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.

As of May 2023, driving accidents were by far the most common type of accident in Germany. This was especially true when roads were slippery, with ** percent taking place in snowy or icy conditions. Accidents when turning or crossing, on the other hand, were more frequent on dry roads. While the number of traffic accidents did not change dramatically by month, in 2023 figures were higher during the summer and at the end of 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.

Road traffic accidents are accidents in which people are killed or injured or property is damaged as a result of driving on public roads and squares. The accident atlas contains accidents involving personal injury. Accidents that only result in property damage are not shown. The accident atlas contains information from the statistics of road traffic accidents based on reports from the police stations. The published CSV data was treated as follows: * - Only the accidents in the city of Konstanz were taken into account. * - A unique ID number was created for each accident. * - The historical data for the years 2016 to 2019 have been aggregated. * - The variable "IstStreet" was renamed to "FAULT STATE" after 2017. For consistency, both have been labeled "FAULT" for the entire period. * - The measurements are the same (0, 1, 2). * - The "LIGHT" variable was renamed to "ULICHTVERH" after 2017. For consistency, both were labeled "ULICHTVERH" for the entire period. The measurements are the same (0, 1, 2) * - The complete data set was also subdivided into 6 more for the years 2016 to 2019, each containing accidents involving only bicycles, passenger cars, pedestrians, motorcycles, goods vehicles and others. * - Year and month have been combined in an additional column to facilitate time series comparisons. Variables: * - AccidentID: unique number for each accident * - year-month: year and month combined 2016-1 * - UJAHR: accident year * - UMONAT: accident month * - HOURS: accident hour * - UWEEKDAY: Day of the week (1 = Sunday 2 = Monday 3 = Tuesday 4 = Wednesday 5 = Thursday 6 = Friday 7 = Saturday) * - UK CATEGORIES: Accident categories (criterion for allocation is the most serious consequence of the accident) 1 = accident with fatalities 2 = accident with serious injuries 3 = accident with minor injuries UART: Accident type 1 = collision with approaching/stopping/stationary vehicle 2 = collision with preceding/waiting vehicle 3 = collision with sideways vehicle moving in the same direction 4 = collision with oncoming vehicle 5 = Collision with turning / crossing vehicle 6 = Collision between vehicle and pedestrian 7 = Impact with lane obstacle 8 = Lane departure to the right 9 = Lane departure to the left 0 = Other type of accident * - UART: Type of accident 1 = Collision with approaching/stopping /stationary vehicle 2 = collision with vehicle ahead/waiting 3 = collision with vehicle traveling sideways in the same direction 4 = collision with oncoming vehicle 5 = collision with turning / crossing vehicle 6 = collision between vehicle and pedestrian 7 = collision with roadway obstacle 8 = departure from lane to the right 9 = departure from lane to the left 0 = accident of a different kind * - UTYP1: Accident type 1 = driving accident 2 = turning accident 3 = turning / crossing accident 4 = crossing accident 5 = accident caused by stationary traffic 6 = accident in parallel traffic 7 = other accident * - LIGHT CONDITIONS: lighting conditions 0 = daylight 1 = twilight 2 = darkness * - IstRad: accident in which at least one bicycle was involved 0 = accident without bicycle involvement 1 = accident with bicycle involvement * - IstPKW Accident with car: Accident in which at least one passenger car was involved 0 = Accident without car involvement 1 = Accident with car involvement * - IstFuss Accident with pedestrian: Accident in which at least one pedestrian was involved 0 = Accident without Pedestrian participation 1 = accident involving pedestrians * - IstKrad Accident involving a motorcycle: Accident involving at least one motorcycle, e.g. B. moped, motorcycle/scooter was involved 0 = accident without motorcycle participation 1 = accident with motorcycle participation * - IstGkfz: Accident with goods vehicle (GKFZ): Accident involving at least one truck with normal body and a total weight of more than 3.5 t truck with tank support or special body, a tractor unit or another tractor unit was involved (this category is included in "Accident with other" in 2016 and 2017) 0 = accident without goods vehicle involvement 1 = accident with goods vehicle involvement * - ActualOther: Accident with other: accident involving at least one means of transport not mentioned above, e.g. B. a bus or a tram (2016 and 2017 inclusive) accident with goods vehicle (GKFZ), from 2018 without accident with GKFZ) 0 = accident without involving a means of transport not mentioned above 1 = accident involving a means of transport not mentioned above * - LINREFX and LINREFY : Graphical coordinate 1 and graphic coordinate 2LINREFX and LINREFY form the coordinate of the accident location on the road section (UTM coordinate of the reference system ETRS89, zone 32N). XGCSWGS84 and YGCSWGS84: Graphic coordinate 1 and graphic coordinate 2 XGCSWGS84 and YGCSWGS84 form the coordinate of the accident site on the road section (coordinate of the reference system GK 3) For further explanations see destatis.de (Source: Accident Atlas of the Federal and State Statistical Offices - Open Data) ### Data source : Open Data Konstanz under DL-DE/BY 2.0

