The number of road accidents per one million inhabitants in the United States was forecast to continuously decrease between 2024 and 2029 by in total 2,490.4 accidents (-14.99 percent). After the eighth consecutive decreasing year, the number is estimated to reach 14,118.78 accidents and therefore a new minimum in 2029. Depicted here are the estimated number of accidents which occured in relation to road traffic. They are set in relation to the population size and depicted as accidents per one million inhabitants.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of road accidents per one million inhabitants in countries like Mexico and Canada.

The number of road traffic fatalities per one million inhabitants in the United States was forecast to continuously increase between 2024 and 2029 by in total 18.5 deaths (+13.81 percent). After the tenth consecutive increasing year, the number is estimated to reach 152.46 deaths and therefore a new peak in 2029. Depicted here are the estimated number of deaths which occured in relation to road traffic. They are set in relation to the population size and depicted as deaths per 100,000 inhabitants.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of road traffic fatalities per one million inhabitants in countries like Mexico and Canada.

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 State of Michigan’s criteria for a crash is a motor vehicle that was in transport and on the roadway, that resulted in death, injury, or property damage of $1,000 or more. Traffic crashes in this dataset are derived from SEMCOG’s Open Data Portal. Each row in the dataset represents a traffic crash that includes data about when and where the crash occurred, road conditions, number of individuals involved in the crash, and various factors that apply to the crash (Train, Bus, Deer, etc.). Also included is the number of injuries and fatalities that are associated with the crash.

The Motor Vehicle Collisions person table contains details for people involved in the crash. Each row represents a person (driver, occupant, pedestrian, bicyclist,..) involved in a crash. The data in this table goes back to April 2016 when crash reporting switched to an electronic system. 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.

National Collision Database (NCDB) – a database containing all police-reported motor vehicle collisions on public roads in Canada. Selected variables (data elements) relating to fatal and injury collisions for the collisions from 1999 to the most recent available data.

