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38 results for “traffic accidents”
Fatal traffic accidents in Catalonia
<p>This dataset contains 1024 fatal traffic accidents ocurred in Catalonia between June 13, 2014 and October 20, 2021. Each record has data about the date and time of the accident, it's localization and a description. The dataset has been obtained from applying web scrapping techniques on the Catalonia's Government (Generalitat de Catalunya) website: http://transit.gencat.cat/ca/el_servei/premsa_i_comunicacio/comunicats_d_accidents_mortals/</p> <p> </p> <p> </p>
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>This database is related to "A CONSOLIDATED DATABASE OF POLICE-REPORTED MOTOR VEHICLE TRAFFIC ACCIDENTS IN THE UNITED STATES FOR ACTUARIAL APPLICATIONS" (Araiza Iturria C.A., Hardy M., Marriott P.).</p> <p>Author Information</p> <p> A. Author<br> Name: Carlos Andrés Araiza Iturria<br> Email: caraizai@uwaterloo.ca<br> <br> B. Co-author<br> Name: Mary Hardy<br> Email: mary.hardy@uwaterloo.ca</p> <p> C. Co-author<br> Name: Paul Marriott<br> Email: pmarriott@uwaterloo.ca<br> <br> Institution: University of Waterloo<br> Address: 200 University Ave W, Waterloo, ON N2L 3G1</p> <p><br> Funding granted by the Natural Sciences and Engineering Research Council of Canada. Hardy: RGPIN-2018-03754, Marriott: RGPIN-2020-04015.</p> <p>The Python scripts to create the database can be directly accessed through related identifiers in this page.</p> <p>Parameter estimates along with their 90% confidence intervals from the 20 multinomial logistic regressions can be seen through related identifiers in this page.</p> <p> </p>
Dataset of traffic accidents reported on Twitter Bogotá Colombia
<p><strong>1 Classification Dataset</strong></p> <p>This dataset for the classification model contains 3,804 tweets, where 1,902 are related to traffic accident reports (TA, positive class) and 1,902 are unrelated (NTA, negative class).</p> <p>For training the tweet classification model, a collaborative labeling strategy was designed. Here, 30 people labeled data according to the instructions given. Each participant had to evaluate a tweet to manually classify it into one of three categories defined as: traffic accident related, unrelated and don´t know/no response. Each tweet was evaluated by 3 participants. The correct label was selected by voting; the 3 people must agree on the selected label, otherwise the tweet was excluded from training. This process took a month and required the development and deployment of a web application.</p> <p><strong>2 NER Dataset (Named Entity Recognition)</strong></p> <p>For the entity recognition model training, a sample of the filtered tweets resulting from the previous classification phase was taken. 1,340 tweets were extracted, where 800 are from “unofficial” users, almost 60% of the sample. These tweets were user reports on traffic incident occurred in Bogota from October 2018 to July 2019, including other tweets that contained some location references such as reports on the state of road infrastructure; some tweets from the years 2016 and 2017 were also included. Although these posts were not related to accidents per se, they were selected because they contained location information. The purpose was to train a model that would recognize these entities, because a classifier of accident-related tweets was previously created. Additionally, the dataset was split, reserving 1,072 tweets for training and 268 for evaluation.</p> <p>This dataset was manually labeled using the IOB (Inside-outside- beginning) format. The labeling tool called Brat Annotation Tools was used for this task. The labels defined are Location, which refers to the location of the report; and Time, which refers to the time or date of the incident. Accordingly, 5 labels were generated: B-loc, I-loc, B-time, I-time and O. The O label refers to Others.</p> <p><strong>3 Traffic accident Twitter geolocation</strong></p> <p>A dataset with 26362 traffic accident tweets with the coordinates of the incident and the date of publication.</p>
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>The parameter estimates along with their 90% confidence intervals obtained for the 20 multinomial logistic regressions are shown here in two presentations. In 'Covariate trends' we show the annual trends for the 20 years of data by type of covariate. In 'Covariates magnitude for each year', we show for each year the magnitude that each covariate has in contrast with the other 23 covariates (the intercept is not included due to scaling issues).</p> <p>All parameter estimates and their confidence intervals can be found in a table format in 'allparameters.csv'.</p>
