Please note that 2024 data are incomplete and will be updated as additional records become available. Data are complete through 12/31/2023. Fatal and serious injury crashes are not “accidents” and are preventable. The City of Tempe is committed to reducing the number of fatal and serious injury crashes to zero. This data page provides details about the performance measure related to High Severity Traffic Crashes, as well as access to the data sets and any supplemental data. The Engineering and Transportation Department uses this data to improve safety in Tempe.This data includes vehicle/vehicle, vehicle/bicycle, and vehicle/pedestrian crashes in Tempe. The data also includes the type of crash and location. This layer is used in the related Vision Zero story map, web maps, and operations dashboard. Time ZonesPlease note that data is stored in Arizona time, which is UTC-07:00 (7 hours behind UTC) and does not adjust for daylight saving (as Arizona does not partake in daylight saving). The data is intended to be viewed in Arizona time. Data downloaded as a CSV may appear in UTC time and, in some rare circumstances and locations, may display online in UTC or local time zones. As a reference to check data, the record with incident number 2579417 should appear as Jan. 10, 2012, 9:04 AM.Please note that 2024 data are incomplete and will be updated as additional records become available. Data are complete through 12/31/2023.This page provides data for the High Severity Traffic Crashes performance measure. The performance measure page is available at 1.08 High Severity Traffic CrashesAdditional InformationSource: Arizona Department of Transportation (ADOT)Contact (author): Shelly SeylerContact (author) E-Mail: Shelly_Seyler@tempe.govContact (maintainer): Julian DresangContact (maintainer) E-Mail: Julian_Dresang@tempe.govData Source Type: CSV files and Excel spreadsheets can be downloaded from the ADOT websitePreparation Method: Data is sorted to remove license plate numbers and other sensitive informationPublish Frequency: semi-annuallyPublish Method: ManualData Dictionary

Dataset Card for US Accidents (2016 - 2021)

  Dataset Summary





  Description

This is a countrywide car accident dataset, which covers 49 states of the USA. The accident data are collected from February 2016 to Dec 2021, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by a variety of entities, such as the US and state departments of transportation, law enforcement agencies, traffic cameras, and… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/us-accidents.

Fatality Analysis Reporting System (FARS) was created in the United States by the National Highway Traffic Safety Administration (NHTSA) to provide an overall measure of highway safety, to help suggest solutions, and to help provide an objective basis to evaluate the effectiveness of motor vehicle safety standards and highway safety programs.

FARS contains data on a census of fatal traffic crashes within the 50 States, the District of Columbia, and Puerto Rico. To be included in FARS, a crash must involve a motor vehicle traveling on a trafficway customarily open to the public and result in the death of a person (occupant of a vehicle or a non-occupant) within 30 days of the crash. FARS has been operational since 1975 and has collected information on over 989,451 motor vehicle fatalities and collects information on over 100 different coded data elements that characterizes the crash, the vehicle, and the people involved.

FARS is vital to the mission of NHTSA to reduce the number of motor vehicle crashes and deaths on our nation's highways, and subsequently, reduce the associated economic loss to society resulting from those motor vehicle crashes and fatalities. FARS data is critical to understanding the characteristics of the environment, trafficway, vehicles, and persons involved in the crash.

NHTSA has a cooperative agreement with an agency in each state government to provide information in a standard format on fatal crashes in the state. Data is collected, coded and submitted into a micro-computer data system and transmitted to Washington, D.C. Quarterly files are produced for analytical purposes to study trends and evaluate the effectiveness highway safety programs.

Content

There are 40 separate data tables. You can find the manual, which is too large to reprint in this space, here.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.nhtsa_traffic_fatalities.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

This dataset was provided by the National Highway Traffic Safety Administration.

The dataset provides detailed statistics on traffic accident casualties in Qatar, categorized by driver's experience.