AbstractThis dataset comprises detailed records of motor vehicle crashes occurring in Ohio, USA, from January 1, 2017, to December 31, 2023. Collected by law enforcement agencies using standardized OH-1 crash reporting forms and centralized by the Ohio Department of Public Safety, the dataset captures detailed information on 1,679,019 crashes involving 2,656,086 vehicles and 3,577,822 occupants. Structured across three levels—crash, vehicle, and occupant—the dataset includes attributes such as crash timing and location, environmental and road conditions, vehicle specifications, operational factors, occupant demographics, injury severity, safety equipment usage, and behavioral indicators like alcohol or drug involvement. Severity information is documented at both the crash and individual occupant levels, covering outcomes ranging from no injury to fatal incidents. The dataset features a total of 119 systematically named variables at the crash, vehicle, and occupant levels. A complete list of features, along with categorical value mappings, is provided in the accompanying documentation.Description of the data and file structureThis dataset contains comprehensive records of motor vehicle crashes reported across the state of Ohio, USA, from January 1, 2017, to December 31, 2023. The data were collected by law enforcement agencies using standardized crash reporting forms (OH-1) and centralized through the Ohio Department of Public Safety’s data systems.It captures detailed, structured information related to crash events, vehicles involved, and individuals affected. Each data sample corresponds to an occupant of a vehicle. There are unique identifiers for each crash and involved vehicle. Hence, the dataset is organized into three primary levels:Crash-Level Data: Includes unique identifiers for each of the 1,679,019 reported crashes, along with temporal details (date, time), location attributes, environmental conditions (e.g., weather, light, road surface), and overall crash characteristics (e.g., number of units involved, severity classification, work zone presence). The identifier for the crash is the feature “DocumentNumber”.Vehicle-Level Data: Comprises identifiers for each of the 2,656,086 vehicles (units) involved in a crash. Attributes include vehicle type, make, model, year of manufacture, vehicle defects, and operational details such as posted speed, traffic control devices, and pre-crash actions. Interacting vehicle types and hazardous material indicators are also documented. Vehicle-Level features are identified by the prefix ”Units.” in the feature name.Occupant-Level Data: Contains 3,577,822 records detailing individuals involved in crashes. This includes demographic information (age, gender), seating position, person injury severity, use of safety equipment (e.g., seat belts, airbags, helmets), and behavioral factors such as alcohol or drug involvement, distraction status, and test results where applicable. Occupant-Level features are identified by the prefix “Units.People.” in the feature name.The severity of the accident is also documented. The “CrashSeverity” feature document the severity of the crash in the following levels: Fatal (15021), Suspected Serious Injury (83764), Suspected Minor Injury (483026), Possible Injury (461019), and No Apparent Injury (2440823). Similarly, also individual people injury levels are recorded in the feature “Units.People.Injury”. The file "summary_2023_new.pdf" is a summary file that contains data analysis of the dataset (statistics and plots).There are 119 unique features in the data, and their complete list of name and type is reported below. Their categorical levels in case of integer-encoding is found in the file “mapping.yaml”.Access informationOther publicly accessible locations of the data:The full dataset submitted to figshare is not available elsewhere in its complete and curated form. However, data covering the most recent five years, including the current year, are publicly accessible through the following sources:Ohio Department of Public Safety Crash Retrieval Portal: https://ohtrafficdata.dps.ohio.gov/crashretrievalOhio Statistics and Analytics for Traffic Safety (OSTATS): https://statepatrol.ohio.gov/dashboards-statistics/ostats-dashboardsThese public portals provide access to selected crash data but do not include the full historical dataset or the cleaned, integrated, and reformatted version provided through this submission.Data was derived from the following sources:Ohio Department of Public SafetyHuman subjects dataThis dataset was derived entirely from publicly available traffic crash reports collected and disseminated by the Ohio Department of Public Safety through the Ohio Statistics and Analytics for Traffic Safety (OSTATS) platform.To ensure compliance with ethical standards for data sharing, this dataset contains no direct identifiers (e.g., names, addresses, license plate numbers, or VINs linked to individuals). All personal identifiers have been removed or were not included in the public dataset. Furthermore, the dataset contains no more than three indirect identifiers per record. These indirect identifiers (e.g., crash year, crash county, and age group) were selected based on their relevance to the study while minimizing re-identification risk.Where possible, continuous variables were converted to categories (e.g., age groups instead of exact age), and geographic detail was limited to broader regional indicators rather than precise location data. Data cleaning and aggregation procedures were conducted to further reduce identifiability while retaining the analytic value of the dataset for modeling injury risk across system domains.As described in the associated manuscript, all analyses were conducted on this de-identified dataset, and no additional linkage to identifiable information was performed. As such, this dataset does not require IRB oversight or data use agreements and is suitable for open-access publication under CC-BY licence.No direct interaction or intervention with human participants occurred during the creation of this dataset, and no personally identifiable information (PII) is included.Given the publicly available nature of the source data and the absence of PII, explicit participant consent was not required. However, by relying exclusively on open-access government data and following de-identification protocols aligned with the Common Rule (45 CFR 46), this dataset meets ethical standards for public data sharing.

This database that can be used for macro-level analysis of road accidents on interurban roads in Europe. Through the variables it contains, road accidents can be explained using variables related to economic resources invested in roads, traffic, road network, socioeconomic characteristics, legislative measures and meteorology. This repository contains the data used for the analysis carried out in the papers:

1. Calvo-Poyo F., Navarro-Moreno J., de Oña J. (2020) Road Investment and Traffic Safety: An International Study. Sustainability 12:6332. https://doi.org/10.3390/su12166332

2. Navarro-Moreno J., Calvo-Poyo F., de Oña J. (2022) Influence of road investment and maintenance expenses on injured traffic crashes in European roads. Int J Sustain Transp 1–11. https://doi.org/10.1080/15568318.2022.2082344

3. Navarro-Moreno, J., Calvo-Poyo, F., de Oña, J. (2022) Investment in roads and traffic safety: linked to economic development? A European comparison. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-022-22567

The file with the database is available in excel.

DATA SOURCES

The database presents data from 1998 up to 2016 from 20 european countries: Austria, Belgium, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Latvia, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden and United Kingdom. Crash data were obtained from the United Nations Economic Commission for Europe (UNECE) [2], which offers enough level of disaggregation between crashes occurring inside versus outside built-up areas.

