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

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

The Measurement template document is available at the archived version of this page on the UK Government Web Archive.

DfT Business plan

Geographical coverage: Great Britain

Information broken down by: Accident site. Data are available by geographical area, age, gender and type of road user.

Latest data

In 2013:

• 1,713 people were killed in reported road traffic accidents in Great Britain, 2% (41) fewer than in 2012. This is the lowest number of fatalities since national records began in 1926. The total number of people killed in 2013 was 39% lower than the 2005-09 baseline average
• the number of fatalities decreased for pedestrians, pedal cyclists and car occupants, by 5%, 8% and 2% respectively, but increased for motorcycle users by 1%. Over the same period motor vehicle traffic remained broadly stable, with a small increase of 0.4% between 2012 and 2013
• with the exception of 2011, road deaths have fallen every year since 2004. Adverse weather (heavy snow falls) experienced in the first and last quarters of 2010 but not in 2011, is likely to be the main factor behind the increase in fatalities recorded in 2011

Year	Road accident fatalities	% change from previous year
2000	3,409	-0.4
2001	3,450	1.2
2002	3,431	-0.6
2003	3,508	2.2
2004	3,221	-8.2
2005	3,201	-0.6
2006	3,175	-0.9
2007	2,946	-7.1
2008	2,538	-13.8
2009	2,222	-12.5
2010	1,850	-16.7
2011	1,901	2.8
2012	1,754	-7.7
2013	1,713	-2.3

The complete set of data is available for download.

Background information

The indicator can be broken down by any geographical area (eg country, region, local authority) since a grid reference is collected for each accident. Information is also available by age, gender, type of road user and road type. Numbers will be relatively small for more detailed breakdowns of the total and may therefore fluctuate from year to year. This needs to be taken into account when assessing trends.

• publishing schedule: annual
• last updated: September 2014
• next update: July 2015

Other related data and information

More detailed analysis and time series can be found in Reported road casualties Great Britain: annual report.

Record level data on accidents and casualties can be found in http://data.gov.uk/dataset/road-accidents-safety-data/">Record level data

Further information

• Business Plan 2012 to 2015
• Input and impact indicators
• Analyst contact: Anil Bhagat, roadacc.stats@dft.gsi.gov.uk

The car crash dataset provides a detailed compilation of information related to common factors influencing road accidents, such as collision severity, weather conditions, road types, and contributing elements, offering valuable insights for the analysis and enhancement of overall road safety measures.

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.

This dataset contains information about vehicles (or units as they are identified in crash reports) involved in a traffic crash. This dataset should be used in conjunction with the traffic Crash and People dataset available in the portal. “Vehicle” information includes motor vehicle and non-motor vehicle modes of transportation, such as bicycles and pedestrians. Each mode of transportation involved in a crash is a “unit” and get one entry here. Each vehicle, each pedestrian, each motorcyclist, and each bicyclist is considered an independent unit that can have a trajectory separate from the other units. However, people inside a vehicle including the driver do not have a trajectory separate from the vehicle in which they are travelling and hence only the vehicle they are travelling in get any entry here. This type of identification of “units” is needed to determine how each movement affected the crash. Data for occupants who do not make up an independent unit, typically drivers and passengers, are available in the People table. Many of the fields are coded to denote the type and location of damage on the vehicle. Vehicle information can be linked back to Crash data using the “CRASH_RECORD_ID” field. Since this dataset is a combination of vehicles, pedestrians, and pedal cyclists not all columns are applicable to each record. Look at the Unit Type field to determine what additional data may be available for that record. The Chicago Police Department reports crashes on IL Traffic Crash Reporting form SR1050. 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. Change 11/21/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.

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

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

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.

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).

Data Dictionary

Column Name	Meaning
CRASH DATE	Date of the crash incident (day)
CRASH TIME	Time of the crash incident (hour and minute)
BOROUGH	Borough in which the crash occurred
ZIP CODE	Postal code of the crash location
LATITUDE	Latitude coordinates of the crash location
LONGITUDE	Longitude coordinates of the crash location
LOCATION	Geographical location description of the crash
ON STREET NAME	Name of the street where the crash occurred
CROSS STREET NAME	Name of the cross street where the crash occurred
NUMBER OF PERSONS INJURED	Total number of persons injured in the crash
NUMBER OF PERSONS KILLED	Total number of persons killed in the crash
NUMBER OF PEDESTRIANS INJURED	Number of pedestrians injured in the crash
NUMBER OF PEDESTRIANS KILLED	Number of pedestrians killed in the crash
NUMBER OF CYCLIST INJURED	Number of cyclists injured in the crash
NUMBER OF CYCLIST KILLED	Number of cyclists killed in the crash
NUMBER OF MOTORIST INJURED	Number of motorists (vehicle occupants) injured in the crash
NUMBER OF MOTORIST KILLED	Number of motorists (vehicle occupants) killed in the crash
CONTRIBUTING FACTOR VEHICLE 1	Primary contributing factor for the crash (vehicle 1)
CONTRIBUTING FACTOR VEHICLE 2	Secondary contributing factor for the crash (vehicle 2)
COLLISION_ID	Unique identifier for the collision incident
VEHICLE TYPE CODE 1	Type of vehicle involved in the crash (vehicle 1)
VEHICLE TYPE CODE 2	Type of vehicle involved in the crash (vehicle 2)

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.