Fatal and serious injury crashes are not “accidents” and are preventable. The City of Tempe is committed to reducing the number of fatal and serious injury crashes to zero. This data page provides details about the performance measure related to High Severity Traffic Crashes as well as access to the data sets and any supplemental data. Click on the Showcases tab for visual representations of this data. The Engineering and Transportation Department uses this data to improve safety in Tempe.This page provides data for the High Severity Traffic Crashes performance measure. City of Tempe crash data summarized to show fatal and serious injury crashes by year.The performance measure dashboard is available at 1.08 High Severity Traffic CrashesAdditional Information Source: Arizona Department of Transportation (ADOT)Contact:  Julian DresangContact E-Mail:  Julian_Dresang@tempe.govData Source Type:  CSV files and Excel spreadsheets can be downloaded from ADOT websitePreparation Method:  Data is sorted to remove license plate numbers and other sensitive informationPublish Frequency:  MonthlyPublish Method:  ManualData Dictionary

The provided crash data comes directly from the standard DMV-349 Crash Form completed by the initial officer at the scene of a crash. Only completed crash reports will be mapped in this data. The coordinates for the crash reports are entered manually by the officer and may be subject to error. Therefore, only crashes with coordinates in Raleigh will be shown on the map.

Instructions for filtering data are available on the Open Data blog.

Follow this link to access the NC DOT DMV-349 Instruction Manual for code descriptions and definitions.https://connect.ncdot.gov/business/DMV/DMV%20Documents/DMV-349%20Instructional%20Manual.pdfUpdate Frequency: DailyTime Period: 2015-PresentTerms of UseThe Raleigh Police Department does not guarantee the accuracy of the information contained herein. While all attempts are made to ensure the correctness and suitability of information under our control and to correct any errors brought to our attention, no representation or guarantee can be made as to the correctness or suitability of the information that is presented, referenced, or implied. Data is provided by initial reports received and processed by the Raleigh Police Department. Data may be amended or corrected by the Raleigh Police Department at any time to reflect changes in the investigation, nature, or accuracy of the initial report and the Raleigh Police Department is not responsible for any error or omission, or for the use of or the results obtained from the use of this information. Misuse of the data may subject a party to criminal prosecution for false advertising under NC GS § 14-117. The Raleigh Police Department may, at its discretion, discontinue or modify this service at any time without notice.

The dataset provides detailed monthly statistics on traffic accident casualties in Qatar, categorized by month, location of the affected person, gender, and result of the accident.

Killed or Seriously Injured (KSI) Road Traffic Accidents Indicator: This indicator measures the percentage change in the number of people killed or seriously injured in road traffic accidents, based on a 3-year rolling average up to the current year. A positive figure indicates improved performance (i.e., a reduction in casualties compared to the previous 3-year period).

Performance Target: For comparability, performance is also assessed against a target to reduce KSI numbers by 40% over 10 years.

Scope: Includes people of all ages killed or seriously injured on the roads. Previously reported as NI 047.

Definitions:

Fatal Casualties: Deaths occurring within 30 days of the accident (excluding confirmed suicides). Seriously Injured Casualties: Injuries requiring hospitalisation or involving fractures, concussion, internal injuries, crushings, burns (excluding friction burns), severe cuts/lacerations, severe shock requiring medical treatment, or injuries causing death 30+ days after the accident. Slight Injuries: Excluded from totals. Includes sprains (e.g., whiplash), bruises, minor cuts, or slight shock requiring roadside attention.

Recording Practices: Police record injuries based on initial information, not medical examination. Hospitalisation practices vary regionally.

Systems Used: Police forces use either CRaSH (Collision Recording and Sharing) or COPA (Case Overview Preparation Application). Estimates are based on police-reported figures.

Data Comparability: Since 2016, changes in severity reporting systems affect comparability of serious and slight injury data. Both adjusted and unadjusted KSI statistics are available.