With reference to the data on economic resources invested in roadways, deserving mention –given its extensive coverage—is the database of the Organisation for Economic Cooperation and Development (OECD), managed by the International Transport Forum (ITF) [1], which collects data on investment in the construction of roads and expenditure on their maintenance, following the definitions of the United Nations System of National Accounts (2008 SNA). Despite some data gaps, the time series present consistency from one country to the next. Moreover, to confirm the consistency and complete missing data, diverse additional sources, mainly the national Transport Ministries of the respective countries were consulted. All the monetary values were converted to constant prices in 2015 using the OECD price index.

To obtain the rest of the variables in the database, as well as to ensure consistency in the time series and complete missing data, the following national and international sources were consulted:

Eurostat [3]

Directorate-General for Mobility and Transport (DG MOVE). European Union [4]

The World Bank [5]

World Health Organization (WHO) [6]

European Transport Safety Council (ETSC) [7]

European Road Safety Observatory (ERSO) [8]

European Climatic Energy Mixes (ECEM) of the Copernicus Climate Change [9]

EU BestPoint-Project [10]

Ministerstvo dopravy, República Checa [11]

Bundesministerium für Verkehr und digitale Infrastruktur, Alemania [12]

Ministerie van Infrastructuur en Waterstaat, Países Bajos [13]

National Statistics Office, Malta [14]

Ministério da Economia e Transição Digital, Portugal [15]

Ministerio de Fomento, España [16]

Trafikverket, Suecia [17]

Ministère de l’environnement de l’énergie et de la mer, Francia [18]

Ministero delle Infrastrutture e dei Trasporti, Italia [19–25]

Statistisk sentralbyrå, Noruega [26-29]

Instituto Nacional de Estatística, Portugal [30]

Infraestruturas de Portugal S.A., Portugal [31–35]

Road Safety Authority (RSA), Ireland [36]

DATA BASE DESCRIPTION

The database was made trying to combine the longest possible time period with the maximum number of countries with complete dataset (some countries like Lithuania, Luxemburg, Malta and Norway were eliminated from the definitive dataset owing to a lack of data or breaks in the time series of records). Taking into account the above, the definitive database is made up of 19 variables, and contains data from 20 countries during the period between 1998 and 2016. Table 1 shows the coding of the variables, as well as their definition and unit of measure.

Table. Database metadata

Code

Variable and unit

fatal_pc_km

Fatalities per billion passenger-km

fatal_mIn

Fatalities per million inhabitants

accid_adj_pc_km

Accidents per billion passenger-km

p_km

Billions of passenger-km

croad_inv_km

Investment in roads construction per kilometer, €/km (2015 constant prices)

croad_maint_km

Expenditure on roads maintenance per kilometer €/km (2015 constant prices)

prop_motorwa

Proportion of motorways over the total road network (%)

populat

Population, in millions of inhabitants

unemploy

Unemployment rate (%)

petro_car

Consumption of gasolina and petrol derivatives (tons), per tourism

alcohol

Alcohol consumption, in liters per capita (age > 15)

mot_index

Motorization index, in cars per 1,000 inhabitants

den_populat

Population density, inhabitants/km2

cgdp

Gross Domestic Product (GDP), in € (2015 constant prices)

cgdp_cap

GDP per capita, in € (2015 constant prices)

precipit

Average depth of rain water during a year (mm)

prop_elder

Proportion of people over 65 years (%)

dps

Demerit Point System, dummy variable (0: no; 1: yes)

freight

Freight transport, in billions of ton-km

ACKNOWLEDGEMENTS

This database was carried out in the framework of the project “Inversión en carreteras y seguridad vial: un análisis internacional (INCASE)”, financed by: FEDER/Ministerio de Ciencia, Innovación y Universidades–Agencia Estatal de Investigación/Proyecto RTI2018-101770-B-I00, within Spain´s National Program of R+D+i Oriented to Societal Challenges.

