Factors Affecting Accident Severity Using Random Forest Model](https://mlsanity.com/paper/2310.05840/) (2023)
- [United States Road Accident Prediction using Random Forest Predictor](https://mlsanity.com/paper/2505.06246/) (2025)
- [ALCo-FM: Adaptive Long-Context Foundation Model for Accident Prediction](https://mlsanity.com/paper/2507.08153/) (2025)
- [STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction](https://mlsanity.com/paper/2308.13183/) (2023)
- [Accident Impact Prediction based on a deep convolutional and recurrent neural network model](https://mlsanity.com/paper/2411.07537/) (2024)
- [MARBLE: A Multi-Agent Rule-Based LLM Reasoning Engine for Accident Severity Prediction](https://mlsanity.com/paper/2507.04893/) (2025)
- [Predicting Traffic Accident Severity with Deep Neural Networks](https://mlsanity.com/paper/2509.03819/) (2025)
- [Towards Resilient Transportation: A Conditional Transformer for Accident-Informed Traffic Forecasting](https://mlsanity.com/paper/2512.09398/) (2025)
- [The Weather Paradox: Why Precipitation Fails to Predict Traffic Accident Severity in Large-Scale US Data](https://mlsanity.com/paper/2601.00152/) (2026)
- [From numerical proportions to analogical proportions between probabilities](https://mlsanity.com/paper/2606.23029/) (2026)
- [Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling](https://mlsanity.com/paper/2607.01022/) (2026)
- [Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data](https://mlsanity.com/paper/2607.05995/) (2026)

## Abstract (arXiv)

Reducing traffic accidents is an important public safety challenge. However, the majority of studies on traffic accident analysis and prediction have used small-scale datasets with limited coverage, which limits their impact and applicability; and existing large-scale datasets are either private, old, or do not include important contextual information such as environmental stimuli (weather, points-of-interest, etc.). In order to help the research community address these shortcomings we have - through a comprehensive process of data collection, integration, and augmentation - created a large-scale publicly available database of accident information named US-Accidents. US-Accidents currently contains data about 2.25 million instances of traffic accidents that took place within the contiguous United States, and over the last three years. Each accident record consists of a variety of intrinsic and contextual attributes such as location, time, natural language description, weather, period-of-day, and points-of-interest. We present this dataset in this paper, along with a wide range of insights gleaned from this dataset with respect to the spatiotemporal characteristics of accidents. The dataset is publicly available at https://smoosavi.org/datasets/usaccidents.