Further Information: Road Accidents and Considerations: Areas with low resident populations but high traffic inflows (e.g., City of London, rural tourist areas) may show artificially high rates. Heathrow Airport counts are included in London Region and England totals only.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Information on accident-prone areas for traffic accidents involving pedestrians aged 12 or younger - Target accidents: Traffic accidents that occurred within a year in which pedestrians aged 12 or younger were injured or killed - Conditions for selecting high-risk areas: Locations where three or more target accidents occurred within a 200m radius (two or more if fatal accidents are included) - API information in WMS format can be found through the Traffic Accident Information Open System (https://opendata.koroad.or.kr/api/sample.do). * This data is based on data from the Traffic Accident Analysis System (https://taas.koroad.or.kr).

This table will be archived effective 7/31/2021. However, crash data will continue to be updated in the following feature layer: https://data.tempe.gov/datasets/1-08-crash-data-report-detail/explore.Fatal and serious injury crashes are not “accidents” and are preventable. The City of Tempe is committed to reducing the number of fatal and serious injury crashes to zero. This data page provides details about the performance measure related to High Severity Traffic Crashes as well as access to the data sets and any supplemental data. The Engineering and Transportation Department uses this data to improve safety in Tempe.This data includes vehicle/vehicle, vehicle/bicycle and vehicle/pedestrian crashes in Tempe. The data also includes the type of crash and location.This page provides data for the High Severity Traffic Crashes performance measure. The performance measure dashboard is available at 1.08 High Severity Traffic CrashesAdditional InformationSource: Arizona Department of Transportation (ADOT)Contact (author): Shelly SeylerContact (author) E-Mail: Shelly_Seyler@tempe.govContact (maintainer): Julian DresangContact (maintainer) E-Mail: Julian_Dresang@tempe.govData Source Type: CSV files and Excel spreadsheets can be downloaded from ADOT websitePreparation Method: Data is sorted to remove license plate numbers and other sensitive informationPublish Frequency: semi-annuallyPublish Method: ManualData Dictionary

Content

The Motor Vehicle Collisions crash table contains details on the crash event. Each row represents a crash event. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.

Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Marc-Olivier Jodoin on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Information on accident-prone areas for pedestrian traffic accidents involving elderly pedestrians aged 65 or older ※ Conditions for selecting accident-prone areas 1) Accident-prone areas from 2012 to 2020 - (Target accident) Traffic accidents that occurred within a year in which elderly pedestrians aged 65 or older were injured or killed - (Conditions for selecting accident-prone areas) Areas with 3 or more target accidents within a 200m radius (2 or more if fatal accidents are included) 2) Accident-prone areas after 2021 - (Target accident) Traffic accidents involving elderly pedestrians aged 65 or older that occurred within the past 3 years in which elderly pedestrians were killed or seriously injured - (Conditions for selecting accident-prone areas) Areas with 5 or more target accidents within a 100m radius - API information in WMS format can be found through the Traffic Accident Information Open System (https://opendata.koroad.or.kr/api/sample.do). *The data is based on data from the Traffic Accident Analysis System (https://taas.koroad.or.kr).

ODS / Goals and targets (from the 2030 Agenda for Sustainable Development) / Goal 8. Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all / Target 8.8. Protect labour rights and promote safe and secure working environments for all workers, including migrant workers, in particular women migrants, and those in precarious employment / Indicator 8.8.1. Frequency rates of fatal and non-fatal occupational injuries, by sex and migrant status

Information on accident-prone areas for motorcycle traffic accidents - Target accidents: Traffic accidents involving two-wheeled vehicles (including motorcycles and ATVs) that resulted in death or serious injury in the past three years - Conditions for selecting high-risk areas: Locations where four or more target accidents occurred within a 100-meter radius - API information in WMS format can be found through the Traffic Accident Information Open System (https://opendata.koroad.or.kr/api/sample.do). * This data is based on data from the Traffic Accident Analysis System (https://taas.koroad.or.kr).