Moreover, the authors would like to express their gratitude to the Ministry of Transport, Mobility and Urban Agenda of Spain (MITMA), and the Federal Ministry of Transport and Digital Infrastructure of Germany (BMVI) for providing data for this study.

REFERENCES

1. International Transport Forum OECD iLibrary | Transport infrastructure investment and maintenance.

2. United Nations Economic Commission for Europe UNECE Statistical Database Available online: https://w3.unece.org/PXWeb2015/pxweb/en/STAT/STAT_40-TRTRANS/?rxid=18ad5d0d-bd5e-476f-ab7c-40545e802eeb (accessed on Apr 28, 2020).

3. European Commission Database - Eurostat Available online: https://ec.europa.eu/eurostat/data/database (accessed on Apr 28, 2021).

4. Directorate-General for Mobility and Transport. European Commission EU Transport in figures - Statistical Pocketbooks Available online: https://ec.europa.eu/transport/facts-fundings/statistics_en (accessed on Apr 28, 2021).

5. World Bank Group World Bank Open Data | Data Available online: https://data.worldbank.org/ (accessed on Apr 30, 2021).

6. World Health Organization (WHO) WHO Global Information System on Alcohol and Health Available online: https://apps.who.int/gho/data/node.main.GISAH?lang=en (accessed on Apr 29, 2021).

7. European Transport Safety Council (ETSC) Traffic Law Enforcement across the EU - Tackling the Three Main Killers on Europe’s Roads; Brussels, Belgium, 2011;

8. Copernicus Climate Change Service Climate data for the European energy sector from 1979 to 2016 derived from ERA-Interim Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-energy-sector?tab=overview (accessed on Apr 29, 2021).

9. Klipp, S.; Eichel, K.; Billard, A.; Chalika, E.; Loranc, M.D.; Farrugia, B.; Jost, G.; Møller, M.; Munnelly, M.; Kallberg, V.P.; et al. European Demerit Point Systems : Overview of their main features and expert opinions. EU BestPoint-Project 2011, 1–237.

10. Ministerstvo dopravy Serie: Ročenka dopravy; Ročenka dopravy; Centrum dopravního výzkumu: Prague, Czech Republic;

11. Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2003/2004; Hamburg, Germany, 2004; ISBN 3871542946.

12. Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2018/2019. In Verkehrsdynamik; Flensburg, Germany, 2018 ISBN 9783000612947.

13. Ministerie van Infrastructuur en Waterstaat Rijksjaarverslag 2018 a Infrastructuurfonds; The Hague, Netherlands, 2019; ISBN 0921-7371.

14. Ministerie van Infrastructuur en Milieu Rijksjaarverslag 2014 a Infrastructuurfonds; The Hague, Netherlands, 2015; ISBN 0921- 7371.

15. Ministério da Economia e Transição Digital Base de Dados de Infraestruturas - GEE Available online: https://www.gee.gov.pt/pt/publicacoes/indicadores-e-estatisticas/base-de-dados-de-infraestruturas (accessed on Apr 29, 2021).

16. Ministerio de Fomento. Dirección General de Programación Económica y Presupuestos. Subdirección General de Estudios Económicos y Estadísticas Serie: Anuario estadístico; NIPO 161-13-171-0; Centro de Publicaciones. Secretaría General Técnica. Ministerio de Fomento: Madrid, Spain;

17. Trafikverket The Swedish Transport Administration Annual report: 2017; 2018; ISBN 978-91-7725-272-6.

18. Ministère de l’Équipement, du T. et de la M. Mémento de statistiques des transports 2003; Ministère de l’environnement de l’énergie et de la mer, 2005;

19. Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle

This dataset provides comprehensive statistics on recorded road accidents in Thailand, spanning from approximately 2019 to 2022. The data was sourced from raw information provided by the Office of the Permanent Secretary, Ministry of Transport, which is also utilized in this public dashboard for easier access and visualization. The dataset encompasses various aspects of road accidents and aims to shed light on the trends and patterns within this critical area of concern, analysis of this data could be crucial in guiding road safety policies and measures👍.

Cleaned and ready-to-use 3 Years of data points with total 81,735 rows.

