The statistic shows the prevalence of deaths or injuries in road traffic crashes in the United States per 100,000 people in 2016, with a breakdown by sex, age and degree of severity. Male passenger vehicle occupants aged between 21 and 24 had a fatality rate of **.

Official statistics of road accidents in 2016, divided by time slot, type of vehicle involved and severity, by sex, by age, by place, by infractions, by days

Statistical distribution of injured car accidents in Taoyuan City in 2016 by driver age.

The statistic shows the number of vehicle occupants injured in road traffic crashes in the United States by gender in 2016. In that year, around *** million males were injured in U.S. road traffic crashes. Motor vehicle crashes are the leading cause of death among those under the age of ** in the United States.

Data on road accidents in 2016 by category. Each .csv file contains differentiated data regarding age, accident severity, location, user, weather conditions, etc.

Age-adjusted rate of deaths from motor vehicle traffic injuries by sex, race/ethnicity, age; trends if available. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017; U.S. Census Bureau; 2010 Census, Tables PCT12, PCT12H, PCT12I, PCT12J, PCT12K, PCT12L, PCT12M; generated by Baath M.; using American FactFinder; Accessed June 20, 2017. METADATA:Notes (String): Lists table title, notes and sourcesYear (String): Year of data; presented as single year or pooled years (2012 to 2016)Category (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and Female, race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only); age categories as follows: 0 to 17, 18 to 44, 45 to 64, 65+; <1, 1 to 4, 5 to 14, 15 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, 65 to 74, 75 to 84, 85+; United States and Healthy People 2020 targetRate per 100,000 people (Numeric): Rate of deaths from motor vehicle traffic injuries. Rates for age groups are reported as age-specific rates per 100,000 people. All other rates are age-adjusted rates per 100,000 people.

This study enrolled children ages 7 to 15 who received medical care at the hospital after an acute traffic accident or burn injury, and up to two parents/caregivers per child. Within 1 month of injury, and at 3 months, and 6 months post-injury, children and parents were assessed for posttraumatic stress symptoms (PTSS) and depression. Parents also completed measures of their own anxiety symptoms and of child behavior and health-related quality of life. The study aimed to achieve a better understanding of dysfunctional trauma-related cognitions considering child and environmental factors in a cross-sectional and a longitudinal design.

The statistical data of traffic accidents leading to death in Taoyuan City in 2016 involving children under the age of 14

In 2021, more than 44,000 male drivers were involved in fatal crashes in U.S. road traffic, which accounted for 72.3 percent of the total, while female drivers were involved in about 15,100 fatal crashes. The number of drivers who were involved in fatal crashes has shown an increase of about 16.2 percent from 2016.

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.

Statistics of children under the age of 14 injured in car accidents in Taoyuan City in 2016.

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

Data refers to State/UT wise accidents Classified according to various parameters i.e Educational Qualifications of Drivers, Type of Junctions, Type of Traffic Control, Nature of Surface Pot Holes, type of location, Time of occurrence, Type of Weather Condition, type of vehicles and objects primarily responsible, Age of Vehicles, type of Manoeuvre, responsibility of driver, vehicular defect, road condition, road features etc. Data contains information like total Number of Road Accidents, number of Persons Killed and Injured in various road accidents.URL of data source: https://data.gov.in/resources/state-ut-wise-accidents-classified-according-nature-accidents-during-2014-and-2016Published on Data Portal: 13/01/2017This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

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.

This table contains information about Dutch residents who died in the given year due to an accident (mainly traffic accidents, accidental falls, drowning or poisoning). The accident may have taken place abroad and/or in the previous year, the date and place of death is the criterion. The data is split by sex and age of the victim. The ICD-10 codes that belong to accidents are V01-W77, W80-X59, Y10-Y19, Y34 en Y85-Y86. The persons who died in the MH17 crash in 2014 are not categorised as an accident with an airplane, but as an operation of war (ICD-10 code Y36). These persons are not part of this accidents table.

Data available from: 1996

Status of the figures: The figures up to and including 2016 are final and the figures for 2017 are provisional.

Changes as of September 6th 2018: Provisional figures for 2017 have been added.