Overview:

Information on location and characteristics of crashes in Queensland for all reported Road Traffic Crashes occurred from 1 January 2001 to 31 Dec 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).

Leeds Road Traffic Accidents

Number of Vehicles, Casualties and Impact Severity

By data.world's Admin [source]

About this dataset

This dataset provides detailed information on road traffic accidents across Leeds from 2009-2018. By analysing this data, we can gain invaluable insights into the locations, amounts of vehicles and people involved, road surfaces, weather conditions, and severities of any casualties in each accident.

In order to get the most out of this dataset for our projects, it is helpful to utilise Excel pivot tables for summarising large amounts of data in a concise manner. Additionally, certain figures may be repeated due to the format of the report (for example: Reference Number Grid Ref: Easting Grid Ref: Northing Number of vehicles Accident Date Time (24hr)). Please also note that due to poor internet connectivity at times when generating Eastings and Northings needed for accident location points - there could be errors that arise from these coordinates being inaccurate. These should then be directed to accident.studiesleeds.gov.uk.

The columns of this dataset include helpful information such as reference number; easting and northing coordinates; number of vehicles involved; time and date; 1st road class; lighting conditions; weather conditions; casualty classifications(sex & severity); type vehicle - all helping us gain a better understanding not only into why accidents occur but also how they are managed and what makes them more preventable in future situations ahead! Before you delve deeper into this dataset - please read through the guidance document - outlining further details regarding particular categories included in all records!

More Datasets

For more datasets, click here.

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How to use the dataset

This dataset contains information regarding road traffic accidents in Leeds from 2009-2018. The data includes the location of each accident, the number of vehicles and people involved, the road surface, weather conditions, and severity of any casualties.

Research Ideas

• Identifying high-risk roads for more concentrated safety/enforcement measures: By analyzing the types of accidents, casualty severity and accident locations in this dataset, governments can identify roads with a higher rate of accidents in order to better allocate resources (such as police patrol and enforcement or increased safety measures) to minimize road fatalities and injuries.
• Predicting which type of vehicles are involved most in accidents: With the data on vehicle type provided in this dataset, researchers can use predictive analytics techniques such as Machine Learning (ML) models to predict which vehicle types have a higher probability of being involved in an accident.
• Examining weather conditions for more effective regulations: With detailed weather data captured from each accident, local governments can define better regulations based on whether the weather at the time was conducive to safe driving or not, especially when it comes to reducing speed limits during extremely humid or foggy days or clear off road obstructions during rainy days

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: 2016-7.csv | Column name | Description | |:------------------------|:------------------------------------------------------------------| | Reference Number | Unique identifier for each accident. (Integer) | | Number of Vehicles | Number of vehicles involved in the accident. (Integer) | | Expr1 | Unknown. (Integer) | | Time (24hr) | Time of the accident in 24 hour format. (String) | | 1st Road Class | Classification of the road where the accident occurred. (String) | | Road Surface | Type of surface of the road where the accident occurred. (String) | | Lighting Conditions | Lighting conditions at the time of the accident. (String) | | Weather Conditions | Weather conditions at the time of the accident. (String) | | Casualty Class | Classification of the casualty involved in the accident. (String) | | Casualty Severity | Severity of the casualty involved in the accident. (String) | | Sex of Casualty | Gender of the casualty involve...

Road accidents have been a major cause for concern across the Indian subcontinent. In 2022 alone, the country reported nearly *** thousand fatalities due to road accidents. Each year, about ***** to **** percent of the country’s GDP was invested in road accidents. Notably, while India has about *** percent of the world’s vehicle population, it also accounted for about *** percent of the global road traffic incidents. Almost ** percent of the accidents involved young Indians. Cases and causesTwo-wheelers had the maximum involvement in fatal road accidents across the country in 2018. A major portion of the accidents that year occurred at T-junctions. Over speeding has been a cause for concern throughout the country regardless of day or nighttime. Moreover, fast and risky maneuvers and illegal street races on roads and highways not designed for the purpose created significant trouble for the police. Over ** percent of the accidents occurred on straight roads. Additionally, state highways had a share of about ** percent of the total road accidents in 2018. Future scenarioRoughly around 17 accident-related deaths occur across India every hour. Fewer cops and empty roads at night, and sometimes even during the day seem to enable motorists to do away with the traffic rules. However, efforts were made to reduce these discrepancies. The police had equipped themselves with night vision speed guns to identify the culprits. Over speeding fine was increased in the amendment of the Motor Vehicles Act as well. The road network has played a crucial role in India’s economic development and the government is likely to continue to invest resources in making road safety a vital component of everyday commute.

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.