A Tagged Traffic Accident Dataset for Machine Learning
<p>This dataset contains tagged accident data and is provided for reproducibility for our journal paper </p> <p><strong>Pablo Moriano, Andy Berres, Haowen Xu, Jibonananda Sanyal. “Spatiotemporal Features of Traffic Help Reduce Automatic Accident Detection Time.” <em>Expert Systems with Applications</em> 244 (2024): 122813. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.eswa.2023.122813" target="_blank" rel="noopener">https://doi.org/10.1016/j.eswa.2023.122813</a></strong></p> <p>The accompanying Data in Brief publication discusses the methodology behind the creation of these data.</p> <p><strong>Berres, Andy, Pablo Moriano, Haowen Xu, Sarah Tennille, Lee Smith, Jonathan Storey, and Jibonananda Sanyal. "A Traffic Accident Dataset for Chattanooga, Tennessee." <em>Data in Brief</em> (2024): 110675.</strong></p> <p> </p> <p>The zip folder <strong><em>annotatedData.zip</em></strong> contains two subfolders: <strong><em>allData</em></strong> and <strong><em>bestData</em></strong>. The <em>bestData</em> folder contains all data for which a full neighborhood of five sensors upstream and five sensors downstream is available, whereas <em>allData</em> includes everything from <em>bestData</em> as well as data with a smaller number of neighboring sensors. Each folder contains one subfolder called <strong><em>accidents</em></strong> and one subfolder called <strong><em>non-accidents</em></strong>. The <em>accidents</em> folder contains one file per accident. The <em>non-accidents</em> 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.</p> <p>The file names in both folders are formatted as follows: <strong>yyyy-mm-dd-hhmm-rrrrrXaaa.a.csv</strong>, 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 <em>2020-11-03-1611-00I24W182.8.csv </em> 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.</p> <p>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:</p> <ul> <li><strong>incident at sensor(i)</strong>: 1 for yes (<em>accidents</em> folder), 0 for no (<em>non-accidents</em> folder)</li> <li><strong>road</strong>: road name with heading, e.g. 00I24E</li> <li><strong>mile</strong>: mile marker of nearest radar sensor, e.g. 182.8</li> <li><strong>type</strong>: accident type, e.g. “Prop Damage (over)” for property damage exceeding a certain threshold. For non-accidents, the type is given as “None”.</li> <li><strong>date</strong>: 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.</li> <li><strong>incident_time</strong>: 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.</li> <li><strong>incident_hour</strong>: just the hour from the incident_time, in integer format.</li> <li><strong>data_time</strong>: timestamp for the timeseries contained in the file in hh:mm:ss format. The timeseries consists of 30 second timesteps.</li> <li><strong>weather</strong>: weather during <em>data_time</em>, 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 <em>rain</em> (at least 1mm precipitation and temperatures above 2°C), <em>snow </em>(at least 1mm precipitation and temperatures at or below 2°C), and <em>wind</em> (wind speeds over 30 mph or 13.5 m/s). If there were no inclement weather conditions, we set the category to <em>“--"</em>.</li> <li><strong>light</strong>: light conditions during data_time. To produce this field, we collected sunrise, sunset, civil twilight start and civil twilight end times from <a href="https://sunrise-sunset.org">https://sunrise-sunset.org</a>, and derived the categories dawn, daylight, dusk, and dark using these start and end times.</li> <li>The last 33 columns contain radar data for the 11 sensors surrounding the accident or non-accident. For each sensor, we collected <em>speed</em> (mean over 30-second interval in miles per hour, or empty if no vehicles passed), <em>volume</em> (count of all vehicles passing during 30-second interval), and <em>occupancy</em> (mean % of occupancy over 30-second interval). These three variables are grouped in triples, of <strong>speed (k), volume (k), occupancy (k)</strong>, where <em>k</em> indicates the sensor number relative to the closest sensor <em>i</em> to the incident, <em>k<i</em> indicate upstream sensors and <em>k>i</em> indicate downstream sensors. For example, <strong>speed (i-5)</strong> refers to the mean speed at the sensor which is 5 hops upstream from the accident, and <strong>volume(i+1) </strong>refers to the number of vehicles at the sensor immediately downstream from the accident.