Changes as of August 30th 2016: - The 'victim's way of participation in traffic' for 'mopeds' for all presented years are split into 'mopeds and light mopeds' and 'motorised disabled vehicle'. - The former numbers contained also codes which are not included in fatal accidents. Amongst these codes were some causes of death where the intention was undetermined (ICD-10 codes Y20-Y33) and some sequelae (ICD-10 codes Y87-Y89). These causes of death are now excluded. This has consequences for all years for the number of deaths with the cause 'other including sequelae' and this effects all concerning totals and relative figures. The numbers fluctuate per year between 9 in 2008 and 55 in 1998. - The numbers in the cause of poisoning columns 'medicine' and 'illicit drugs' were switched for all years starting in 2014 with the making of this new table. This has been corrected.

Changes as of December 12th 2014: Since 2013 Statistics Netherlands is using IRIS for automatic coding for cause of death. This improved the international comparison of the data. The change in coding did cause a considerable shift in the statistic. Since 2013 the (yearly) ICD-10 updates are applied. For accidents no changes in coding have taken place however.

When will new figures be published: In the fourth quarter of 2018 provisional figures for 2017 will be made final.

This dataset includes accidents with Google, Uber, Tesla, and Waymo autonomous cars. Accordingly, the inventory of accidents involvs highly automated vehicles. The database comprises 40 accidents from all over the world, which occurred between 2016 and 2021, and consists of the following fields: • year: year of accident 1..2100; • month: month of accidents (1..12); • day: day of the accident (1..31) • hour: hour of the accident (0:00..23:59) • period of the day: hour of the accident (0,00..23,59) • country of the accident: e.g. USA, China, etc. • GPS coordinates: e.g. 39°18′N 116°42′E • state: e.g. Florida • state: e.g. Florida • description: e.g. 23-year-old Gao Yuning was killed when his Tesla, with Autopilot mode engaged, slammed into the back of a stationary road sweeping truck parked at the edge of the road. • death: number of fatal injuries of the accident • • Serious injury: number of seriously injured persons related to the accident • slight injury: number of slight injury related to the accident • Uber driver: number of Uber driver involved • Total number of vehicles: number of vehicles involved • Environment: flat, elevating, mountain • Environment code: flat-1, elevating-2, mountain-3 • Period of the day :Day, evening, night • visibility: 1 clear, 0 not clear • Weather condition: rainy, snow, sunny, haze • season: summer-1, spring-2, autumn-3, winter-4 • season rate: summer-1/4, spring-2/4, autumn-3/4, winter-4/4 • speed limit: the regular speed limit at the location of the accident • speed condition of highly automated vehicle: the actual velocity of the investigated highly automated vehicle • Normalization of speed: normalized value of speed condition • Model of highly automated vehicle: the name of the model of the investigated highly automated vehicle • Autopilot mode:yes-1, no-0 • Age of highly automated vehicle: number of years from manufacturing the highly automated vehicle • Type of accident: frontal, rear-end collision, sideswipe collisions, chain-reaction collision • curvature: straight, in curve • Total number of vehicles: number of vehicles involved • Technical reasons: brief description, introducing the causes • Sources: web link

In 2022, the number of deaths due to road accidents in India among victims between 25 to 35 years amounted to nearly **** thousand, the most compared to other age groups. That year, there were over 169 thousand accidental fatalities across the south Asian country. Over-speeding was the leading contributor of accidents. Combined, state and national highways recorded around 258 thousand road accidents in 2022. This number had dropped significantly in 2016, before increasing again in recent years.

Accident demographics

The Indian subcontinent ranked first in terms of road accident deaths according to the World Road Statistics which comprised of *** countries. A majority of victims were two-wheeler commuters. Additionally, pedestrians made up a high share of victims as well, reflecting the lack of infrastructure, be it improper footpaths and the lack of foot-over bridges or negligence of traffic rules. About ** percent of the road accidents in India accounted for about *** percent of the global road traffic accidents.

Accident prevention

Poor enforcement of fines, in addition to mild punishments and corruption encourages drivers, especially among young Indians, to engage in rash driving. Accident awareness programs were initiated by the government among the motorists, along with the National Road Safety Policy to encourage safe transport, strict enforcement of safety laws and fines and establishment of road safety database.