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 data on traffic accidents by vehicle type by year is provided by the National Police Agency, and is the number of accidents by vehicle type by year. It is the data that aggregates the number of accidents by passenger cars, vans, trucks, two-wheeled vehicles, personal mobility devices (PMs), agricultural machinery, and others by year. According to these statistics, the traffic accident status by vehicle type in 2024 shows some characteristics despite the overall downward trend. Passenger car accidents decreased slightly from 2023 to 130,774 cases, recording the largest number of accidents among major vehicle types. Two-wheeled vehicle (15,290 cases) and personal mobility devices (PMs, 2,232 cases) accidents continued to decrease steadily. On the other hand, passenger car (12,146 cases) and truck (24,464 cases) accidents increased slightly compared to the previous year, and agricultural machinery (277 cases) accidents decreased. This data can be used as important basic data for establishing customized traffic safety policies for each vehicle type.

This metadata set was generated by the mCLOUD and summarises all the individual metadata records of this data series. Data on traffic accidents in Schleswig-Holstein ## Description of the fields — ‘ID’ — Running number of the accident (one record per accident) — ‘Uland’ — state, here only ‘01’ = Schleswig-Holstein — ‘UREGBEZ’ — always ‘0’ — ‘Ukreis’ — Circle — ‘UGEMEINDE’ — municipality — ‘UYear’ — year of accident — ‘UMONAT’ — accident month — ‘USTUNDE’ — Accident Hour — ‘UWOCHENTAG’ — weekday: 1 = Sunday, 2 = Monday, 3 = Tuesday,... 6 = Saturday — ‘UKATEGORIE’ — Accident category (criteria for assignment is the most serious accident sequence): 1 = accident with killed, 2 = accident with serious injuries, 3 = accident with minor injuries — ‘UART’ — type of accident: 1 = collision with starting/continuing/resting vehicle, 2 = collision with ahead/waiting vehicle, 3 = collision with vehicle driving sideways in the same direction, 4 = collision with oncoming vehicle, 5 = collision with the vehicle to the right, 6 = collision between vehicle and pedestrian, 7 = impact on road barrier, 8 = agreement from roadway to the right, 9 = agreement from roadway to the left, 0 = accident of other type — ‘UTYP1’ — Accident type: 1 = driving accident, 2 = turn accident, 3 = bend/cross accident, 4 = crossing accident, 5 = accident caused by resting traffic, 6 = accident in longitudinal traffic, 7 = other accident — ‘ULICHTVERH’ — lighting conditions: 0 = daylight, 1 = twilight, 2 = darkness — ‘ISTRAD’ — Accident with wheel: 1, if at least one bicycle was involved in the accident — ‘IstPKW’ — Accident with car: 1, if at least one passenger car was involved in the accident — ‘IstFuss’ — Accident with pedestrians: 1, if at least one pedestrian was involved in the accident — ‘IstKrad’ — accident with motorcycle: 1, if at least one motorcycle, such as mofa, motorcycle/roller was involved in the accident — ‘IstGkfz’ — Accident with goods motor vehicle: 1, if the accident involved at least one lorries with a normal body and a total weight of more than 3.5 tonnes, a truck with a tank or special body, a tractor unit or another tractor — ‘actual’ — Accident with others: 1, if at least one of the above-mentioned means of transport (e.g. bus or train) was involved in the accident — ‘USTRZUSTAND’ — Road condition: 0 = dry, 1 = wet/moist/slip, 2 = winter smooth — ‘LINREFX’ — The geocoordinates of the accident location on the road section (UTM coordinate of the reference system ETRS89, Zone 32N) — ‘LINREFY’ — ‘XGCSWGS84’ — The geocoordinates of the accident location on the road section (geographical coordinates in decimal degree of the reference system WGS84) — ‘YGCSWGS84’ ## Data origin This is an excerpt from the accident data for Germany https://unfallatlas.statistikportal.de/_opendata2021.html Filtered to the entries where ‘Uland=01’ is. For the geocoordinates comma was replaced by decimal point. Further explanations on the traffic accident data can be found on the page of the Accident Atlas of the Federal and State Statistical Offices.

