The Motor Vehicle Collisions crash table contains details on the crash event. Each row represents a crash event. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.For the most accurate, up to date statistics on traffic fatalities, please refer to the NYPD Motor Vehicle Collisions page (updated weekly) or Vision Zero View (updated monthly).

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

This dataset contains 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.

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

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.

Traffic fatalities within the City of Chicago that are included in Vision Zero Chicago (VZC) statistics. Vision Zero is Chicago’s commitment to eliminating fatalities and serious injuries from traffic crashes. The VZC Traffic Fatality List is compiled by the Chicago Department of Transportation (CDOT) after monthly reviews of fatal traffic crash information provided by Chicago Police Department’s Major Accident Investigation Unit (MAIU). CDOT uses a standardized process – sometimes differing from other sources and everyday use of the term -- to determine whether a death is a “traffic fatality.” Therefore, the traffic fatalities included in this list may differ from the fatal crashes reported in the full Traffic Crashes dataset (https://data.cityofchicago.org/d/85ca-t3if). Official traffic crash data are published by the Illinois Department of Transportation (IDOT) on an annual basis. This VZC Traffic Fatality List is updated monthly. Once IDOT publishes its crash data for a year, this dataset is edited to reflect IDOT’s findings. VZC Traffic Fatalities can be linked with other traffic crash datasets using the “Person_ID” field. State of Illinois considers a “traffic fatality” as any death caused by a traffic crash involving a motor vehicle, within 30 days of the crash. Fatalities that meet this definition are included in this VZC Traffic Fatality List unless excluded by any criteria below. There may be records in this dataset that do not appear as fatalities in the other datasets. The following criteria exclude a death from being considered a "traffic fatality," and are derived from Federal and State reporting standards. The Medical Examiner determined that the primary cause of the fatality was not the traffic crash, including: a. The fatality was reported as a suicide based on a police investigation. b. The fatality was reported as a homicide in which the "party at fault" intentionally inflicted serious bodily harm that caused the victim's death. c. The fatality was caused directly and exclusively by a medical condition or the fatality was not attributable to road user movement on a public roadway. (Note: If a person driving suffers a medical emergency and consequently hits and kills another road user, the other road user is included, although the driver suffering a medical emergency is excluded.) The crash did not occur within a trafficway. The crash involved a train or other such mode of transport within the rail dedicated right-of-way. The fatality was on a roadway not under Chicago Police Department jurisdiction, including: a. The fatality was occurred on an expressway. The City of Chicago does not have oversight on the expressway system. However, a fatality on expressway ramps occurring within the City jurisdiction will be counted in VZC Traffic Fatality List. b. The fatality occurred outside City limits. Crashes on streets along the City boundary may be assigned to another jurisdiction after the investigation if it is determined that the crash started or substantially occurred on the side of the street that is outside the City limits. Jurisdiction of streets along the City boundary are split between City and neighboring jurisdictions along the street centerline. The fatality is not a person (e.g., an animal). Change 12/7/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.

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.

This indicator provides information about the mortality rate from motor vehicle crashes and traffic-related injuries, including among pedestrians. Death rate has been age-adjusted to the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Motor vehicle crashes are a leading cause of death from unintentional injury both in Los Angeles County and in the US. While many factors contribute to motor vehicle crash mortality, the built environment plays a critical role. Communities that are exposed to heavy traffic or that lack adequate walking infrastructure for pedestrians have higher rates of motor vehicle crash-related injuries and deaths. They are also more impacted by traffic-related environmental hazards, such as vehicle emissions and air pollution. In Los Angeles County, many of these communities are also home to a large number of low-income residents. Thus, motor vehicle crash mortality can be viewed as an environmental justice issue.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This is publicly available data on all motor vehicle crashes in the US from the National Highway Traffic Safety Administration.

