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A traffic accident dataset for Chattanooga, Tennessee.

Andy Berres1,2, Pablo Moriano3, Haowen Xu2

  • 1Energy Conversion and Storage Systems Center, National Renewable Energy Laboratory, 15301 Denver West Parkway, Mail Stop RSF 042, Golden, CO 80401, United States.

Data in Brief
|August 5, 2024
PubMed
Summary

This study introduces a fused traffic accident dataset, combining sensor, weather, and light data for machine learning analysis. The dataset aids in predicting and detecting traffic anomalies during accidents.

Keywords:
Annotated dataIncident dataLight conditionsMachine learningRadar sensor dataTimeseriesTransportationWeather conditions

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Area of Science:

  • Transportation Engineering
  • Data Science
  • Traffic Safety

Background:

  • Accurate traffic data is crucial for understanding and mitigating accident impacts.
  • Existing datasets often lack comprehensive fusion of real-time traffic, environmental, and accident specifics.
  • Machine learning approaches require well-structured, annotated data for effective traffic pattern analysis.

Purpose of the Study:

  • To present a novel, annotated dataset fusing traffic sensor data, weather conditions, light conditions, and accident details.
  • To facilitate machine learning-based analysis, prediction, and detection of traffic patterns during accidents.
  • To provide baseline data for normal traffic conditions for comparative analysis.

Main Methods:

  • Fusion of data from radar detection sensors, weather, light, and traffic accident records.
  • Inclusion of time-series data for traffic speed, flow, and occupancy from accident-proximate sensors (nearest, 5 upstream, 5 downstream).
  • Annotation of accident type, date, and time within the dataset.

Main Results:

  • A comprehensive dataset covering 6 months (November 2020 - April 2021) of traffic and accident data.
  • Detailed records for 361 accidents within the monitored area of Chattanooga, Tennessee.
  • Dataset format optimized for machine learning tools, databases, and data workflows.

Conclusions:

  • The presented dataset provides a valuable resource for advancing research in traffic accident analysis and prediction.
  • This fused dataset supports the development of automated accident detection systems.
  • The data enables deeper insights into the relationship between traffic conditions, environmental factors, and accident occurrences.