Column	Description
acc_code	The accident code or identifier.
incident_datetime	The date and time of the accident occurrence.
report_datetime	The date and time when the accident was reported.
province_th	The name of the province in Thailand, written in Thai.
province_en	The name of the province in Thailand, written in English.
agency	The government agency responsible for the road and traffic management.
route	The route or road segment where the accident occurred.
vehicle_type	The type of vehicle involved in the accident.
presumed_cause	The presumed cause or reason for the accident.
accident_type	The type or nature of the accident.
number_of_vehicles_involved	The number of vehicles involved in the accident.
number_of_fatalities	The number of fatalities resulting from the accident.
number_of_injuries	The number of injuries resulting from the accident.
weather_condition	The weather condition at the time of the accident.
latitude	The latitude coordinate of the accident location.
longitude	The longitude coordinate of the accident location.
road_description	The description of the road type or configuration where the accident occurred.
slope_description	The description of the slope condition at the accident location.

Overview:

Information on location and characteristics of crashes in Queensland for all reported Road Traffic Crashes occurred from 1 January 2001 to 30 June 2024.

Fatal, Hospitalisation, Medical treatment and Minor injury:

This dataset contains information on crashes reported to the police which resulted from the movement of at least 1 road vehicle on a road or road related area. Crashes listed in this resource have occurred on a public road and meet one of the following criteria:

• a person is killed or injured, or
• at least 1 vehicle was towed away, or
• the value of the property damage meets the appropriate criteria listed below.

Property damage:

1. $2500 or more damage to property other than vehicles (after 1 December 1999)
2. $2500 or more damage to vehicle and/or other property (after 1 December 1991 and before 1 December 1999)
3. value of property damage is greater than $1000 (before December 1991).

Please note:

• This data has been extracted from the Queensland Road Crash Database.
• Information held in the Road Crash Database on events occurring within the last 12 months is considered preliminary as investigations into crashes can take up to 1 year to finalise.
• Property damage only crashes ceased to be reported/recorded by Queensland Police Service after 31 December 2010.
• These crash location coordinates reference the current Australian geodetic datum is GDA2020 (previously it was GDA94).

This dataset contains all traffic crashes reported to CSPD . This dataset may be tied to the Tickets and Citations dataset by ticket number.

The main source of the crash data is owned and maintained by the Virginia Department of Motor Vehicle (DMV). DMV’s Traffic Records Electronic Data System (TREDS) is a state-of-the-art data system maintained by the DMV Highway Safety Office (HSO) that automates and centralizes all crash data in Virginia. Per data sharing use agreement with DMV, VDOT publishes the non-privileged crash data through Virginia Roads data portal. In providing this data, VDOT assumes no responsibility for the accuracy and completeness of the data. In the process of recording and compiling the data, some deletions and/or omissions of data may occur and VDOT is not responsible for any such occurrences. The most recent data contained in this dataset is preliminary and subject to change.

Please be advised that, under Title 23 United State Code – Section 407, this crash information cannot be used in discovery or as evidence in a Federal or State court proceeding or considered for other purposes in any action for damages against VDOT or the State of Virginia arising from any occurrence at the location identified.

All users shall comply with and be subject to all applicable laws and regulations, whether federal or state, in connection with any of the receipt and use of DMV data including, but not limited to, (1) the Federal Drivers Privacy Protection Act (18 U.S.C. § 2721 et seq.), (2) the Government Data Collection and Dissemination Practices Act (Va. Code § 2.2-3800 et seq.), (3) the Virginia Computer Crimes Act (Va. Code § 18.2-152.1 et seq.), (4) the provisions of Va. Code §§ 46.2-208 and 58.1-3, and (5) any successor rules, regulations, or guidelines adopted by DMV with regard to disclosure or dissemination of any information obtained from DMV records or files.

This is a subset of a larger dataset. This dataset includes pedestrians and cyclists killed in traffic collisions in 2021.

The Motor Vehicle Collisions person table contains details for people involved in the crash. Each row represents a person (driver, occupant, pedestrian, bicyclist,..) involved in a crash. The data in this table goes back to April 2016 when crash reporting switched to an electronic system.