</li> </ul> <p>The folder <strong><em>metaData.zip</em></strong> contains the following files:</p> <ul> <li><strong>Accidents.csv</strong>: 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.</li> <li><strong>WeatherDict</strong><strong>.json:</strong> a dictionary containing the weather data synthesized from NASA POWER.</li> <li><strong>LightDict.json</strong>: a dictionary containing the light data synthesized from Sunrise-and-Sunset.</li> <li><strong>SensorTopology.csv</strong>: neighborhood information for each radar sensor in the Chattanooga area.</li> <li><strong>SensorZones.geojson</strong>: polygons used to determine the nearest radar sensor for each accident location. Each polygon is tagged with the corresponding radar sensor’s name.</li> </ul>
Magnitude and determinants of road traffic accidents in North Gondar Zone, Amhara Region, Ethiopia
<p>Number and types of a road traffic accidents in relation to road and road user, environmental and time related and vehicle related factors</p>
Synthetic dataset: Traffic Accidents & Vehicle Registry
<ul> <li><strong>Dataset A: Traffic Accidents</strong> -- Comprising 15,000 records with attributes such as Accident ID, Date, Time, Location, Vehicle ID, Severity, and Description.</li> <li><strong>Dataset B: Vehicle Registry</strong> -- Comprising 20,000 records with attributes including Vehicle ID, Owner Age, Owner Gender, Vehicle Model, and Registration Date.</li> </ul>
A Feasibility Trial of Eye Movement Desensitization and Reprocessing Therapy- Integrative Treatment Group Protocol for Ongoing Traumatic Stress In Road Traffic Accident Survivors for Reduction of Post
ClinicalTrials.gov study NCT07027930. IPD Sharing: NO. Countries: 1. Publications: 1.
NGII Data Set for Black Ice Traffic Accident Prediction
<p><a href="../api/records/10863284/draft/files/NGII%20Data%20Set%20for%20Black%20Ice%20Traffic%20Accident%20Prediction.zip/content" target="_blank" rel="noopener noreferrer">Title: NGII Data Set for Black Ice Traffic Accident Prediction</a></p> <p>This dataset has been processed for scholarly purposes, utilizing data provided by the National Geographic Information Institute of Korea.</p> <p><Reference></p> <p>National Geographic Information Institute. (n.d.). National Land Information Platform. Retrieved from https://map.ngii.go.kr/ms/map/NlipMap.do</p>
Learn from Human-driving Accidents to Attack Autonomous Driving_Practical Traffic Flow Attacks on Decision-making
<p>We provide some demo videos of attack patterns</p>
US-Accidents: A San Francisco Traffic Accident Dataset
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Safety and Efficacy of Integrative Korean Medicine Treatment for Elderly After Traffic Accident
ClinicalTrials.gov study NCT07007299. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effect of Early MSAT Treatment on Sciatica Caused by Traffic Accidents.
ClinicalTrials.gov study NCT06179901. IPD Sharing: NO. Countries: 1. Publications: 0.
Psychological Interventions in Children After Road Traffic Accidents or Burns
ClinicalTrials.gov study NCT01085370. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluation of Reporting of Road Traffic Accidents With Drugs Responsible for Cognitive Side Effects (ERoADS)
ClinicalTrials.gov study NCT04480996. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prevention of Posttraumatic Stress Symptoms and Behavioral Problems in Children After Road Traffic Accidents: a Randomized Controlled Trial
ClinicalTrials.gov study NCT00296842. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Efficacy and Safety of Non-resistance Manual Therapy in Inpatients With Acute Neck Pain Caused by Traffic Accidents: a Randomised Controlled Trial
ClinicalTrials.gov study NCT04660175. IPD Sharing: NO. Countries: 1. Publications: 2.
NGII Data Set for Black Ice Traffic Accident Prediction
<p>NGII Data Set for Black Ice Traffic Accident Prediction</p> <p> </p> <p> </p>
Spatial Distribution and Cluster Analysis of Road Traffic Accidents in Nepal
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A spatial autocorrelation analysis of Road Traffic Accidents by severity using Moran's I spatial statistics: A study from Nepal 2019-2022
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.