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.For the most accurate, up to date statistics on traffic fatalities, please refer to the NYPD Motor Vehicle Collisions page (updated weekly) or Vision Zero View (updated monthly).

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 dataset contains crash information from the last five years to the current date. The data is based on the National Incident Based Reporting System (NIBRS). The data is dynamic, allowing for additions, deletions and modifications at any time, resulting in more accurate information in the database. Due to ongoing and continuous data entry, the numbers of records in subsequent extractions are subject to change.About Crash DataThe Cary Police Department strives to make crash data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. As the data is updated on this site there will be instances of adding new incidents and updating existing data with information gathered through the investigative process.Not surprisingly, crash data becomes more accurate over time, as new crashes are reported and more information comes to light during investigations.This dynamic nature of crash data means that content provided here today will probably differ from content provided a week from now. Likewise, content provided on this site will probably differ somewhat from crime statistics published elsewhere by the Town of Cary, even though they draw from the same database.About Crash LocationsCrash locations reflect the approximate locations of the crash. Certain crashes may not appear on maps if there is insufficient detail to establish a specific, mappable location.

Attributes about each crash case which may consist of a single or multiple reports occurring during the year.

The Motor Vehicle Collisions vehicle table contains details on each vehicle involved in the crash. Each row represents a motor vehicle 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 dataset provides information on motor vehicle operators (drivers) involved in traffic collisions occurring on county and local roadways. The dataset reports details of all traffic collisions occurring on county and local roadways within Montgomery County, as collected via the Automated Crash Reporting System (ACRS) of the Maryland State Police, and reported by the Montgomery County Police, Gaithersburg Police, Rockville Police, or the Maryland-National Capital Park Police. This dataset shows each collision data recorded and the drivers involved. Please note that these collision reports are based on preliminary information supplied to the Police Department by the reporting parties. Therefore, the collision data available on this web page may reflect: -Information not yet verified by further investigation -Information that may include verified and unverified collision data -Preliminary collision classifications may be changed at a later date based upon further investigation -Information may include mechanical or human error This dataset can be joined with the other 2 Crash Reporting datasets (see URLs below) by the State Report Number. * Crash Reporting - Incidents Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Incidents-Data/bhju-22kf * Crash Reporting - Non-Motorists Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Non-Motorists-Data/n7fk-dce5 Update Frequency : Weekly

The Traffic_Crashes feature class models roadway traffic crashes within the Pikes Peak Area Council of Governments (PPACG) region, typically over a five-year period. This data is developed from crash data records compiled by CDOT from police accident reports filed with the state by each jurisdiction in the region. CDOT consolidates the police reports into a single database, removes personal identifying information, and then provides PPACG with an exported table of crash data records. The majority of the crash records (greater than 99.99%) are successfully linear referenced to routes or geocoded to address information acquired from CDOT, the El Paso-Teller County E911 Authority, and Park County. All crash records associated with roadways modeled in PPACG's Route Centerlines feature class are further refined by PPACG to align with coincident roadway network links and intersections.The 'Crash_ID' field stores the unique ID value for each feature in this data set, and is derived as a concatenation of the crash year and the 'CUID' CDOT crash database identifier field. The 'CUID' field is provided by CDOT in their crash data deliverable, and may not be unique. The 'RouteCL_ID' field, the ‘NetLink_ID' field, and the 'Intrsct_ID' fields record the foreign keys for any related features in PPACG's respective Route Centerlines, Roadway Network Links, and Roadway Network Intersections feature classes. The ‘Severity’ field categorizes each crash according to the severity of injuries or resultant fatalities, if any, as reported in the crash data. The 'Date_Time' field stores the concatenated crash date and crash time from each report into a single date field, while the 'Day' field records the day of the week of each crash. The 'DriveImp_1', 'DriveImp_2', and 'NM_Imp' fields identify if the drivers or non-motorist involved in each crash was suspected of drug or alcohol impairment according to the crash record, which may have included field sobriety testing. Those fields are not updated with the results of any further tests or other conclusive measures that may have been performed after the crash report was filed. All remaining fields are derived directly from CDOT crash data records, altered by PPACG to provide descriptive and consistent field names and aliases that still logically reference the original CDOT crash data schema, and primarily describe the location, roadway features, conditions, vehicle information, and driver actions documented with each crash record.