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

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.

Crashes data includes crash event level details such as location - the Lat/Long of the nearest intersection, A and B street names, with distance and direction of the crash from nearest intersection, etc... It also includes crash level details like weather and roadway conditions, and time of day. Also included are the involved party (vehicle involved with), primary collision factor and severity of injury in terms of fatalities, and severe, moderate and minor injuries per crash.

The vehicles data includes the vehicle level details of the crash such as vehicle types, driver's (vehicle, party) age and sex, driver conditions and violations proceeding the crash, etc...

There is a one to many relationship that needs to be built that relates the crash to the vehicles involved. (i.e. there are an average of 2.07 vehicles/parties involved per crash)

Match the Crash name in vehicle data to the Name in the Crash data to relate the two sets of data.

Road Safety Statistics releases and guidance about the data collection. Collision analysis tool for bespoke breakdowns of our data. STATS19 R package developed independently of DfT, offering an alternative way to access this data for those familiar with the R language. Latest data Provisional data for the first 6 months of 2024 published 28 November 2024. These are provisional un-validated data. Data included These files provide detailed road safety data about the circumstances of personal injury road collisions in Great Britain from 1979, the types of vehicles involved and the consequential casualties. The statistics relate only to personal injury collisions on public roads that are reported to the police, and subsequently recorded, using the STATS19 collision reporting form. This data contains all the non-sensitive fields that can be made public. Sensitive data fields, for example contributory factors data, can be requested by completing the sensitive data form and contacting the road safety statistics team at roadacc.stats@dft.gov.uk All the data variables are coded rather than containing textual strings. The lookup tables are available in the supporting documents section towards the bottom of the table. Data relating to the casualty and collision severity adjustment to account for changes in police reporting of severity is provided in separate files and can be joined using the appropriate record identifiers. Timing of data release Final annual data is released annually in late September following the publication of the annual reported road casualties Great Britain statistical publication. Individual years data is available for each of the last 5 years, with earlier years available as part of a single download. In addition, un-validated provisional mid-year data (covering January to June) is released at end November, to provide more up to date information Data revisions Except for the severity adjustments, data are not routinely revised those occasionally minor amendments to previous years can be made. Details of recent revisions are available, together with a request for any feedback on the approach to revising the data. The files published here represent the latest data.

