The National Highway System consists of a network of roads important to the economy, defense and mobility. On October 1, 2012 the existing National Highway System (NHS) was expanded to include all existing Principal Arterials (i.e. Functional Classifications 1, 2 and 3) to the new Enhanced NHS. Under MAP-21, the Enhanced NHS is composed of rural and urban roads nationwide serving major population centers, international border crossings, intermodal transportation facilities, and major travel destinations.The NHS includes:The Interstate System. Other Principal arterials and border crossings on those routes (including other urban and rural principal arterial routes, and border crossings on those routes, that were not included on the NHS before the date of enactment of the MAP-21).Intermodal connectors -- highways that provide motor vehicle access between the NHS and major intermodal transportation facilities. STRAHNET -- the network of highways important to U.S. strategic defense. STRAHNET connectors to major military installations.

Daily Miles Traveled (T14)

FULL MEASURE NAME
Total vehicle miles traveled

LAST UPDATED
August 2022

DESCRIPTION
Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for total vehicle miles traveled.

DATA SOURCE
California Department of Transportation: California Public Road Data/Highway Performance Monitoring System - http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php
2001-2020

Federal Highway Administration: Highway Statistics - https://www.fhwa.dot.gov/policyinformation/statistics/2020/hm71.cfm
2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2001-2020

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

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Vehicle miles traveled (VMT) reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examines county and regional data, where through-trips are generally less common.

The metropolitan area comparison was performed by summing all of the urbanized areas for which the majority of its population falls within a given metropolitan area (9-county region for the San Francisco Bay Area and the primary metropolitan statistical area (MSA) for all others). For the metro analysis, no VMT data is available in rural areas; it is only available for intraregional analysis purposes. VMT per capita is calculated by dividing VMT by an estimate of the traveling population.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

Annual average daily traffic is the total volume for the year divided by 365 days. The traffic count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Traffic Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.Traffic Census Program Page

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.

HPMS compiles data on highway network extent, use, condition, and performance. The system consists of a geospatially-enabled database that is used to generate reports and provides tools for data analysis. Information from HPMS is used by many stakeholders across the US DOT, the Administration, Congress, and the transportation community.

Daily Miles Traveled (T14)

FULL MEASURE NAME
Total vehicle miles traveled

LAST UPDATED
August 2022

DESCRIPTION
Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for total vehicle miles traveled.

DATA SOURCE
California Department of Transportation: California Public Road Data/Highway Performance Monitoring System - http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php
2001-2020

Federal Highway Administration: Highway Statistics - https://www.fhwa.dot.gov/policyinformation/statistics/2020/hm71.cfm
2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2001-2020

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

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Vehicle miles traveled (VMT) reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examines county and regional data, where through-trips are generally less common.

The metropolitan area comparison was performed by summing all of the urbanized areas for which the majority of its population falls within a given metropolitan area (9-county region for the San Francisco Bay Area and the primary metropolitan statistical area (MSA) for all others). For the metro analysis, no VMT data is available in rural areas; it is only available for intraregional analysis purposes. VMT per capita is calculated by dividing VMT by an estimate of the traveling population.

The Caltrans All Roads Linear Referencing Services (LRS) dataset provides the base geometry for federally required Highway Performance Monitoring System (HPMS) business data, functionally classified roads for the California Roads System (CRS) (a requirement for federal funding of local agency projects), and the State Highway Network (SHN), which supports a wide range of internal Caltrans business needs. Description The Federal Highway Administration (FHWA) requires all state DOT's to develop and submit a Linear Referencing System (LRS) network for all public roads in their respective states known as the All Roads Network of Linear Referenced Data (ARNOLD). This ARNOLD requirement is an integral part of each state’s federally mandated Highway Performance Monitoring System (HPMS) annual submittal. To meet the ARNOLD requirement, the Division of Research, Innovation and System Information (DRISI) has developed a representation of all roads in California using a combination of the Census Bureau’s Topologically Integrated Geographic Encoding and Reference (TIGER) files and previously developed line work representing the State Highway System. This data is published publicly.

