DOI: 10.5281/zenodo.7964288
title: A Tagged Traffic Accident Dataset for Machine Learning

description: This dataset contains tagged accident data and is provided for reproducibility for our journal paper 

Pablo Moriano, Andreas Berres, Haowen Xu, Jibonananda Sanyal. “Spatiotemporal Features of Traffic Help Reduce Automatic Accident Detection Time.” Expert Systems with Applications 244 (2024): 122813. https://doi.org/10.1016/j.eswa.2023.122813

The accompanying Data in Brief publication discusses the methodology behind the creation of these data.

Berres, Andreas, Pablo Moriano, Haowen Xu, Sarah Tennille, Lee Smith, Jonathan Storey, and Jibonananda Sanyal. "A Traffic Accident Dataset for Chattanooga, Tennessee." Data in Brief (2024): 110675.

 

The zip folder annotatedData.zip contains two subfolders: allData and bestData. The bestData folder contains all data for which a full neighborhood of five sensors upstream and five sensors downstream is available, whereas allData includes everything from bestData as well as data with a smaller number of neighboring sensors. Each folder contains one subfolder called accidents and one subfolder called non-accidents. The accidents folder contains one file per accident. The non-accidents folder contains files for the same location, day of the week and time as a corresponding accident, for each week during which there was no accident impact on the traffic.

The file names in both folders are formatted as follows: yyyy-mm-dd-hhmm-rrrrrXaaa.a.csv, consisting of date (yyyy-mm-dd), time (hhmm in 24-hour format), and sensor name (rrrrrXaaa.a), which consists of road name (rrrrr; 5 alphanumerical characters), heading (X), and mile marker (aaa.a). For example, the file 2020-11-03-1611-00I24W182.8.csv  contains data for an accident which occurred at 4:11 p.m. on November 3, 2020 on I-24 Westbound near the radar sensor at mile marker 182.8.

The content of each CSV file is a timeseries of radar data beginning 15 minutes prior to the reported incident and ending 15 minutes after the reported incident. It also contains metadata, such as the accident type, etc. Each CSV file contains the following columns:



incident at sensor(i): 1 for yes (accidents folder), 0 for no (non-accidents folder)

road: road name with heading, e.g. 00I24E

mile: mile marker of nearest radar sensor, e.g. 182.8

type: accident type, e.g. “Prop Damage (over)” for property damage exceeding a certain threshold. For non-accidents, the type is given as “None”.

date: date of the data sample. For accidents, this is the date on which the accident occurred. For non-accidents, this is the date for which the non-accident data sample is collected.

incident_time: time the reference accident was reported in hh:mm. This is the time which is provided in E-TRIMS as the time the 911 call was made.

incident_hour: just the hour from the incident_time, in integer format.

data_time: timestamp for the timeseries contained in the file in hh:mm:ss format. The timeseries consists of 30 second timesteps.

weather: weather during data_time, based on data collected from NASA POWER. We used dry bulb temperature (°C), precipitation (mm/h), and wind speed (m/s) from the raw NASA POWER data to produce the classifications of rain (at least 1mm precipitation and temperatures above 2°C), snow (at least 1mm precipitation and temperatures at or below 2°C), and wind (wind speeds over 30 mph or 13.5 m/s). If there were no inclement weather conditions, we set the category to “--".

light: light conditions during data_time. To produce this field, we collected sunrise, sunset, civil twilight start and civil twilight end times from https://sunrise-sunset.org, and derived the categories dawn, daylight, dusk, and dark using these start and end times.

The last 33 columns contain radar data for the 11 sensors surrounding the accident or non-accident. For each sensor, we collected speed (mean over 30-second interval in miles per hour, or empty if no vehicles passed), volume (count of all vehicles passing during 30-second interval), and occupancy (mean % of occupancy over 30-second interval).  These three variables are grouped in triples, of speed (k), volume (k), occupancy (k), where k indicates the sensor number relative to the closest sensor i to the incident, k<i indicate upstream sensors and k>i indicate downstream sensors. For example, speed (i-5) refers to the mean speed at the sensor which is 5 hops upstream from the accident, and volume(i+1) refers to the number of vehicles at the sensor immediately downstream from the accident.


The folder metaData.zip contains the following files:



Accidents.csv: cleaned-up accidents file with all accidents which happened on Chattanooga area highways between November 1, 2020 and April 29, 2021. We have removed accidents which happened on non-highway roads, and we have corrected the timestamps (which were in 12-hour format but missing a.m./p.m. markers) by cross-referencing light and weather conditions.

WeatherDict.json: a dictionary containing the weather data synthesized from NASA POWER.

LightDict.json: a dictionary containing the light data synthesized from Sunrise-and-Sunset.

SensorTopology.csv: neighborhood information for each radar sensor in the Chattanooga area.

SensorZones.geojson: polygons used to determine the nearest radar sensor for each accident location. Each polygon is tagged with the corresponding radar sensor’s name.