Data on traffic accidents in Schleswig-Holstein ## Description of the fields - ID - Consecutive number of the accident (one data record per accident) - ULAND - State, here only 01 = Schleswig-Holstein - UREGBEZ - always 0 - UKREIS - district - UGEGEINDE - municipality - UJAHR - accident year - UMONAT - accident month - USTUNDE - accident hour - UWECHENDAY - weekday: 1 = Sunday, 2 = Monday, 3 = Tuesday , … 6 = Saturday - UKATEGORIE - Accident category (criterion for classification is the most serious consequence of the accident): 1 = accident with fatalities, 2 = accident with serious injuries, 3 = accident with minor injuries - UART - type of accident: 1 = collision with approaching/stopping/stationary vehicle, 2 = collision with preceding/waiting vehicle, 3 = collision with vehicle traveling sideways in the same direction, 4 = collision with oncoming vehicle, 5 = collision with turning/crossing vehicle, 6 = collision between vehicle and pedestrian , 7 = impact with lane obstacle, 8 = lane departure to the right, 9 = lane departure to the left, 0 = other type of accident - UTYP1 - type of accident: 1 = driving accident, 2 = turning accident, 3 = turning/crossing accident , 4 = crossing accident, 5 = accident caused by stationary traffic, 6 = accident in parallel traffic, 7 = other accident - ULICHTVERH - lighting conditions: 0 = daylight, 1 = twilight, 2 = darkness - IstRad - accident with bike : 1 if at least one bicycle was involved in the accident - IstPKW - accident involving a car: 1 if at least one passenger car was involved in the accident - IstFuss - accident involving a pedestrian: 1 if at least one pedestrian was involved in the accident was involved - IstKrad - Accident involving a motorcycle: 1 if at least one motorcycle, e.g normal body and a total weight of more than 3.5 t, a truck with tank support or special body, a tractor unit or another tractor unit was involved - IsOther - Accident with others: 1 if at least one means of transport not mentioned above (e.g. bus or train) was involved - USTRSTATE - Road condition: 0 = dry, 1 = wet/damp/slippery, 2 = slippery - LINREFX - The geo-coordinates of the accident location on the road section (UTM coordinate of the ETRS89 reference system, zone 32N) - LINREFY - XGCSWGS84 - The geocoordinates of the accident site on the road section (geographical coordinates in decimal degrees of the WGS84 reference system) - YGCSWGS84 ## Data origin This is an extract from the accident data for Germany https:// unfallatlas.Statisticsportal.de/_opendata2021.html Filtering was done for the entries where ULAND=01 is. In the geo-coordinates, the comma has been replaced by a decimal point. Further explanations of the traffic accident data can be found on the page of the accident atlas of the statistical offices of the federal and state governments.

Analysis of ‘Road traffic accidents ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/d22714da-94a1-4380-96f9-404128a2cbd3 on 15 January 2022.

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

Data on traffic accidents in Schleswig-Holstein

# Description of the fields

— ‘ID’ — Current number of the accident (one record per accident) — ‘ULAND’ — Land, here only ‘01’ = Schleswig-Holstein — 'UREGBEZ' — always’ 0’ — ‘UKREIS’ — Kreis — ‘UGEMEINDE’ — Municipality — ‘UJAHR’ — year of accident — ‘UMONAT’ — month of accident — ‘USTUNDE’ — hour of accident — ‘UWOCHENTAG’ — Day of the week: 1 = Sunday, 2 = Monday, 3 = diestag,... 6 = Saturday — ‘UKATEGORY’ — category of accident (classification criterion is the most serious accident sequence): 1 = fatal accident, 2 = accident with serious injuries, 3 = accident with minor injuries — ‘UART’ — type of accident: 1 = collision with starting/continuing/dormant vehicle, 2 = collision with preceding/waiting vehicle, 3 = collision with vehicle running side in the same direction, 4 = oncoming vehicle collision, 5 = collision with turning/cross vehicle, 6 = collision between vehicle and pedestrian, 7 = impact on road barrier, 8 = agreement between ground and right, 9 = agreement from other ground to ground 0, — ‘UTYP1’ — accident type: 1 = driving accident, 2 = turning accident, 3 = turning/cross accident, 4 = accident exceeding, 5 = accident caused by dormant traffic, 6 = longitudinal accident, 7 = other accident — ‘ULICHTVERH’ — Luminous conditions: 0 = daylight, 1 = insulation, 2 = darkness — ‘Istrad’ — accident with wheel: 1 if the accident involved at least one bicycle — ‘actual cars’ — Car accident: 1 if the accident involved at least one passenger car — ‘IstFuss’ — accident involving pedestrians: 1 if the accident involved at least one pedestrian — ‘IstKrad’ — accident by motorcycle: 1 if the accident involved at least one motorcycle, such as Mofa, motorcycle/scooter — ‘IstGkfz’ — accident involving a goods vehicle: 1 if the accident involved at least one lorry with a standard bodywork and a total weight exceeding 3,5 tonnes, a truck with a tank or special body, a tractor unit or other tractor — ‘IstOther’ — Accident with others: 1 if the accident involved at least one means of transport not mentioned above (e.g. bus or train) — ‘USTRZUSTAND’ — Road condition: 0 = dry, 1 = wet/wet/plugged, 2 = winter smooth — ‘LINREFX’ — The geo-coordinates of the place of accident on the road section (UTM coordinate of the ETRS89 reference system, zone 32N) — 'LINREFY’ — ‘XGCSWGS84 '— The geo-coordinates of the place of accident on the road section (geographical coordinates in decimal degrees of the WGS84 reference system) — 'YGCSWGS84’

# Source of data

This is an extract from the accident data for Germany https://unfallatlas.statistikportal.de/_opendata2021.html

Filtered to those entries for which ‘ULAND = 01’. In the geocoordinates, comma was replaced by a decimal point.