In the United States, deaths among pedestrians have increased dramatically since 2009 relative to other vulnerable road users, with substance use described as an important risk factor. This study aimed to explore blood alcohol concentrations (BAC g/dL) among pedestrian fatalities in the United States between 2016 and 2020. Exploring the presence of alcohol among pedestrian cases will support targeted interventions designed to reduce risk. This study utilized pedestrian fatality and alcohol screening data provided by the Fatality Analysis Reporting System (FARS). Logistic models were examined to identify statistical associations (ORs, 95% CI) by age, race, and sex relative to positive BAC exposure (BAC > 0.0 g/dL), mild exposure (BAC > 0.0 < .079 g/dL), moderate to severe alcohol exposure (BAC 0.08–.299 g/dL), and severe exposure (BAC ≥ 0.30 g/dL). Between 2016 and 2020, 33,375 pedestrian fatalities were reported to FARS with 75.1% of cases retained for analysis (n = 25,077). Fatalities were more likely to be White (69.3%), male (69.9%), and between 25-64 years of age (67.3%). 74.0% of fatalities were tested for alcohol, with 40.9% screening positive. Females, cases ≥ 75 years of age, and those identified as Asians reported the lowest odds of being positive for alcohol exposure. Results suggest an ongoing threat to pedestrians due to alcohol consumption and that exposure odds vary by demographic characteristics. Unfortunately, analytical approaches to understanding the roles played by drugs and alcohol among vulnerable road users tend to be marginalized in the literature. Analytical, evidence-based investigations are needed to curtail the risk of pedestrian fatalities in the U.S.

Ensuring social data's reliability is essential in accurately evaluating social and economic impacts across geographical locations, economic sectors and stakeholder categories. Yet, the MRIO model utilized in our research (EXIOBASE) was hindered by out-of-date or significantly proxy fatality statistics, causing potential inaccuracies in our findings. We have comprehensively revised EXIOBASE fatality data to address this shortcoming, incorporating detailed, nation-specific, and up-to-date data. The update includes work-related fatal occupational injuries as well as fatalities associated with occupational exposure to a variety of 17 hazardous substances and conditions such as asbestos, arsenic, benzene, beryllium, cadmium, chromium, diesel engine exhaust, formaldehyde, nickel, polycyclic aromatic hydrocarbons, silica, sulfuric acid, trichloroethylene, asthmagens, particulate matter, gases and fumes, noise and ergonomic factors. Our methodological process is built on three pillars: data acquisition, raw data processing, and computation of fatal injuries by country, gender, year, and EXIOBASE economic sector.

Data were sourced from the World Health Organization (WHO) (Pega et al., 2021) and Eurostat databases (Publications Office of the European Union, 2013). The WHO data was carefully screened based on specific criteria such as age above 15 years, gender, and fatal injuries only. Eurostat data provided granular information on work-related fatalities, classified by economic activities in the European Community (or NACE Rev.2 (Eurostat, 2008)). The WHO provided aggregate fatality data for 2010 and 2016. The strategy for allocating these deaths across Eurostat categories depended on the countries' geographical location, with different methods applied to European and non-European nations.

For European nations, fluctuations in fatality numbers within a NACE Rev.2 sector mirrored the changes registered by Eurostat. For non-European countries, fatality figures were proportionally allocated across economic sectors split according to the NACE Rev.2 classification, reflecting the workforce size associated with each economic sector. Due to the scarcity of data for nations within Asia, America, or Africa, we adopted a regional approach, computing fatality ratios over each NACE Rev.2 category for each region by integrating data for available countries over a reference year. For 2010 and 2016, the aggregate fatality figures for nations within these three zones were established. Due to the temporal proximity of both reference years, we postulated a linear trend in the fatality count between these two years. The number of fatalities for a specific country, year, and per NACE Rev.2 activity was then calculated by applying the previously mentioned fatality ratio to the total number of deaths for that nation. Last, we applied the European annual ratios to their total mortality figures for the few countries that could not be classified as European or belonging to one of the aforementioned zones.