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.

This is the number of people of all ages killed or seriously injured (KSI) in road traffic accidents, in an area, adjusted. This indicator includes only casualties who are fatally or seriously injured and these categories are defined as follows:

Fatal casualties are those who sustained injuries which caused death less than 30 days after the accident; confirmed suicides are excluded.

Seriously injured casualties are those who sustained an injury for which they are detained in hospital as an in-patient, or any of the following injuries, whether or not they are admitted to hospital: fractures, concussion, internal injuries, crushings, burns (excluding friction burns), severe cuts and lacerations, severe general shock requiring medical treatment and injuries causing death 30 or more days after the accident.

An injured casualty is recorded as seriously or slightly injured by the police on the basis of information available within a short time of the collision. This generally will not reflect the results of a medical examination, but may be influenced according to whether the casualty is hospitalised or not. Hospitalisation procedures will vary regionally.

Slight injuries are excluded from the total, such as a sprain (including neck whiplash injury), bruise or cut which are not judged to be severe, or slight shock requiring roadside attention.

Police forces use one of two systems for recording reported road traffic collisions; the CRaSH (Collision Recording and Sharing) or COPA (Case Overview Preparation Application). Estimates are calculated from figures which are as reported by police. Since 2016, changes in severity reporting systems for a large number of police forces mean that serious injury figures, and to a lesser extent slight injuries, are not comparable with earlier years. As a result, both adjusted and unadjusted killed or seriously injured statistics are available. Further information about the reporting systems can be found here.

Areas with low resident populations but have high inflows of people or traffic may have artificially high rates because the at-risk resident population is not an accurate measure of exposure to transport. This is likely to affect the results for employment centres e.g. City of London and sparsely populated rural areas which have high numbers of visitors or through traffic. Counts for Heathrow Airport are included in the London Region and England totals only.

From the publication of the 2023 statistics onwards, casualty rates shown in table RAS0403 to include rates based on motor vehicle traffic only. This is because the department does not consider pedal cycle traffic to be robust at the local authority level.

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

People killed or seriously injured (KSI) is used as the metric for roadway safety rather than simply fatalities because fatalities alone tend to be random in nature and can obscure long-term trends. Including serious injuries makes the data more robust and better highlights how the region is doing at preventing serious vehicle crashes. This approach has been promoted by the FHWA and embraced by both NJDOT and PennDOT. Because KSI can fluctuate from year to year, five-year rolling averages are used to identify trends, as seen in the first chart. The data separates pedestrians and bicyclists from motor vehicle occupants because these users are more vulnerable to death or serious injury when involved in a crash. Data for motor vehicle and combined bicyclist and pedestrian KSI can be looked at as a raw total, normalized based on population (per capita), or based on vehicle miles driven (per VMT).

Each year, transit agencies have to fulfill the Federal Transit Agency’s (FTA) TPM requirements by reporting data to the FTA’s National Transit Database (NTD) on passengers who are killed and injured (regardless of severity) on their services, employees who are injured at work, and safety events. Transit fatalities are defined as deaths confirmed within thirty days, excluding deaths from trespassing and suicide. SEPTA includes fatalities from trespassers and suicides in their TPM reporting and target setting, while New Jersey Transit and PATCO do not. To use consistent data for all three transit agencies, trespassing deaths and suicides are included in this analysis. Transit injuries are defined as harm to a person which requires immediate medical attention away from the scene. While crime-related injuries are reported to the NTD, they are excluded from the injury performance target. As with fatalities, these are included in the analysis for data consistency. The third table below shows employee injuries per 200,000 work hours, which is also a TPM requirement. Major safety events include collisions, derailments, fires, hazardous material spills, or evacuations. Major security events are excluded from this analysis, per federal guidance.

See our: Crash Analysis System (CAS) data user guide

This data comes from the Waka Kotahi Crash Analysis System (CAS), which records all traffic crashes reported to us by the NZ Police. CAS covers crashes on all New Zealand roadways or places where the public have legal access with a motor vehicle.

The data updates monthly, in the first week of each month.