Further explanations on traffic accident data can be found on the page of the Accident Atlas of the Statistical Offices of the Federal Government and the Länder.

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

Road Safety Statistics releases [missing hyperlink]

Data download tool [missing hyperlink] for bespoke breakdowns of our data.

These files provide detailed road safety data about the circumstances of personal injury road accidents in GB from 1979, the types of vehicles involved and the consequential casualties. The statistics relate only to personal injury accidents on public roads that are reported to the police, and subsequently recorded, using the STATS19 accident reporting form.

There has been an increasing demand for more up to date information on reported road accidents to be made available to the public, stakeholders and researchers. As a result, the Department for Transport made a dataset covering accidents for the first and second quarters of 2018 in Great Britain available for the first time on data.gov.uk. The data released was an un-validated subset and has been superseded by the full accident dataset for 2018, released after validation for the full year.

All the data variables are coded rather than containing textual strings. The lookup tables are available in the "Additional resources" section towards the bottom of the table.

Please note that the 2015 data were revised on the 29th September 2016. Accident, Vehicle and Casualty data for 2005 - 2009 are available in the time series files under 2014. Data for 1979 - 2004 are available as a single download under 2004 below.

Also includes: Results of breath-test screening data from recently introduced digital breath testing devices, as provided by Police Authorities in England and Wales Results of blood alcohol levels (milligrams / 100 millilitres of blood) provided by matching coroners’ data (provided by Coroners in England and Wales and by Procurators Fiscal in Scotland) with fatality data from the STATS19 police data of road accidents in Great Britain. For cases when the Blood Alcohol Levels for a fatality are "unknown" are a consequence of an unsuccessful match between the two data sets.

These files provide detailed road safety data about the circumstances of personal injury road accidents in GB from 1979, the types (including Make and Model) of vehicles involved and the consequential casualties. The statistics relate only to personal injury accidents on public roads that are reported to the police, and subsequently recorded, using the STATS19 accident reporting form.

All the data variables are coded rather than containing textual strings. The lookup tables are available in the "Additional resources" section towards the bottom of the table.

Please note that the 2015 data were revised on the 29th September 2016.

Accident, Vehicle and Casualty data for 2005 - 2009 are available in the time series files under 2014. Data for 1979 - 2004 are available as a single download under 2004 below.

Also includes: Results of breath-test screening data from recently introduced digital breath testing devices, as provided by Police Authorities in England and Wales

Results of blood alcohol levels (milligrams / 100 millilitres of blood) provided by matching coroners’ data (provided by Coroners in England and Wales and by Procurators Fiscal in Scotland) with fatality data from the STATS19 police data of road accidents in Great Britain. For cases when the Blood Alcohol Levels for a fatality are "unknown" are a consequence of an unsuccessful match between the two data sets.

As of October 2023, there were approximately ****** thousand minor injuries caused by local road accidents, followed by around ****** accidents with serious injuries in Thailand. In that same period, Thailand had about ****** thousand total road accidents.

This dataset contains tagged accident data and is provided for reproducibility for our journal paper

Pablo Moriano, Andy Berres, Haowen Xu, Jibonananda Sanyal. “Spatiotemporal Features of Traffic Help Reduce Automatic Accident Detection Time.” Expert Systems with Applications 244 (2024): 122813. https://doi.org/10.1016/j.eswa.2023.122813

The accompanying Data in Brief publication discusses the methodology behind the creation of these data.

Berres, Andy, Pablo Moriano, Haowen Xu, Sarah Tennille, Lee Smith, Jonathan Storey, and Jibonananda Sanyal. "A Traffic Accident Dataset for Chattanooga, Tennessee." Data in Brief (2024): 110675.

