mlsanityData to 7 Oct 2026

A Countrywide Traffic Accident Dataset

arXiv 1906.05409 ↗First posted 12 Jun 2019cs.DB cs.CYcited by 18

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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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 3; 2025: 5; 2026: 4.

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  1. 2022TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillancecited by 20
  2. 2024A Survey on Evaluation of Out-of-Distribution Generalizationcited by 20
  3. 2022Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisationcited by 6
  4. 2024CrashFormer: A Multimodal Architecture to Predict the Risk of Crashcited by 5
  5. 2021Big Data for Traffic Estimation and Prediction: A Survey of Data and Toolscited by 3
  6. 2022Traffic incident duration prediction via a deep learning framework for text description encodingcited by 3
  7. 2023Predicting Accident Severity: An Analysis Of Factors Affecting Accident Severity Using Random Forest Modelcited by 2
  8. 2025United States Road Accident Prediction using Random Forest Predictorcited by 1
  9. 2025ALCo-FM: Adaptive Long-Context Foundation Model for Accident Predictioncited by 1
  10. 2023STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Predictioncited by 0
  11. 2024Accident Impact Prediction based on a deep convolutional and recurrent neural network modelcited by 0
  12. 2025MARBLE: A Multi-Agent Rule-Based LLM Reasoning Engine for Accident Severity Predictioncited by 0
6 more
  1. 2025Predicting Traffic Accident Severity with Deep Neural Networkscited by 0
  2. 2025Towards Resilient Transportation: A Conditional Transformer for Accident-Informed Traffic Forecastingcited by 0
  3. 2026The Weather Paradox: Why Precipitation Fails to Predict Traffic Accident Severity in Large-Scale US Datacited by 0
  4. 2026From numerical proportions to analogical proportions between probabilitiescited by 0
  5. 2026Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modelingcited by 0
  6. 2026Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Datacited by 0