The dataset provides detailed statistics on traffic accident casualties in Qatar from 2014 to 2022, categorized by age groups, gender, and location of the injured.

the Ministry of Road Transport and Highways of the Government of India releases annual reports on road accidents and casualties in the country. Additionally, many state governments also release data on road accidents within their jurisdiction. There are many potential causes of road accidents, including: Distracted driving (e.g. using a cell phone, eating, or applying makeup while driving) Impaired driving (e.g. driving under the influence of alcohol or drugs) Reckless or aggressive driving (e.g. speeding, tailgating, or running red lights) Fatigue or drowsy driving Poor road conditions (e.g. potholes, debris, or lack of proper signage) Vehicle defects or malfunctions Poor weather conditions (e.g. rain, snow, or fog) Inadequate infrastructure (e.g. lack of proper lighting, median barriers, or guardrails) Pedestrian or bicycle errors Wildlife crossing the road. There are several datasets available on road accidents, depending on the country and region. Here are a few examples:

In the United States, the National Highway Traffic Safety Administration (NHTSA) provides data on vehicle crashes, including details such as the location, cause, and number of injuries and fatalities. The United Kingdom's Department for Transport provides data on reported road accidents, including information on the type of vehicle, number of casualties, and severity of injuries. The World Health Organization (WHO) also has a Global Status Report on Road Safety, which provides data on road accidents and fatalities for countries around the world. In India, the Ministry of Road Transport and Highways (MoRTH) provides annual data on road accidents and fatalities. The Global Road Safety Partnership (GRSP) also has a wealth of data on road accidents and fatalities, particularly in low- and middle-income countries. It is important to note that these datasets may have different data collection methodologies, and may not include all road accidents that have occurred.

The PeMS traffic datasets have been collected by the California Transportation (Caltrans) agency for 30-second granularity, and the raw and aggregated data are publicly available on their website (https://pems.dot.ca.gov/?dnode=Clearinghouse&type=meta&district_id=7&submit=Submit). We have gathered 5-minute aggregated vehicular traffic state (i.e traffic speed) dataset for district four and seven of California for 2022.

We have used Bing Distance Matrix API to compute a driving distance between each sensor. The API can be used to compute a driving distance between a single source or multiple sources and source or multiple destinations at once.

In addition, the weather datasets have been collected from https://www.visualcrossing.com/weather/weather-data-services and the datasets have one-hour granularity, and we have only removed some of the unnecessary columns.

The provided crash data comes directly from the standard DMV-349 Crash Form completed by the initial officer at the scene of a crash. Only completed crash reports will be mapped in this data. The coordinates for the crash reports are entered manually by the officer and may be subject to error. Therefore, only crashes with coordinates in Raleigh will be shown on the map.

Instructions for filtering data are available on the Open Data blog.

Follow this link to access the NC DOT DMV-349 Instruction Manual for code descriptions and definitions.https://connect.ncdot.gov/business/DMV/DMV%20Documents/DMV-349%20Instructional%20Manual.pdfUpdate Frequency: DailyTime Period: 2015-PresentTerms of UseThe Raleigh Police Department does not guarantee the accuracy of the information contained herein. While all attempts are made to ensure the correctness and suitability of information under our control and to correct any errors brought to our attention, no representation or guarantee can be made as to the correctness or suitability of the information that is presented, referenced, or implied. Data is provided by initial reports received and processed by the Raleigh Police Department. Data may be amended or corrected by the Raleigh Police Department at any time to reflect changes in the investigation, nature, or accuracy of the initial report and the Raleigh Police Department is not responsible for any error or omission, or for the use of or the results obtained from the use of this information. Misuse of the data may subject a party to criminal prosecution for false advertising under NC GS § 14-117. The Raleigh Police Department may, at its discretion, discontinue or modify this service at any time without notice.

Bus stops with total number of pedestrian accidents within 300ft.

Analysis of ‘Traffic accident interventions in interurban roads (Autonomous Police - Mossos d'Esquadra)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-analisi-transparenciacatalunya-cat-api-views-iaq4-5fyp on 08 January 2022.

--- Dataset description provided by original source is as follows ---

Intervencions en accidents de trànsit en via interurbana. La Policia de la Generalitat - Mossos d'Esquadra té la competència del control i vigilància del trànsit a Catalunya. En aquells municipis en què hi ha Policia local, aquesta s'ocupa de l'ordenació del trànsit urbà.

--- Original source retains full ownership of the source dataset ---