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

In 2024, approximately 290.9 thousand road traffic accidents were recorded in Japan, down from about 536.9 thousand in 2015. The figure continued its downward trend to reach its lowest recorded value during the past decade. Traffic safety as national policy in Japan Since the 1970s, the Japanese government has announced a new Fundamental Traffic Safety Program every five years (11th program: FY 2021 to FY 2025). The government’s ultimate goal is to achieve a society without traffic accidents. The numbers of recent years seem to be encouraging: not only have they decreased overall, but numbers have also reduced relative to population size, the number of registered vehicles, or the distance traveled. Consequently, Japan has one of the lowest ratios of traffic fatalities per capita in international comparison. However, the rapidly aging society impacts the case fatality rate, which has increased in the past decade. Therefore, if people get hurt in accidents, they have become more likely to suffer death. This problem holds especially true for the elderly, the only age group whose risk has increased. Circumventing all too human errors Since driving is a complex action, people are likely to neglect safety precautions (consciously or unconsciously). However, by improving the traffic environment, one can prevent risky situations. In this regard, systems like ADAS (advanced driver assistance system) have contributed enormously. New technologies ‘connect’ the automobile to its surroundings - whether it is the road infrastructure, other vehicles, cloud systems, or pedestrians - progressing more and more in the field of autonomous driving.While the connected car emphasizes the automobile itself, the Intelligent Transport System (ITS), for example, focuses on the infrastructure itself. Besides car navigation systems, the ITS in Japan includes the Vehicle Information and Communication System (VICS) and the Electronic Toll Collection (ETC). The ETC, which is more than a mere charging device, bi-directionally communicates data between the vehicle and the road infrastructure. The improved ETC 2.0 system is likely to become the new standard, thereby paving the road for big data technology. These kinds of advances contribute to mitigating accidents.

A. SUMMARY This table contains all victims (parties who are injured) involved in a traffic crash resulting in an injury in the City of San Francisco. Fatality year-to-date crash data is obtained from the Office of the Chief Medical Examiner (OME) death records, and only includes those cases that meet the San Francisco Vision Zero Fatality Protocol maintained by the San Francisco Department of Public Health (SFDPH), San Francisco Police Department (SFPD), and San Francisco Municipal Transportation Agency (SFMTA). Injury crash data is obtained from SFPD’s Interim Collision System for 2018 to YTD, Crossroads Software Traffic Collision Database (CR) for years 2013-2017 and the Statewide Integrated Transportation Record System (SWITRS) maintained by the California Highway Patrol for all years prior to 2013. Only crashes with valid geographic information are mapped. All geocodable crash data is represented on the simplified San Francisco street centerline model maintained by the Department of Public Works (SFDPW). Collision injury data is queried and aggregated on a quarterly basis. Crashes occurring at complex intersections with multiple roadways are mapped onto a single point and injury and fatality crashes occurring on highways are excluded. The crash, party, and victim tables have a relational structure. The traffic crashes table contains information on each crash, one record per crash. The party table contains information from all parties involved in the crashes, one record per party. Parties are individuals involved in a traffic crash including drivers, pedestrians, bicyclists, and parked vehicles. The victim table contains information about each party injured in the collision, including any passengers. Injury severity is included in the victim table. For example, a crash occurs (1 record in the crash table) that involves a driver party and a pedestrian party (2 records in the party table). Only the pedestrian is injured and thus is the only victim (1 record in the victim table). B. HOW THE DATASET IS CREATED Traffic crash injury data is collected from the California Highway Patrol 555 Crash Report as submitted by the police officer within 30 days after the crash occurred. All fields that match the SWITRS data schema are programmatically extracted, de-identified, geocoded, and loaded into TransBASE. See Section D below for details regarding TransBASE. C. UPDATE PROCESS After review by SFPD and SFDPH staff, the data is made publicly available approximately a month after the end of the previous quarter (May for Q1, August for Q2, November for Q3, and February for Q4). D. HOW TO USE THIS DATASET This data is being provided as public information as defined under San Francisco and California public records laws. SFDPH, SFMTA, and SFPD cannot limit or restrict the use of this data or its interpretation by other parties in any way. Where the data is communicated, distributed, reproduced, mapped, or used in any other way, the user should acknowledge TransBASE.sfgov.org as the source of the data, provide a reference to the original data source where also applicable, include the date the data was pulled, and note any caveats specified in the associated metadata documentation provided. However, users should not attribute their analysis or interpretation of this data to the City of San Francisco. While the data has been collected and/or produced for the use of the City of San Francisco, it cannot guarantee its accuracy or completeness. Accordingly, the City of San Francisco, including SFDPH, SFMTA, and SFPD make no representation as to the accuracy of the information or its suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. As all data is associated with methodological assumptions and limitations, the City recommends that users review methodological documentation associated with the data prior to its analy

This dataset contains traffic crash records for crashes which have occurred within current City of Austin Full Purpose boundaries, inclusive of all public safety jurisdictions, in the last ten years. It is one of two datasets which power our Vision Zero Viewer dashboard, available here: https://visionzero.austin.gov/viewer.

