In 2024, the state of California reported ***** motor-vehicle deaths, an increase from the year before. Death from motor-vehicles remains a relevant problem across the United States. Motor-vehicle deaths in the United States In the United States, a person’s lifetime odds of dying in a motor vehicle accident is around * in **. Death rates from motor vehicles have decreased in recent years and are significantly lower than the rates recorded in the ***** and *****. This is due to a mass improvement in car safety standards and features. For example, all states, with the exception of New Hampshire, have laws against not wearing safety belts. Drinking and driving One of the biggest causes of motor-vehicle deaths is driving while under the influence of alcohol. The state with the highest number of fatalities due to alcohol-impaired driving in 2022 was Texas, followed by California and Florida. Light trucks are the vehicle type most often involved in fatal crashes caused by alcohol-impaired drivers, with around ***** such accidents in the United States in 2022.

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.

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

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.

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

The statistic shows the number of road traffic fatalities in California by person type in 2016. In that year, *** pedestrians were killed on the roads of California.

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

The statistic shows the number of occupants killed in California road traffic by vehicle type in 2016. In that year, ***** occupants of passenger cars were killed in California road traffic.

A. SUMMARY This table contains all victims (parties who are injured) involved in a traffic crash resulting in an injury in the City of San Francisco. Fatality year-to-date crash data is obtained from the Office of the Chief Medical Examiner (OME) death records, and only includes those cases that meet the San Francisco Vision Zero Fatality Protocol maintained by the San Francisco Department of Public Health (SFDPH), San Francisco Police Department (SFPD), and San Francisco Municipal Transportation Agency (SFMTA). Injury crash data is obtained from SFPD’s Interim Collision System for 2018 to YTD, Crossroads Software Traffic Collision Database (CR) for years 2013-2017 and the Statewide Integrated Transportation Record System (SWITRS) maintained by the California Highway Patrol for all years prior to 2013. Only crashes with valid geographic information are mapped. All geocodable crash data is represented on the simplified San Francisco street centerline model maintained by the Department of Public Works (SFDPW). Collision injury data is queried and aggregated on a quarterly basis. Crashes occurring at complex intersections with multiple roadways are mapped onto a single point and injury and fatality crashes occurring on highways are excluded.

The crash, party, and victim tables have a relational structure. The traffic crashes table contains information on each crash, one record per crash. The party table contains information from all parties involved in the crashes, one record per party. Parties are individuals involved in a traffic crash including drivers, pedestrians, bicyclists, and parked vehicles. The victim table contains information about each party injured in the collision, including any passengers. Injury severity is included in the victim table.

For example, a crash occurs (1 record in the crash table) that involves a driver party and a pedestrian party (2 records in the party table). Only the pedestrian is injured and thus is the only victim (1 record in the victim table).

B. HOW THE DATASET IS CREATED Traffic crash injury data is collected from the California Highway Patrol 555 Crash Report as submitted by the police officer within 30 days after the crash occurred. All fields that match the SWITRS data schema are programmatically extracted, de-identified, geocoded, and loaded into TransBASE. See Section D below for details regarding TransBASE.

C. UPDATE PROCESS After review by SFPD and SFDPH staff, the data is made publicly available approximately a month after the end of the previous quarter (May for Q1, August for Q2, November for Q3, and February for Q4).

D. HOW TO USE THIS DATASET This data is being provided as public information as defined under San Francisco and California public records laws. SFDPH, SFMTA, and SFPD cannot limit or restrict the use of this data or its interpretation by other parties in any way. Where the data is communicated, distributed, reproduced, mapped, or used in any other way, the user should acknowledge TransBASE.sfgov.org as the source of the data, provide a reference to the original data source where also applicable, include the date the data was pulled, and note any caveats specified in the associated metadata documentation provided. However, users should not attribute their analysis or interpretation of this data to the City of San Francisco. While the data has been collected and/or produced for the use of the City of San Francisco, it cannot guarantee its accuracy or completeness. Accordingly, the City of San Francisco, including SFDPH, SFMTA, and SFPD make no representation as to the accuracy of the information or its suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. As all data is associated with methodological assumptions and limitations, the City recommends that users review methodological documentation associated with the data prior to its 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 DATASETS Traffic Crashes Resulting in Injury Traffic Crashes Resulting in Injury: Parties Involved TransBASE Dashboard iSWITRS TIMS

The statistic displays total fatality numbers of U.S. traffic crashes in 2009, sorted by state. In that year, ***** people were killed in traffic crashes in California.