FARS is a census of fatal motor vehicle crashes with a set of data files documenting all qualifying fatalities that occurred within the 50 States, the District of Columbia, and Puerto Rico since 1975. To qualify as a FARS case, the crash had to involve a motor vehicle traveling on a traffic way customarily open to the public, and must have resulted in the death of a motorist or a non-motorist within 30 days of the crash.

One of the primary objectives of the National Highway Traffic Safety Administration is to reduce the staggering human toll and property damage that motor vehicle traffic crashes impose on our society. Crashes each year result in thousands of lives lost, hundreds of thousands of injured victims, and billions of dollars in property damage. Accurate data are required to support the development, implementation, and assessment of highway safety programs aimed at reducing this toll. NHTSA uses data from many sources, including the Fatality Analysis Reporting System (FARS) that began operation in 1975. Providing data about fatal crashes involving all types of vehicles, the FARS is used to identify highway safety problem areas, provide a basis for regulatory and consumer information initiatives, and form the basis for cost and benefit analyses of highway safety initiatives.

You will want to start with the accident dataset and go from there.

Codebook and explanation of variables found here: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813556

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.

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 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.

Redirect Notice: The website https://transbase.sfgov.org/ is no longer in operation. Visitors to Transbase will be redirected to this page where they can view, visualize, and download Traffic Crash data.A. SUMMARYThis table contains all crashes resulting in an injury in the City of San Francisco. Fatality year-to-date crash data is obtained from the Office of the Chief Medical Examiner (OME) death records, and only includes those cases that meet the San Francisco Vision Zero Fatality Protocol maintained by the San Francisco Department of Public Health (SFDPH), San Francisco Police Department (SFPD), and San Francisco Municipal Transportation Agency (SFMTA). Injury crash data is obtained from SFPD’s Interim Collision System for 2018 through the current year-to-date, Crossroads Software Traffic Collision Database (CR) for years 2013-2017 and the Statewide Integrated Transportation Record System (SWITRS) maintained by the California Highway Patrol for all years prior to 2013. Only crashes with valid geographic information are mapped. All geocodable crash data is represented on the simplified San Francisco street centerline model maintained by the Department of Public Works (SFDPW). Collision injury data is queried and aggregated on a quarterly basis. Crashes occurring at complex intersections with multiple roadways are mapped onto a single point and injury and fatality crashes occurring on highways are excluded.The crash, party, and victim tables have a relational structure. The traffic crashes table contains information on each crash, one record per crash. The party table contains information from all parties involved in the crashes, one record per party. Parties are individuals involved in a traffic crash including drivers, pedestrians, bicyclists, and parked vehicles. The victim table contains information about each party injured in the collision, including any passengers. Injury severity is included in the victim table. For example, a crash occurs (1 record in the crash table) that involves a driver party and a pedestrian party (2 records in the party table). Only the pedestrian is injured and thus is the only victim (1 record in the victim table). To learn more about the traffic injury datasets, see the TIMS documentationB. HOW THE DATASET IS CREATEDTraffic crash injury data is collected from the California Highway Patrol 555 Crash Report as submitted by the police officer within 30 days after the crash occurred. All fields that match the SWITRS data schema are programmatically extracted, de-identified, geocoded, and loaded into TransBASE. See Section D below for details regarding TransBASE. C. UPDATE PROCESSAfter review by SFPD and SFDPH staff, the data is made publicly available approximately a month after the end of the previous quarter (May for Q1, August for Q2, November for Q3, and February for Q4). D. HOW TO USE THIS DATASETThis data is being provided as public information as defined under San Francisco and California public records laws. SFDPH, SFMTA, and SFPD cannot limit or restrict the use of this data or its interpretation by other parties in any way. Where the data is communicated, distributed, reproduced, mapped, or used in any other way, the user should acknowledge TransBASE.sfgov.org as the source of the data, provide a reference to the original data source where also applicable, include the date the data was pulled, and note any caveats specified in the associated metadata documentation provided. However, users should not attribute their analysis or interpretation of this data to the City of San Francisco. While the data has been collected and/or produced for the use of the City of San Francisco, it cannot guarantee its accuracy or completeness. Accordingly, the City of San Francisco, including SFDPH, SFMTA, and SFPD make no representation as to the accuracy of the information or its suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. As all data is associated with methodological assumptions and limitations, the City recommends that users review methodological documentation associated with the data prior to its analysis, interpretation, or communication.This dataset can also be queried on the TransBASE Dashboard. TransBASE is a geospatially enabled database maintained by SFDPH that currently includes over 200 spatially referenced variables from multiple agencies and across a range of geographic scales, including infrastructure, transportation, zoning, sociodemographic, and collision data, all linked to an intersection or street segment. TransBASE facilitates a data-driven approach to understanding and addressing transportation-related health issues,informed by a large and growing evidence base regarding the importance of transportation system design and land use decisions for health. TransBASE’s purpose is to inform public and private efforts to improve transportation system safety, sustainability, community health and equity in San Francisco.E. RELATED DATASETSTraffic Crashes Resulting in Injury: Parties InvolvedTraffic Crashes Resulting in Injury: Victims InvolvedTransBASE DashboardiSWITRSTIMSData pushed to ArcGIS Online on November 5, 2025 at 4:19 PM by SFGIS.Data from: https://data.sfgov.org/d/ubvf-ztfxDescription of dataset columns:

 unique_id
 unique table row identifier


 cnn_intrsctn_fkey
 nearest intersection centerline node key


 cnn_sgmt_fkey
 nearest street centerline segment key (empty if crash occurred at intersection)


 case_id_pkey
 unique crash report number


 tb_latitude
 latitude of crash (WGS 84)


 tb_longitude
 longitude of crash (WGS 84)


 geocode_source
 geocode source


 geocode_location
 geocode location


 collision_datetime
 the date and time when the crash occurred


 collision_date
 the date when the crash occurred


 collision_time
 the time when the crash occurred (24 hour time)


 accident_year
 the year when the crash occurred


 month
 month crash occurred


 day_of_week
 day of the week crash occurred


 time_cat
 generic time categories


 juris
 jurisdiction


 officer_id
 officer ID


 reporting_district
 SFPD reporting district


 beat_number
 SFPD beat number


 primary_rd
 the road the crash occurred on


 secondary_rd
 a secondary reference road that DISTANCE and DIRECT are measured from


 distance
 offset distance from secondary road


 direction
 direction of offset distance


 weather_1
 the weather condition at the time of the crash


 weather_2
 the weather condition at the time of the crash, if a second description is necessary


 collision_severity
 the injury level severity of the crash (highest level of injury in crash)


 type_of_collision
 type of crash


 mviw
 motor vehicle involved with


 ped_action
 pedestrian action involved


 road_surface
 road surface


 road_cond_1
 road condition


 road_cond_2
 road condition, if a second description is necessary


 lighting
 lighting at time of crash


 control_device
 control device status


 intersection
 indicates whether the crash occurred in an intersection


 vz_pcf_code
 California vehicle code primary collision factor violated


 vz_pcf_group
 groupings of similar vehicle codes violated


 vz_pcf_description
 description of vehicle code violated


 vz_pcf_link
 link to California vehicle code section


 number_killed
 counts victims in the crash with degree of injury of fatal


 number_injured
 counts victims in the crash with degree of injury of severe, visible, or complaint of pain


 street_view
 link to Google Streetview


 dph_col_grp
 generic crash groupings based on parties involved


 dph_col_grp_description
 description of crash groupings


 party_at_fault
 party number indicated as being at fault


 party1_type
 party 1 vehicle type


 party1_dir_of_travel
 party 1 direction of travel


 party1_move_pre_acc
 party 1 movement preceding crash


 party2_type
 party 2 vehicle type (empty if no party 2)


 party2_dir_of_travel
 party 2 direction of travel (empty if no party 2)


 party2_move_pre_acc
 party 2 movement preceding crash (empty if no party 2)


 point
 geometry type of crash location


 data_as_of
 date data added to the source system


 data_updated_at
 date data last updated the source system


 data_loaded_at
 date data last loaded here (in the open data portal)


 analysis_neighborhood



 supervisor_district



 police_district



 Current Police Districts
 This column was automatically created in order to record in what polygon from the dataset 'Current Police Districts' (qgnn-b9vv) the point in column 'point' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.