Named Freeways, Highways, Structures and Other Appurtenances in California Publication Named Freeways, Highways, Structures and Other Appurtenances in California are shown in the Name Freeway Publication as a reference to the many named facilities that are part of the California State Highway System. This publication provides information on officially named freeways; highways; structures such as bridges, tunnels, and interchanges; Blue Star Memorial Highways; Safety Roadside Rest Areas; and memorial plaques.

This dataset provides information about the number of properties, residents, and average property values for Highway 50 cross streets in California, MO.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not bedivided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

Context

I curate a dataset of California traffic collisions, which you can find here: Kaggle: California Traffic Collision Data from SWITRS.

However, I made certain decisions about how to clean and process the data, which others might disagree with. This dataset contains the raw data, allowing you to make your own cleaning decisions!

Content

This data comes from the California Highway Patrol and covers collisions from January 1st, 2001 until mid-December, 2020. I have requested full database dumps from the CHP four times, once in 2016, 2017, 2018, 2020, 2021. These are the raw files as provided by the CHP with no post-processing

Acknowledgements

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

VITAL SIGNS INDICATOR Injuries From Crashes (EN4-6)

FULL MEASURE NAME Fatalities from crashes (traffic collisions)

LAST UPDATED October 2017

DESCRIPTION Fatalities from crashes refers to deaths as a result of injuries sustained in collisions. The California Highway Patrol includes deaths within 30 days of the collision that are a result of injuries 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

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) The data is reported by the National Highway Safety Administration's Fatalities Analysis Reporting System. 2016 data comes from the California Highway Patrol (CHP) to the Statewide Integrated Traffic Records System (SWITRS), which was accessed via SafeTREC’s Transportation Injury Mapping System (TIMS). The data was tabulated using provided categories specifying injury level, individuals involved, causes of collision, and location/jurisdiction of collision (for more: http://tims.berkeley.edu/help/files/switrs_codebook.doc). Fatalities were normalized over historic population data from the US Census and California Department of Finance and vehicle miles traveled (VMT) data from the Federal Highway Administration.

For more regarding reporting procedures and injury classification see the California Highway Patrol Manual (http://www.nhtsa.gov/nhtsa/stateCatalog/states/ca/docs/CA_CHP555_Manual_2_2003_ch1-13.pdf).

This dataset provides information about the number of properties, residents, and average property values for Highway F cross streets in California, MO.

This dataset provides information about the number of properties, residents, and average property values for Highway Cc cross streets in California, MO.

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.

The Highway-Runoff Database (HRDB) was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway runoff on the Nation's receiving waters (Granato and Cazenas, 2009; Granato, 2013; 2019; Granato and others, 2018; Granato and Friesz, 2021). The HRDB was assembled by using a Microsoft Access database application to facilitate use of the data and to calculate runoff-quality statistics with methods that properly handle censored-concentration data. The HRDB was first published as version 1.0 in cooperation with the FHWA in 2009 (Granato and Cazenas, 2009). The second version (1.0.0a) was published in cooperation with the Massachusetts Department of Transportation Highway Division to include data from Ohio and Massachusetts (Smith and Granato, 2010). The third version (1.0.0b) was published in cooperation with FHWA to include a substantial amount of additional data (Granato and others, 2018; Granato and Jones, 2019). The fourth version (1.1.0) was updated with additional data and modified to provide data-quality information within the Graphical User Interface (GUI), calculate statistics for multiple sites in batch mode, and output additional statistics. The fifth version (1.1.0a) was published in cooperation with the California Department of Transportation to add highway-runoff data collected in California. The sixth version published in this release (1.2.0) has been updated to include additional data, correct data-transfer errors in previous versions, add new parameter information, and modify the statistical output. This version includes data from 270 highway sites across the country (26 states); data from 8,108 storm events; and 119,224 concentration values with data for 418 different water-quality constituents or parameters.

This statistic represents the annual highway use of motor fuel in California in 2009 and 2016, with a breakdown by fuel type. In 2016, approximately **** billion gallons of gasoline were used on highways in California.

Financial overview and grant giving statistics of California Highway Patrolmans Club

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