The zip folder annotatedData.zip contains two subfolders: allData and bestData. The bestData folder contains all data for which a full neighborhood of five sensors upstream and five sensors downstream is available, whereas allData includes everything from bestData as well as data with a smaller number of neighboring sensors. Each folder contains one subfolder called accidents and one subfolder called non-accidents. The accidents folder contains one file per accident. The non-accidents folder contains files for the same location, day of the week and time as a corresponding accident, for each week during which there was no accident impact on the traffic.

The file names in both folders are formatted as follows: yyyy-mm-dd-hhmm-rrrrrXaaa.a.csv, consisting of date (yyyy-mm-dd), time (hhmm in 24-hour format), and sensor name (rrrrrXaaa.a), which consists of road name (rrrrr; 5 alphanumerical characters), heading (X), and mile marker (aaa.a). For example, the file 2020-11-03-1611-00I24W182.8.csv contains data for an accident which occurred at 4:11 p.m. on November 3, 2020 on I-24 Westbound near the radar sensor at mile marker 182.8.

The content of each CSV file is a timeseries of radar data beginning 15 minutes prior to the reported incident and ending 15 minutes after the reported incident. It also contains metadata, such as the accident type, etc. Each CSV file contains the following columns:

incident at sensor(i): 1 for yes (accidents folder), 0 for no (non-accidents folder)

road: road name with heading, e.g. 00I24E

mile: mile marker of nearest radar sensor, e.g. 182.8

type: accident type, e.g. “Prop Damage (over)” for property damage exceeding a certain threshold. For non-accidents, the type is given as “None”.

date: date of the data sample. For accidents, this is the date on which the accident occurred. For non-accidents, this is the date for which the non-accident data sample is collected.

incident_time: time the reference accident was reported in hh:mm. This is the time which is provided in E-TRIMS as the time the 911 call was made.

incident_hour: just the hour from the incident_time, in integer format.

data_time: timestamp for the timeseries contained in the file in hh:mm:ss format. The timeseries consists of 30 second timesteps.

weather: weather during data_time, based on data collected from NASA POWER. We used dry bulb temperature (°C), precipitation (mm/h), and wind speed (m/s) from the raw NASA POWER data to produce the classifications of rain (at least 1mm precipitation and temperatures above 2°C), snow (at least 1mm precipitation and temperatures at or below 2°C), and wind (wind speeds over 30 mph or 13.5 m/s). If there were no inclement weather conditions, we set the category to “--".

light: light conditions during data_time. To produce this field, we collected sunrise, sunset, civil twilight start and civil twilight end times from https://sunrise-sunset.org, and derived the categories dawn, daylight, dusk, and dark using these start and end times.

The last 33 columns contain radar data for the 11 sensors surrounding the accident or non-accident. For each sensor, we collected speed (mean over 30-second interval in miles per hour, or empty if no vehicles passed), volume (count of all vehicles passing during 30-second interval), and occupancy (mean % of occupancy over 30-second interval). These three variables are grouped in triples, of speed (k), volume (k), occupancy (k), where k indicates the sensor number relative to the closest sensor i to the incident, ki indicate downstream sensors. For example, speed (i-5) refers to the mean speed at the sensor which is 5 hops upstream from the accident, and volume(i+1) refers to the number of vehicles at the sensor immediately downstream from the accident.

The folder metaData.zip contains the following files:

Accidents.csv: cleaned-up accidents file with all accidents which happened on Chattanooga area highways between November 1, 2020 and April 29, 2021. We have removed accidents which happened on non-highway roads, and we have corrected the timestamps (which were in 12-hour format but missing a.m./p.m. markers) by cross-referencing light and weather conditions.

WeatherDict.json: a dictionary containing the weather data synthesized from NASA POWER.

LightDict.json: a dictionary containing the light data synthesized from Sunrise-and-Sunset.

SensorTopology.csv: neighborhood information for each radar sensor in the Chattanooga area.

SensorZones.geojson: polygons used to determine the nearest radar sensor for each accident location. Each polygon is tagged with the corresponding radar sensor’s name.

Data analysis worksheets and average crash rates by intersection type and roadway functional classification.