The Crash Data On California State Highways Report is produced annually by the California Department of Transportation (Caltrans) to provide high level summaries of road miles, travel, crashes and crash rates on the California State Highway System.

This table lists statewide vehicle travel expressed in Million Vehicle Miles (MVM), road miles, and one and three year crash rates and fatality rates based on lane types and population codes.

While crash rates for total crash and fatal + injury crashes are calculated per MVM, fatality rates are expressed per 100 MVM.

In 2022, the United States' most populous state, California, recorded ***** pedestrian fatalities, the highest number in the U.S. It was followed by Texas, where ***** pedestrians were killed in road traffic crashes in the same year.

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.

Road Traffic Injuries in California

Pedestrian Fatalities and Injury Rates By Transport Mode, 2002-2010

By Health [source]

About this dataset

This table contains data on the number of annual fatal and severe road traffic injuries per population and per miles traveled by transport mode, for the state of California and its various regions, counties, county divisions, cities/towns, and census tracts. Road traffic injury is an important public health issue in California; it ranks second among leading causes of death for people under 45 in the state with an average of 4,018 fatalities per year (2006-2010). In addition to this terrible statistic are also elevated risks for certain population subgroups; Native American male pedestrians experience 4 times the death rate as Whites or Asians while African-Americans and Latinos experience twice the death rate as Whites or Asians.

This dataset has been generated through a combination of datasets--SWITRS (Statewide Integrated Traffic Records System), CHP (California Highway Patrol), 2002-2010 data from TIMS (Transportation Injury Mapping System)--and presents itself as part of a healthy community indicators project from the Office of Health Equity. By looking at this data users can learn about which communities are bearing a disproportionate share in terms of pedestrian/car fatalities due to road traffic injuries without taking into account additional factors such as socioeconomic status or gender. Through understanding these statistics more accurately we can begin to take steps towards promoting safe transportation practices across all communities

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How to use the dataset

Welcome to the Road Traffic Injury dataset! This dataset contains information on the annual number of fatal and severe road traffic injuries per population and per miles traveled by transport mode in California from 2002-2010. We hope that this data will be useful to you in understanding trends, evaluating safety policies, and tracking changes in transportation safety over time.

In this guide, we’ll provide an overview of the dataset so you can start making use of it. We’ll cover what each column means and how you can use them for further analysis and exploration.

The columns in this dataset include detailed information about each road traffic injury event: - ind_definition: Definition of the indicator – i.e., whether it is a rate (per population) or a risk ratio (relative to some reference group).
- reportyear: Year of the report; - race_eth_code/name: Race/ethnicity code and name provided;
- geotype/value/name: Type of geographic area included as well as its corresponding value or name; - county_fips/name: FIPS code for counties, as well as their corresponding names;

• region_code/name: Region codes with accompanying region names provided respectively;

• mode: Mode of transportation associated with these events (motorcycles, pedestrians, buses & rail passengers);

• severity : Severity level (fatal or severe);

• • 11): Number of injuries occurring within that time period within each race ethinic category (injuries, totalpop [its total population], poprate [the rate by which there are injuries happening]) ;

12)- 15): Confidence Intervals associated with 95% Lower & Upper Limits (LL 95CI [Lower than 95% range] & UL95CI [Upper than 95% range]) by population rates (poprate) & miles traveled rates (avmtrate)

16): Standard Error Rates calculated by both Population Rate(poprate) & Miles Traveled Rate(amtrate) ; 19), 20), 23)}: Relative Risk Ration Rates providing values compared bottom line across geographic regions respectively {Population Rate(CA RR poprate), Miles Traveled Rate()) ; 21), 22}, 24), 25 => Decile Rankings arranging breakdowns from 1-10 into 10 respective categories calculations

Research Ideas

• The dataset can be used to develop maps that show impact of traffic injuries in different areas by race, geotype and mode.
• It can be used to measure the performance of safety improvement interventions by comparing changes in injury rates at certain county or cities before and after safety tactics have been implemented.
• It could also be used to study the effects of individual driving behaviors on collision related injury rates by analyzing data from counties with disparate levels of enforcement

Acknowledgements

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

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Traffic collision reports recorded by the San Diego Police Department. Generally a report is not taken for property damage-only collisions that do not involve hit & run or DUI. The California Highway Patrol is responsible for handling collisions occurring on the freeway. This dataset includes basic information about collisions. Each row has a report_id, a unique identifier for the collision. A single collision may involve multiple people and/or vehicles. For collisions data that includes details about people and vehicles, use the Traffic Collisions Details dataset.