 Current Supervisor Districts
 This column was automatically created in order to record in what polygon from the dataset 'Current Supervisor Districts' (26cr-cadq) the point in column 'point' is located. This

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

Redirect Notice: The website https://transbase.sfgov.org/ is no longer in operation. Visitors to Transbase will be redirected to this page where they can view, visualize, and download Traffic Crash data.A. SUMMARYThis table contains all crashes resulting in an injury in the City of San Francisco. Fatality year-to-date crash data is obtained from the Office of the Chief Medical Examiner (OME) death records, and only includes those cases that meet the San Francisco Vision Zero Fatality Protocol maintained by the San Francisco Department of Public Health (SFDPH), San Francisco Police Department (SFPD), and San Francisco Municipal Transportation Agency (SFMTA). Injury crash data is obtained from SFPD’s Interim Collision System for 2018 through the current year-to-date, Crossroads Software Traffic Collision Database (CR) for years 2013-2017 and the Statewide Integrated Transportation Record System (SWITRS) maintained by the California Highway Patrol for all years prior to 2013. Only crashes with valid geographic information are mapped. All geocodable crash data is represented on the simplified San Francisco street centerline model maintained by the Department of Public Works (SFDPW). Collision injury data is queried and aggregated on a quarterly basis. Crashes occurring at complex intersections with multiple roadways are mapped onto a single point and injury and fatality crashes occurring on highways are excluded.The crash, party, and victim tables have a relational structure. The traffic crashes table contains information on each crash, one record per crash. The party table contains information from all parties involved in the crashes, one record per party. Parties are individuals involved in a traffic crash including drivers, pedestrians, bicyclists, and parked vehicles. The victim table contains information about each party injured in the collision, including any passengers. Injury severity is included in the victim table. For example, a crash occurs (1 record in the crash table) that involves a driver party and a pedestrian party (2 records in the party table). Only the pedestrian is injured and thus is the only victim (1 record in the victim table). To learn more about the traffic injury datasets, see the TIMS documentationB. HOW THE DATASET IS CREATEDTraffic crash injury data is collected from the California Highway Patrol 555 Crash Report as submitted by the police officer within 30 days after the crash occurred. All fields that match the SWITRS data schema are programmatically extracted, de-identified, geocoded, and loaded into TransBASE. See Section D below for details regarding TransBASE. C. UPDATE PROCESSAfter review by SFPD and SFDPH staff, the data is made publicly available approximately a month after the end of the previous quarter (May for Q1, August for Q2, November for Q3, and February for Q4). D. HOW TO USE THIS DATASETThis data is being provided as public information as defined under San Francisco and California public records laws. SFDPH, SFMTA, and SFPD cannot limit or restrict the use of this data or its interpretation by other parties in any way. Where the data is communicated, distributed, reproduced, mapped, or used in any other way, the user should acknowledge TransBASE.sfgov.org as the source of the data, provide a reference to the original data source where also applicable, include the date the data was pulled, and note any caveats specified in the associated metadata documentation provided. However, users should not attribute their analysis or interpretation of this data to the City of San Francisco. While the data has been collected and/or produced for the use of the City of San Francisco, it cannot guarantee its accuracy or completeness. Accordingly, the City of San Francisco, including SFDPH, SFMTA, and SFPD make no representation as to the accuracy of the information or its suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. As all data is associated with methodological assumptions and limitations, the City recommends that users review methodological documentation associated with the data prior to its analysis, interpretation, or communication.This dataset can also be queried on the TransBASE Dashboard. TransBASE is a geospatially enabled database maintained by SFDPH that currently includes over 200 spatially referenced variables from multiple agencies and across a range of geographic scales, including infrastructure, transportation, zoning, sociodemographic, and collision data, all linked to an intersection or street segment. TransBASE facilitates a data-driven approach to understanding and addressing transportation-related health issues,informed by a large and growing evidence base regarding the importance of transportation system design and land use decisions for health. TransBASE’s purpose is to inform public and private efforts to improve transportation system safety, sustainability, community health and equity in San Francisco.E. RELATED DATASETSTraffic Crashes Resulting in Injury: Parties InvolvedTraffic Crashes Resulting in Injury: Victims InvolvedTransBASE DashboardiSWITRSTIMSData pushed to ArcGIS Online on December 2, 2025 at 4:11 AM by SFGIS.Data from: https://data.sfgov.org/d/ubvf-ztfxDescription of dataset columns:

 unique_id
 unique table row identifier


 cnn_intrsctn_fkey
 nearest intersection centerline node key


 cnn_sgmt_fkey
 nearest street centerline segment key (empty if crash occurred at intersection)


 case_id_pkey
 unique crash report number


 tb_latitude
 latitude of crash (WGS 84)


 tb_longitude
 longitude of crash (WGS 84)


 geocode_source
 geocode source


 geocode_location
 geocode location


 collision_datetime
 the date and time when the crash occurred


 collision_date
 the date when the crash occurred


 collision_time
 the time when the crash occurred (24 hour time)


 accident_year
 the year when the crash occurred


 month
 month crash occurred


 day_of_week
 day of the week crash occurred


 time_cat
 generic time categories


 juris
 jurisdiction


 officer_id
 officer ID


 reporting_district
 SFPD reporting district


 beat_number
 SFPD beat number


 primary_rd
 the road the crash occurred on


 secondary_rd
 a secondary reference road that DISTANCE and DIRECT are measured from


 distance
 offset distance from secondary road


 direction
 direction of offset distance


 weather_1
 the weather condition at the time of the crash


 weather_2
 the weather condition at the time of the crash, if a second description is necessary


 collision_severity
 the injury level severity of the crash (highest level of injury in crash)


 type_of_collision
 type of crash


 mviw
 motor vehicle involved with


 ped_action
 pedestrian action involved


 road_surface
 road surface


 road_cond_1
 road condition


 road_cond_2
 road condition, if a second description is necessary


 lighting
 lighting at time of crash


 control_device
 control device status


 intersection
 indicates whether the crash occurred in an intersection


 vz_pcf_code
 California vehicle code primary collision factor violated


 vz_pcf_group
 groupings of similar vehicle codes violated


 vz_pcf_description
 description of vehicle code violated


 vz_pcf_link
 link to California vehicle code section


 number_killed
 counts victims in the crash with degree of injury of fatal


 number_injured
 counts victims in the crash with degree of injury of severe, visible, or complaint of pain


 street_view
 link to Google Streetview


 dph_col_grp
 generic crash groupings based on parties involved


 dph_col_grp_description
 description of crash groupings


 party_at_fault
 party number indicated as being at fault


 party1_type
 party 1 vehicle type


 party1_dir_of_travel
 party 1 direction of travel


 party1_move_pre_acc
 party 1 movement preceding crash


 party2_type
 party 2 vehicle type (empty if no party 2)


 party2_dir_of_travel
 party 2 direction of travel (empty if no party 2)


 party2_move_pre_acc
 party 2 movement preceding crash (empty if no party 2)


 point
 geometry type of crash location


 data_as_of
 date data added to the source system


 data_updated_at
 date data last updated the source system


 data_loaded_at
 date data last loaded here (in the open data portal)


 analysis_neighborhood



 supervisor_district



 police_district



 Current Police Districts
 This column was automatically created in order to record in what polygon from the dataset 'Current Police Districts' (qgnn-b9vv) the point in column 'point' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.


