A traffic accident dataset for Chattanooga, Tennessee

Journal Article · 04 July 2024 · Data in Brief

DOI: https://doi.org/10.1016/j.dib.2024.110675 · OSTI ID:2397469

Berres, Andy  ^[1]; Moriano, Pablo  ^[2]; Xu, Haowen  ^[2]; Tennille, Sarah  ^[2]; Smith, Lee ^[3]; Storey, Jonathan ^[3]; Sanyal, Jibonananda  ^[4]

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1. National Renewable Energy Laboratory (NREL), Golden, CO (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
2. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
3. Tennessee Department of Transportation, Nashville, TN (United States)
4. National Renewable Energy Laboratory (NREL), Golden, CO (United States)

This publication presents an annotated accident dataset which fuses traffic data from radar detection sensors, weather condition data, and light condition data with traffic accident data (as illustrated in Fig. 1) in a format that is easy to process using machine learning tools, databases, or data workflows. The purpose of this data is to analyze, predict, and detect traffic patterns when accidents occur. Each file contains a timeseries of traffic speeds, flows, and occupancies at the sensor nearest to the accident, as well as 5 neighboring sensors upstream and downstream. It also contains information about the accident type, date, and time. In addition to the accident data, we provide baseline data for typical traffic patterns during a given time of day. Overall, the dataset contains 6 months of annotated traffic data from November 2020 to April 2021. During this timeframe, and 361 accidents occurred in the monitored area around Chattanooga, Tennessee. This dataset served as the basis for a study on topology-aware automated accident detection for a companion publication [1].

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO)
• Grant/Contract Number: AC05-00OR22725; AC36-08GO28308
• OSTI ID: 2397469
• Journal Information: Data in Brief, Journal Name: Data in Brief Vol. 55; ISSN 2352-3409
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English