The result is a comprehensive database that includes the number of fatalities (expressed in the number of deaths for work-related fatal occupational injuries and in Disability-adjusted life years (DALYs), for fatalities associated with occupational exposure to a specific risk factor), detailed at the country, gender, and NACE Rev.2 sector levels from 2008 to 2019, providing insights into work-related fatal injuries across different health effects and geographical regions.

Nomenclature

Archives:

• Concordance_ISIC_Exiobase.xlsx : Concordance between the International Standard Industrial Classification (ISIC) and the exiobase sectors
• Concordance_ISO3_EXIO3.xlsx - Concordance between the ISO3 code and the Exiobase regions
• Workforce_by_ISO3.csv - Number of active persons per Country (ISO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Year (from 1991 to 2021)
• Workforce_by_EXIO3.csv - Number of active persons per Exiobase region (EXIO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Year (from 1991 to 2021)
• Death_ISO3.csv - Number of death per Country (ISO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)
• Death_EXIO3.csv - Number of death per Exiobase Region (EXIO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)
• Death_EXIO3_region_exiobase_sector.csv - Number of death per Exiobase Region (EXIO3 code), exiobase sector, Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)
• Injuries_ISO3.zip - Archive of DALY per Country (ISO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Type of Exposure, Year (from 2009 to 2019)
• Injuries_EXIO3.zip - Archive of DALY per Exiobase Region (EXIO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Type of Exposure, Year (from 2009 to 2019)
• Workforce_EXIO3_sector_exiobase.xlsx - Number of active persons per Exiobase region (EXIO3 code), per exiobase sector, Sex, Year (from 1991 to 2021).
  This file is available here : Berthet, Etienne and Lavalley, Julien and Anquetil-Deck, Candy and Ballesteros, Fernanda and Stadler, Konstantin and Soytas, Ugur and Hauschild, Michael and Laurent, Alexis, Assessing the Social and Environmental Impacts of Critical Mineral Supply Chains for the Energy Transition in Europe. Available at SSRN: https://ssrn.com/abstract=4610350 or http://dx.doi.org/10.2139/ssrn.4610350

Content of Injuries_*.zip:

• arsenic_*.csv
• asbestos_*.csv
• asthmagens_*.csv
• benzene_*.csv
• beryllium_*.csv
• cadmium_*.csv
• chromium_*.csv
• diesel_*.csv
• ergonomic_*.csv
• formaldehyde_*.csv
• gases_*.csv
• nickel_*.csv
• noise_*.csv
• polycyclic_*.csv
• silica_*.csv
• sulfuric_*.csv
• trichloro_*.csv

Abstract copyright UK Data Service and data collection copyright owner.Northern Ireland Police Recorded Injury Road Traffic Collision Data (prior to 2010 known as the Northern Ireland Road Traffic Collision Data) are derived from information recorded by the Police Service of Northern Ireland (PSNI) in relation to collisions reported to them. The information is captured on a collision report form (CRF) and while there are slight differences between this form and the Department for Transport’s STATS19 form (used in England, Scotland and Wales), the vast majority of the information sought is the same. The main aim of collecting and publishing road traffic collision statistics in Northern Ireland is to provide a basis for assisting the police and government to determine and monitor effective road safety policies to reduce the number of people killed and injured on the roads. Users should note that the data collection form changed format on 1st April 2007. Further information is available in the documentation. Data changes have also taken place over time: from 2011, individual age was replaced with age group. From 2014, the reference id variable changed from string format to a sequential reference number. Further information about the series is available on the PSNI Road Traffic Collision Statistics webpage, alongside the latest User Guide to Police Recorded Injury Road Traffic Collision Statistics in Northern Ireland. Main Topics:The data are divided into three files: Collision: The circumstances of the collisions - details include the collision severity, number of vehicles and casualties involved, time and location, weather and road conditions, and carriageway hazards. Vehicle: Vehicles involved in each collision - details include vehicle type, manoeuvre at time of collision, and data about the driver (age, sex). Casualty: Casualties resulting from a collision - details include age, sex, injury severity and whether a driver, passenger, pedestrian or cyclist.