 Current Supervisor Districts
 This column was automatically created in order to record in what polygon from the dataset 'Current Supervisor Districts' (26cr-cadq) the point in column 'point' is located. This

Dataset 1 (AXA collisions 2015–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on collisions using an interrupted time series analysis. Collisions data came from insurance collision claims (January 2015 to December 2019). The dataset contains 8 variables: year (anio_n), week (semana), count of total collisions per week (c_total), count of collisions resulting in injury per week (c_p_lesion), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time), an estimate of the number of insured vehicles per week (veh_a_cdmx). Dataset 2 (Road traffic deaths 2013–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on mortality using an interrupted time series analysis. Mortality data came from vital registries collated by the Mexican Institute for Geography and Statistics, INEGI, (January 2013 to December 2019). The dataset contains 7 variables: year (anio_ocur), week (semana), count of traffic-related deaths per week (def_trans), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time) and an estimate of the Mexico City population per week (pob_tot_p). Methods Dataset 1 arises from publicly available data on insurance-reported collisions published on the website of the International Institute for Data Science (see reference below). The data were collected by claims adjusters from the company AXA at the site of the collision using an electronic device. These data were available for public use from January 2015 to December 2019 and include information on individual collisions and their characteristics: date the collision occurred, location (coordinates and adjuster reported location), type of vehicle involved and whether there were injuries or deaths. Data were processed and cleaned, mapping collisions, and keeping only those georeferenced within Mexico City boundaries as well as coded to Mexico City in the reported location variable. We then summed the number of collisions per week and merged it with data on an estimate of the number of insured registered vehicles per week (using information from registered vehicles and proportion of insured vehicles from the Mexican Association of Insurance companies). Two more variables were created, one that identifies the week when the intervention came into effect and another variable to number the weeks since baseline. This dataset contains all the necessary information to conduct the interrupted time series analysis for total collisions and collisions resulting in injuries. Dataset 2: mortality data were validated and reported by INEGI (see reference below) from death certificates filed mainly by the Health Sector, using the International Classification of Disease, 10th Revision (ICD-10) for diagnosis codes. We used data from January 2013 to December 2019 and included deaths with the following ICD-10 codes: V02-V04 (.1-.9), V09, V092, V09.3, V09.9, V12-V14 (.3-.9), V19.4-V19.6, V19.9, V20-V28 (.3-.9), V29, V30-V39, V40-V79 (.4-.9), V80.3-V80.5, V81.1, V82.1, V82.1, V83-V86 (.0-.3), V87-V89.2 and V89.9. We summed the number of traffic-related deaths per week and merged it with data on an estimate of the total population in Mexico City per week (see refs below). Two more variables were created, one that identifies the week when the intervention came into effect and another variable to number the weeks since baseline. This dataset contains all the necessary information to conduct the interrupted time series analysis for road traffic deaths. References to original data:

Instituto Internacional de Ciencia de Datos. Datos AXA de Percances Viales [Internet]. 2020 [July 2021]. Available from: https://i2ds.org/datos-abiertos/. Instituto Nacional de Geografía y Estadística. Parque Vehicular [Internet]. 2019 [July 2021]. Available from: https://www.inegi.org.mx/temas/vehiculos/default.html#Tabulados. Dirección Ejecutiva de Líneas de Negocio área de Automóviles. Sistema Estadístico del Sector Asegurador del ramo Automóviles SESA 2018. Mexico City: Asociación Mexicana de Instituciones de Seguro, 2020. Instituto Nacional de Geografía y Estadística. Mortalidad [Internet]. 2020 [July 2021]. Available from: https://www.inegi.org.mx/programas/mortalidad/default.html#Datos_abiertos.

World Health Organisation. ICD-10 Version:2010 [Internet]. 2010 [July 2021]. Available from: https://icd.who.int/browse10/2010/en. Consejo Nacional de Población. Proyecciones de la Población de México y de las Entidades Federativas, 2016-2050 [Internet]. 2018 [July 2021]. Available from: https://datos.gob.mx/busca/dataset/proyecciones-de-la-poblacion-de-mexico-y-de-las-entidades-federativas-2016-2050.