In 2022, Texas was the state recording the highest volume of fatalities resulting from alcohol-impaired driving at ***** number of fatalities, followed by California, which reported around ***** fatalities from drunk driving. These two states alone, out of 51, account for nearly ********* of the total fatalities incurred by alcohol-impaired driving. The Golden state amounted to the largest share of motor vehicle registrations in the country in 2021.

Mexico Traffic Accidents: Baja California data was reported at 39.000 Number in Dec 2017. This records an increase from the previous number of 28.000 Number for Nov 2017. Mexico Traffic Accidents: Baja California data is updated monthly, averaging 37.000 Number from Jan 2012 (Median) to Dec 2017, with 72 observations. The data reached an all-time high of 52.000 Number in Oct 2015 and a record low of 22.000 Number in Sep 2016. Mexico Traffic Accidents: Baja California data remains active status in CEIC and is reported by Secretary of Communications and Transportations. The data is categorized under Global Database’s Mexico – Table MX.TA011: Traffic Accidents: by State.

Statistic crash records within State of California

Mexico Traffic Accidents: Injured: Baja California data was reported at 30.000 Person in Dec 2017. This records an increase from the previous number of 15.000 Person for Nov 2017. Mexico Traffic Accidents: Injured: Baja California data is updated monthly, averaging 31.500 Person from Jan 2012 (Median) to Dec 2017, with 72 observations. The data reached an all-time high of 58.000 Person in Jun 2013 and a record low of 5.000 Person in Sep 2016. Mexico Traffic Accidents: Injured: Baja California data remains active status in CEIC and is reported by Secretary of Communications and Transportations. The data is categorized under Global Database’s Mexico – Table MX.TA011: Traffic Accidents: by State.

Context

My father started riding a motorcycle when I was in high school and he only stopped recently. A few years ago I wondered how risky it was and started looking for some data to answer that question. What I found was a treasure trove of data: every traffic collision from 2001 to present in the state of California.

Content

This data comes from the California Highway Patrol and covers collisions from January 1st, 2001 until mid-October, 2020. I have requested full database dumps from the CHP four times, once in 2016, 2017, 2018, and 2020. I have combined these datasets into the one presented here. For additional details, see my post: Introducing the SWITRS SQLite Hosted Dataset

There are three main tables:

• collisions: Contains information about the collision, where it happened, what vehicles were involved.
• parties: Contains information about the groups people involved in the collision including age, sex, and sobriety.
• victims: Contains information about the injuries of specific people involved in the collision.

There is also a table called case_ids which I used to build the other tables. It tells you which of the four original datasets each row came from.

There is a data dictionary here: https://tims.berkeley.edu/help/SWITRS.php I have in some cases remapped values so that they are human readable (making a left turn instead of D for example); you can find those mappings here: https://github.com/agude/SWITRS-to-SQLite/blob/master/switrs_to_sqlite/value_maps.py

Acknowledgements

This data would not exist without the California Highway Patrol compiling it, thanks!

Inspiration

There is SO much to explore and discover in this data!

Here are some of the things I have looked into:

• On What Days Do Cyclists Crash?
• On What Days Do Motorcycles Crash?
• On What Days Do People Crash?
• Car Crashes After Daylight Saving Time
• Car Crashes After Daylight Saving Time Ends

And here are questions I'd like to answer:

• Task: When do different makes and models of motorcycles crash?
• On what days are pedestrians involved in collisions?
• Are DUIs more likely on certain days?
• How has COVID19 changed collisions? Are different types of vehicles involved? At different times?
• What type/color of car/motorcycle is involved in the most crashes?
• Can we predict injuries based on the other variables in a collision?
• Are their more accidents around sunrise/sunset?

Bugs

Found a problem with the data? Please post a bug report (or a PR to fix the problem) on the ETL script's Github page: SWITRS-to-SQLite. Thanks!