This table contains data on the annual number of fatal and severe road traffic injuries per population and per miles traveled by transport mode, for California, its regions, counties, county divisions, cities/towns, and census tracts. Injury data is from the Statewide Integrated Traffic Records System (SWITRS), California Highway Patrol (CHP), 2002-2010 data from the Transportation Injury Mapping System (TIMS) . The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity]. Transportation accidents are the second leading cause of death in California for people under the age of 45 and account for an average of 4,018 deaths per year (2006-2010). Risks of injury in traffic collisions are greatest for motorcyclists, pedestrians, and bicyclists and lowest for bus and rail passengers. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience 4 times the death rate as Whites or Asians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

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 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.

The program collects data for analysis of traffic safety crashes to identify problems, and evaluate countermeasures leading to reducing injuries and property damage resulting from motor vehicle crashes. The FARS dataset contains descriptions, in standard format, of each fatal crash reported. To qualify for inclusion, a crash must involve a motor vehicle traveling a traffic-way customarily open to the public and resulting in the death of a person (occupant of a vehicle or a non-motorist) within 30 days of the crash. Each crash has more than 100 coded data elements that characterize the crash, the vehicles, and the people involved. The specific data elements may be changed slightly each year to conform to the changing user needs, vehicle characteristics and highway safety emphasis areas. The type of information that FARS, a major application, processes is therefore motor vehicle crash data.

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.

VITAL SIGNS INDICATOR
Fatalities From Crashes (EN4)

FULL MEASURE NAME
Fatalities from Crashes (traffic collisions)

LAST UPDATED
October 2022

DESCRIPTION
Fatalities from crashes refers to deaths as a result of fatalities sustained in collisions. The California Highway Patrol includes deaths within 30 days of the collision that are a result of fatalities sustained as part of this metric. This total fatalities dataset includes fatality counts for the region and counties, as well as individual collision data and metropolitan area data.

DATA SOURCE
National Highway Safety Administration: Fatality Analysis Reporting System - https://www.nhtsa.gov/file-downloads?p=nhtsa/downloads/FARS/
1990-2020

Caltrans: Highway Performance Monitoring System (HPMS) - https://dot.ca.gov/programs/research-innovation-system-information/highway-performance-monitoring-system
Annual Vehicle Miles Traveled (VMT)
2001-2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
1990-2020

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
1990-2020

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Fatalities from crashes data is reported to the National Highway Traffic Safety Administration through the Fatality Analysis Reporting System (FARS) program. Data for individual collisions is reported by the California Highway Patrol (CHP) to the Statewide Integrated Traffic Records System (SWITRS). The data was tabulated using provided categories specifying injury level, individuals involved, causes of collision and location/jurisdiction of collision (for more information refer to the SWITRS codebook - http://tims.berkeley.edu/help/files/switrs_codebook.doc). For case data, latitude and longitude information for each accident is geocoded by SafeTREC’s Transportation Injury Mapping System (TIMS). Fatalities were normalized over historic population data from the US Census Bureau’s population estimates and vehicle miles traveled (VMT) data from the Federal Highway Administration.

The crash data only include crashes that involved a motor vehicle. Bicyclist and pedestrian fatalities that did not involve a motor vehicle, such as a bicyclist and pedestrian collision or a bicycle crash due to a pothole, are not included in the data.

For more regarding reporting procedures and injury classification, refer to the CHP Manual - https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ca_chp555_manual_2_2003_ch1-13.pdf.

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

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

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

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

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

The Motor Vehicle Collisions person table contains details for people involved in the crash. Each row represents a person (driver, occupant, pedestrian, bicyclist,..) involved in a crash. The data in this table goes back to April 2016 when crash reporting switched to an electronic system. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details. Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.

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.

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.

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).

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 http://visionzeroboston.org.

This dataset, provided as part of the Vision Zero Boston program, contains records of the date, time, location, and type of fatality for Vision Zero related crashes resulting in a fatality. All records are compiled by the Department of Innovation and Technology from the City's Computer-Aided Dispatch (911) system and verified by the Boston Police Department as being a Vision Zero related fatality. To protect the privacy of individuals involved in these incidents, we do not provide any descriptions of the incident 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 location of an incident reflects where public safety response was dispatched to, not the incident 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.
• The data includes traffic fatalities that take place on streets that are within the jurisdiction of the Boston Police Department. It does not include traffic fatalities on streets that are within the jurisdiction of the Massachusetts State Police.
• The following are excluded: fatal crashes that occur on private property or on streets that are not owned by the City of Boston; fatalities due to driver medical emergencies, intentional assault, or suicide; fatalities where the driver or passenger fell out of a car.

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

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

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

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

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

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

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

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

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

The dataset contains monthly data on road accidents that occurred in the territory of the Municipality of Milan, which caused injuries to people. The data cover the number of accidents (distinguished by fatalities and injuries only) and the number of injured persons, broken down by accident outcome (injuries/deaths).

NOTE: The data on road accidents collected by the Local Police on the territory of the Municipality of Milan concern (as indicated by ISTAT) only accidents with injuries to persons. Excluded are those who have not caused death or injury. Persons injured in the accident who died within 30 days of the accident shall be considered to have died as a result of the accident.

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

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

Monthly data on the number of accidents with personal injuries and the corresponding number of people injured are collected according to the number of vehicles involved (from one up to 7 and more). The historical series is available from 2001 to 2021. ** NOTE **: The data on road accidents collected by the Local Police in the Municipality of Milan concern (as per ISTAT indications) only accidents with injuries to people. Those who have not caused deaths or injuries are excluded. Persons injured in the accident who died within 30 days of the event are considered to have died as a result of the accident.