Data is currently available from 1 January 2000. The dataset includes crash variables that are non-personal data.

To give you a quick overview of the data, see the charts in the ‘Attributes’ section below. These will give you information about each of the attributes (variables) in the dataset.

Each chart is specific to a variable, and shows all data (without any filters applied).

Crash Analysis System data - field descriptions

Data reuse caveats: we’ve taken reasonable care in compiling this information, and provide it on an ‘as is, where is’ basis. We're not liable for any action taken on the basis of the information. For further information see the terms of the CC-BY 4.0 International license.

CC-BY 4.0 International licence details

Variables in the dataset are formatted for analytical use. This can result in attribute charts that may not appear meaningful, and are not suitable for broader analysis or use. In addition, some variables aren't mutually exclusive – do not consider them in isolation.

You must not take and use these charts directly as analysis of the overall data.

Data quality statement: we aim to process all fatal crashes within one working day of receiving the crash report from NZ Police.

We aim to process all injury crashes (serious and minor injury) within 4 weeks of receiving the crash report.

It may take up to seven months for non-injury crashes to be processed into CAS.

Up-to-date information on current number of outstanding crash reports

Most unprocessed crash reports will be for crashes where there weren’t any injuries.

Data quality caveats: this data comes from the road traffic crash database Crash Analysis System (CAS) version 2.1.0. As the data is live, data can sometimes change after we receive it – that is, the data is not static after we publish it.

Waka Kotahi NZ Transport Agency maintains the Crash Analysis System. This open data is an appropriately confidentialised version of that.

After a crash, NZ Police send us a Traffic Crash Report (TCR). This may not happen immediately.

A crash must have happened on a road to be recorded in CAS. The CAS definition of a road is any street, motorway or beach, or a place that people can access with a motor vehicle.

There is a lag between the time of a crash to CAS having full and correct crash records. This is due to the police reporting time frame, and data processing.

People don’t report all crashes to the NZ Police. The level of reporting increases with the severity of the crash.

Crash severity is the severity of the worst injury in the crash. There may be more than one injury in a crash.

2020 and 2021 data is incomplete.

For API explorer users, there is a known issue with number-based attribute filters where the “AND” operator is used instead of the “BETWEEN” operator. Substituting “BETWEEN” for “AND” manually in the query URL will resolve this.Update 13/07/2021: previously, there was a 5 month buffer between our internal CAS data and our CAS open data. We have reduced this buffer to 1 month, due to user demand and improved systems.Update 10/12/2020: field type change. The field type for ‘crashFinancialYear’ has changed from integer to text.

Road accidents are accidents in which persons have been killed or injured as a result of driving on public roads and places or where property damage has occurred. The map shows only accidents involving personal injury. Accidents in which only material damage occurred are not shown. The accident atlas contains information from road accident statistics based on reports from police departments. Accidents to which the police have not been called are not included in the statistics. Before the accident coordinates recorded by the police are summarised on the basis of road sections and presented as points in the accident atlas, they must undergo a multi-stage plausibility process. During this process, individual accidents that do not meet plausibility requirements can be sorted out. These accidents are not depicted in the accident atlas. Killed: People who died of the accident within 30 days Severely injured: Persons admitted directly to a hospital for inpatient treatment (at least 24 hours) Lightly injured: all other injured Accident with goods vehicle (GKFZ): Accident involving at least one truck with a normal body and a total weight exceeding 3.5 tonnes, a tanker or special body truck, a tractor unit or other tractor unit (this category is included in “accident with others” in 2016 and 2017) Further information: https://unfallatlas.statistikportal.de/_opendata2021.html Link to the interactive application: https://unfallatlas.statistikportal.de

The Crash Data On California State Highways Report is produced by the California Department of Transportation (Caltrans) to provide high level summaries of road miles, travel, crashes and crash rates on the California State Highway System.

This table lists statewide vehicle travel expressed in Million Vehicle Miles (MVM), road miles, and one and three year crash rates and fatality rates based on lane types and population codes.

While crash rates for total crash and fatal + injury crashes are calculated per MVM, fatality rates are expressed per 100 MVM.