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 Dataset

This dataset provides a comprehensive record of 216,000+ traffic accidents reported in Nashville, Tennessee, between January 2018 and early April 2025. It is for traffic analysts, public safety researchers, and data scientists seeking to understand patterns, risk factors, and trends in vehicular incidents across time, location, and conditions.

📝 Column Descriptions

• Accident Number: Unique numeric ID assigned to each accident report.

Example: 2,008,473,471

• Date and Time: The exact date and time the accident occurred (format: MM/DD/YYYY HH:MM:SS AM/PM).

Example: 7/14/2018, 6:00 PM

• Number of Motor Vehicles: Number of motor vehicles involved in the accident.

Example: 2

• Number of Injuries: Total number of people injured during the accident.

Example: 2

• Number of Fatalities: Total number of deaths resulting from the accident.

Example: 0

• Property Damage: Indicates if property damage occurred.

Example: Y

• Hit and Run: Whether the accident was a hit-and-run.

Example: N

• Collision Type Description: Text description of the type of collision.

Example: HEAD-ON

• Weather Description: Weather condition at the time of the accident.

Example: RAIN

• Illumination Description: Describes lighting conditions during the accident.

Example: DAYLIGHT

• Street Address: Street name or location where the accident took place.

Example: 28TH AVE N & JEFFERSON ST

• City: City in which the accident occurred.

Example: ANTIOCH

• State: State where the accident occurred

Example: TN

• Precinct: Police precinct responsible for handling the accident report.

Example: NORTH

• Lat: Latitude coordinate of the accident location.

Example: 36.168

• Long: Longitude coordinate of the accident location.

Example: -86.821

• HarmfulCodes: Coded representation of harmful events or objects involved.

Example: 12;39

• HarmfulDescriptions: Text description of the harmful event from the accident.

Example: GUARDRAIL FACE

• ObjectId: Internal GIS or system-generated identifier (matches row index).

Example: 1,2,3,4,5,6,7,8,9,ect

• Zip Code: ZIP code of the accident location.

Example: 37208

• RPA: Likely a reporting or administrative code used internally.

Example: 4525

• Weather: Numerical weather condition code.

Example: 21

• IlluACCIDEmination: Numerical lighting condition code.

Example: 3

• Collision Type: Numeric collision type code.

Example: 11

• Reporting Officer: Officer badge or ID number who filed the report.

Example: 225845

• x: X-coordinate in projected spatial system (used for GIS mapping).

Example: -9664842.682

• y: Y-coordinate in projected spatial system (used for GIS mapping).

Example: 4323742.127

Killed or Seriously Injured (KSI) Road Traffic Accidents Indicator: This indicator measures the percentage change in the number of people killed or seriously injured in road traffic accidents, based on a 3-year rolling average up to the current year. A positive figure indicates improved performance (i.e., a reduction in casualties compared to the previous 3-year period).

Performance Target: For comparability, performance is also assessed against a target to reduce KSI numbers by 40% over 10 years.

Scope: Includes people of all ages killed or seriously injured on the roads. Previously reported as NI 047.

Definitions:

Fatal Casualties: Deaths occurring within 30 days of the accident (excluding confirmed suicides). Seriously Injured Casualties: Injuries requiring hospitalisation or involving fractures, concussion, internal injuries, crushings, burns (excluding friction burns), severe cuts/lacerations, severe shock requiring medical treatment, or injuries causing death 30+ days after the accident. Slight Injuries: Excluded from totals. Includes sprains (e.g., whiplash), bruises, minor cuts, or slight shock requiring roadside attention.

Recording Practices: Police record injuries based on initial information, not medical examination. Hospitalisation practices vary regionally.

Systems Used: Police forces use either CRaSH (Collision Recording and Sharing) or COPA (Case Overview Preparation Application). Estimates are based on police-reported figures.

Data Comparability: Since 2016, changes in severity reporting systems affect comparability of serious and slight injury data. Both adjusted and unadjusted KSI statistics are available.

Further Information: Road Accidents and Considerations: Areas with low resident populations but high traffic inflows (e.g., City of London, rural tourist areas) may show artificially high rates. Heathrow Airport counts are included in London Region and England totals only.

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

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.

This data contains information about people involved in a crash and if any injuries were sustained. This dataset should be used in combination with the traffic Crash and Vehicle dataset. Each record corresponds to an occupant in a vehicle listed in the Crash dataset. Some people involved in a crash may not have been an occupant in a motor vehicle, but may have been a pedestrian, bicyclist, or using another non-motor vehicle mode of transportation. Injuries reported are reported by the responding police officer. Fatalities that occur after the initial reports are typically updated in these records up to 30 days after the date of the crash. Person data can be linked with the Crash and Vehicle dataset using the “CRASH_RECORD_ID” field. A vehicle can have multiple occupants and hence have a one to many relationship between Vehicle and Person dataset. However, a pedestrian is a “unit” by itself and have a one to one relationship between the Vehicle and Person table.

The Chicago Police Department reports crashes on IL Traffic Crash Reporting form SR1050. 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.

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