Crash data may take several weeks to be submitted, reviewed, and finalized for inclusion in this dataset. To provide the most accurate information as possible, we only provide crash data as recent as two weeks old. Please also note that some crash records may take even longer to appear in this dataset, depending on the circumstances of the crash and the ensuing law enforcement investigation.

Crash data is obtained from the Texas Department of Transportation (TxDOT) Crash Record Information System (CRIS) database, which is populated by reports submitted by Texas Peace Officers throughout the state, including Austin Police Department (APD).

The data and information on this website is for informational purposes only. While we seek to provide accurate information, please note that errors may be present and information presented may not be complete.

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 Fataility Analysis Reporting System (FARS) dataset is as of July 1, 2017, and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). One of the primary objectives of the National Highway Traffic Safety Administration (NHTSA) is to reduce the staggering human toll and property damage that motor vehicle traffic crashes impose on our society. FARS is a census of fatal motor vehicle crashes with a set of data files documenting all qualifying fatalities that occurred within the 50 States, the District of Columbia, and Puerto Rico since 1975. To qualify as a FARS case, the crash had to involve a motor vehicle traveling on a trafficway customarily open to the public, and must have resulted in the death of a motorist or a non-motorist within 30 days of the crash. This data file contains information about crash characteristics and environmental conditions at the time of the crash. There is one record per crash. Please note: 207 records in this database were geocoded to latitude and logtitude of 0,0 due to lack of location information or errors in the reported locations. FARS data are made available to the public in Statistical Analysis System (SAS) data files as well as Database Files (DBF). Over the years changes have been made to the type of data collected and the way the data are presented in the SAS data files. Some data elements have been dropped and new ones added, coding of individual data elements has changed, and new SAS data files have been created. Coding changes and the years for which individual data items are available are shown in the “Data Element Definitions and Codes” section of this document. The FARS Coding and Editing Manual contains a detailed description of each SAS data elements including coding instructions and attribute definitions. The Coding Manual is published for each year of data collection. Years 2001 to current are available at: http://www-nrd.nhtsa.dot.gov/Cats/listpublications.aspx?Id=J&ShowBy=DocType Note: In this manual the word vehicle means in-transport motor vehicle unless otherwise noted.

The Registered Accidents Netherlands file is a file containing the accident reports of various suppliers linked to the digital road network (the National Road File, NWB). This product allows you to perform various road safety analyses for your management area and is particularly suitable for: — policy (formulation, monitoring, evaluation), research and road management.

A. SUMMARY This table contains all fatalities resulting from a traffic crash in the City of San Francisco. Fatality year-to-date crash data is obtained from the Office of the Chief Medical Examiner (OME) death records, and only includes those cases that meet the San Francisco Vision Zero Fatality Protocol maintained by the San Francisco Department of Public Health (SFDPH), San Francisco Police Department (SFPD), and San Francisco Municipal Transportation Agency (SFMTA). Injury crash data is obtained from SFPD’s Interim Collision System for 2018 to YTD, Crossroads Software Traffic Collision Database (CR) for years 2013-2017 and the Statewide Integrated Transportation Record System (SWITRS) maintained by the California Highway Patrol for all years prior to 2013. Only crashes with valid geographic information are mapped. All geocodable crash data is represented on the simplified San Francisco street centerline model maintained by the Department of Public Works (SFDPW). Collision injury data is queried and aggregated on a quarterly basis. Crashes occurring at complex intersections with multiple roadways are mapped onto a single point and injury and fatality crashes occurring on highways are excluded. The fatality table contains information about each party injured or killed in the collision, including any passengers. B. HOW THE DATASET IS CREATED Traffic crash injury data is collected from the California Highway Patrol 555 Crash Report as submitted by the police officer within 30 days after the crash occurred. All fields that match the SWITRS data schema are programmatically extracted, de-identified, geocoded, and loaded into TransBASE. See Section D below for details regarding TransBASE. This table is filtered for fatal traffic crashes. C. UPDATE PROCESS After review by SFPD and SFDPH staff, the data is made publicly available approximately a month after the end of the previous quarter (May for Q1, August for Q2, November for Q3, and February for Q4). D. HOW TO USE THIS DATASET This data is being provided as public information as defined under San Francisco and California public records laws. SFDPH, SFMTA, and SFPD cannot limit or restrict the use of this data or its interpretation by other parties in any way. Where the data is communicated, distributed, reproduced, mapped, or used in any other way, the user should acknowledge the Vision Zero initiative and the TransBASE database as the source of the data, provide a reference to the original data source where also applicable, include the date the data was pulled, and note any caveats specified in the associated metadata documentation provided. However, users should not attribute their analysis or interpretation of this data to the City of San Francisco. While the data has been collected and/or produced for the use of the City of San Francisco, it cannot guarantee its accuracy or completeness. Accordingly, the City of San Francisco, including SFDPH, SFMTA, and SFPD make no representation as to the accuracy of the information or its suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. As all data is associated with methodological assumptions and limitations, the City recommends that users review methodological documentation associated with the data prior to its analysis, interpretation, or communication. TransBASE is a geospatially enabled database maintained by SFDPH that currently includes over 200 spatially referenced variables from multiple agencies and across a range of geographic scales, including infrastructure, transportation, zoning, sociodemographic, and collision data, all linked to an intersection or street segment. TransBASE facilitates a data-driven approach to understanding and addressing transportation-related health issues, informed by a large and growing evidence base regarding the importance of transportation system design and land u

