A Countrywide Traffic Accident Dataset
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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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Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 3; 2025: 5; 2026: 4.
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- 2022TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillancecited by 20
- 2024A Survey on Evaluation of Out-of-Distribution Generalizationcited by 20
- 2022Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisationcited by 6
- 2024CrashFormer: A Multimodal Architecture to Predict the Risk of Crashcited by 5
- 2021Big Data for Traffic Estimation and Prediction: A Survey of Data and Toolscited by 3
- 2022Traffic incident duration prediction via a deep learning framework for text description encodingcited by 3
- 2023Predicting Accident Severity: An Analysis Of Factors Affecting Accident Severity Using Random Forest Modelcited by 2
- 2025United States Road Accident Prediction using Random Forest Predictorcited by 1
- 2025ALCo-FM: Adaptive Long-Context Foundation Model for Accident Predictioncited by 1
- 2023STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Predictioncited by 0
- 2024Accident Impact Prediction based on a deep convolutional and recurrent neural network modelcited by 0
- 2025MARBLE: A Multi-Agent Rule-Based LLM Reasoning Engine for Accident Severity Predictioncited by 0
6 more
- 2025Predicting Traffic Accident Severity with Deep Neural Networkscited by 0
- 2025Towards Resilient Transportation: A Conditional Transformer for Accident-Informed Traffic Forecastingcited by 0
- 2026The Weather Paradox: Why Precipitation Fails to Predict Traffic Accident Severity in Large-Scale US Datacited by 0
- 2026From numerical proportions to analogical proportions between probabilitiescited by 0
- 2026Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modelingcited by 0
- 2026Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Datacited by 0
- 2026Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data7 Jul 2026
- 2026Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling1 Jul 2026
- 2026From numerical proportions to analogical proportions between probabilities22 Jun 2026
- 2026The Weather Paradox: Why Precipitation Fails to Predict Traffic Accident Severity in Large-Scale US Data1 Jan 2026
- 2025Towards Resilient Transportation: A Conditional Transformer for Accident-Informed Traffic Forecasting10 Dec 2025
- 2025Predicting Traffic Accident Severity with Deep Neural Networks4 Sep 2025
- 2025ALCo-FM: Adaptive Long-Context Foundation Model for Accident Prediction10 Jul 2025
- 2025MARBLE: A Multi-Agent Rule-Based LLM Reasoning Engine for Accident Severity Prediction7 Jul 2025
- 2025United States Road Accident Prediction using Random Forest Predictor28 Apr 2025
- 2024Accident Impact Prediction based on a deep convolutional and recurrent neural network model12 Nov 2024
- 2024A Survey on Evaluation of Out-of-Distribution Generalization4 Mar 2024
- 2024CrashFormer: A Multimodal Architecture to Predict the Risk of Crash7 Feb 2024
6 more
- 2023Predicting Accident Severity: An Analysis Of Factors Affecting Accident Severity Using Random Forest Model9 Oct 2023
- 2023STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction25 Aug 2023
- 2022TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance26 Sep 2022
- 2022Traffic incident duration prediction via a deep learning framework for text description encoding19 Sep 2022
- 2022Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation10 May 2022
- 2021Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools18 Mar 2021
- 2022TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillancecited by 20
- 2024A Survey on Evaluation of Out-of-Distribution Generalizationcited by 20
- 2022Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisationcited by 6
- 2024CrashFormer: A Multimodal Architecture to Predict the Risk of Crashcited by 5
- 2021Big Data for Traffic Estimation and Prediction: A Survey of Data and Toolscited by 3
- 2022Traffic incident duration prediction via a deep learning framework for text description encodingcited by 3
- 2023Predicting Accident Severity: An Analysis Of Factors Affecting Accident Severity Using Random Forest Modelcited by 2
- 2025United States Road Accident Prediction using Random Forest Predictorcited by 1
- 2023STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Predictioncited by 0
- 2024Accident Impact Prediction based on a deep convolutional and recurrent neural network modelcited by 0
- 2025Predicting Traffic Accident Severity with Deep Neural Networkscited by 0
- 2025Towards Resilient Transportation: A Conditional Transformer for Accident-Informed Traffic Forecastingcited by 0
12 most cited of 16 All 16 (JSON)
- 2026Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data7 Jul 2026
- 2026Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling1 Jul 2026
- 2026From numerical proportions to analogical proportions between probabilities22 Jun 2026
- 2026The Weather Paradox: Why Precipitation Fails to Predict Traffic Accident Severity in Large-Scale US Data1 Jan 2026
- 2025Towards Resilient Transportation: A Conditional Transformer for Accident-Informed Traffic Forecasting10 Dec 2025
- 2025Predicting Traffic Accident Severity with Deep Neural Networks4 Sep 2025
- 2025United States Road Accident Prediction using Random Forest Predictor28 Apr 2025
- 2024Accident Impact Prediction based on a deep convolutional and recurrent neural network model12 Nov 2024
- 2024A Survey on Evaluation of Out-of-Distribution Generalization4 Mar 2024
- 2024CrashFormer: A Multimodal Architecture to Predict the Risk of Crash7 Feb 2024
- 2023Predicting Accident Severity: An Analysis Of Factors Affecting Accident Severity Using Random Forest Model9 Oct 2023
- 2023STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction25 Aug 2023
12 newest of 16 All 16 (JSON)