Crash data shows information about each traffic crash on city streets within the City of Chicago limits and under the jurisdiction of Chicago Police Department (CPD). Data are shown as is from the electronic crash reporting system (E-Crash) at CPD, excluding any personally identifiable information. Records are added to the data portal when a crash report is finalized or when amendments are made to an existing report in E-Crash. Data from E-Crash are available for some police districts in 2015, but citywide data are not available until September 2017. About half of all crash reports, mostly minor crashes, are self-reported at the police district by the driver(s) involved and the other half are recorded at the scene by the police officer responding to the crash. Many of the crash parameters, including street condition data, weather condition, and posted speed limits, are recorded by the reporting officer based on best available information at the time, but many of these may disagree with posted information or other assessments on road conditions. If any new or updated information on a crash is received, the reporting officer may amend the crash report at a later time. A traffic crash within the city limits for which CPD is not the responding police agency, typically crashes on interstate highways, freeway ramps, and on local roads along the City boundary, are excluded from this dataset.

All crashes are recorded as per the format specified in the Traffic Crash Report, SR1050, of the Illinois Department of Transportation. The crash data published on the Chicago data portal mostly follows the data elements in SR1050 form. The current version of the SR1050 instructions manual with detailed information on each data elements is available here.

As per Illinois statute, only crashes with a property damage value of $1,500 or more or involving bodily injury to any person(s) and that happen on a public roadway and that involve at least one moving vehicle, except bike dooring, are considered reportable crashes. However, CPD records every reported traffic crash event, regardless of the statute of limitations, and hence any formal Chicago crash dataset released by Illinois Department of Transportation may not include all the crashes listed here.

Change 11/21/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.

This dataset includes motor vehicle crashes reported to the Denver Police Department, that occurred within the City and County of Denver and during the previous five calendar years plus the current year to date. The data are dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. General variable descriptions can be found below within the downloadable file, Metadata.xml. For additional information, please review the Colorado Investigating Officer's Traffic Accident Reporting Manual, available online.

Vision Zero Boston is our commitment to focus the city’s resources on proven strategies to eliminate fatal and serious traffic crashes in the city by 2030. We are inspired by the belief that even one fatality is too many. Learn more about about the Vision Zero Boston program at https://www.boston.gov/departments/transportation/vision-zero.

This dataset, provided as part of the Vision Zero Boston program, contains records of the date, time, location, and type of crash for incidents requiring public safety response which may involve injuries or fatalities. All records are compiled by the Department of Innovation and Technology from the City's Computer-Aided Dispatch (911) system and verified as having required a response from a public safety agency. To protect the privacy of individuals involved in these incidents, we do not indicate the severity of specific crashes or whether medical care was provided in any specific case.

Additional notes:

• Each incident is included only once regardless of the number of individuals involved.
• The date and time of an incident reflects when public safety response was dispatched to the location, not the crash itself.
• Records are typically updated on a monthly basis, but because the verification process involves manual confirmation of incidents, exact posting schedules may vary.
• Records may be updated after their initial posting if new information becomes available.
• Data on pedestrian and bicycle incidents is now available through December 31, 2024. Motor Vehicle incident data for July 1, 2024 to December 31, 2024 is delayed due to a change in the reporting system.

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 Infrastrutture e dei Trasporti Anno 2000; Istituto Poligrafico e Zecca dello Stato: Roma, Italy, 2001; 20. Ministero delle Infrastrutture e dei Trasporti Conto nazionale dei trasporti 1999. 2000. 21. Generale, D.; Informativi, S. delle Infrastrutture e dei Trasporti Anno 2004. 22. Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle Infrastrutture e dei Trasporti Anno 2001; 2002; 23. Ministero delle Infrastrutture e dei

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).