This table contains data on the annual miles traveled by place of occurrence and by mode of transportation (vehicle, pedestrian, bicycle), for California, its regions, counties, and cities/towns. The ratio uses data from the California Department of Transportation, the U.S. Department of Transportation, and the U.S. Census Bureau. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Miles traveled by individuals and their choice of mode – car, truck, public transit, walking or bicycling – have a major impact on mobility and population health. Miles traveled by automobile offers extraordinary personal mobility and independence, but it is also associated with air pollution, greenhouse gas emissions linked to global warming, road traffic injuries, and sedentary lifestyles. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which has many documented health benefits. More information about the data table and a data dictionary can be found in the About/Attachments section.

VITAL SIGNS INDICATOR

Daily Miles Traveled (T14)

FULL MEASURE NAME

Total vehicle miles traveled

LAST UPDATED

July 2017

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

2001-2015

http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php

CONTACT INFORMATION

vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)

Vehicle miles traveled 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 examine 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-nine region for the San Francisco Bay Area and the primary MSA for all others). For the metro analysis, no VMT data is available in rural areas; it is only available for intraregional analysis purposes.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

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 map shows that the San Francisco Bay Region has among the lowest vehicle miles traveled (VMT) per capita of any metropolitan area in the state.

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.

In 2013, then-Governor Jerry Brown signed Senate Bill (SB 743) into law. Pursuant to that direction, the Governor’s Office of Planning and Research (OPR) and the California Natural Resources Agency promulgated regulations and technical guidance that eliminated automobile level of service (LOS) – a measure of automobile delay – as a transportation impact metric for land development projects under the California Environmental Quality Act (CEQA), and replaced it with Vehicle Miles Traveled (VMT) – a measure of the amount of vehicular travel. Actual implementation of the LOS-to-VMT shift was left up to lead agencies—the agencies with primary approval authority over a given project, which for land development projects is usually leading a local government (city or county). Agencies were required to start using a VMT-based metric by July 1, 2020. Using LOS as the guiding metric for transportation impacts prioritizes vehicular flows and speed. As a result, it has had increasingly well-recogniz..., ,

This dataset was provided to the San Francisco Planning Department from the San Francisco County Transportation Authority (Transportation Authority) in March 2016. The Transportation Authority uses the San Francisco Chained Activity Model Process (SF-CHAMP) to estimate vehicle miles traveled (VMT) by private automobiles and taxis for different land use types. Travel behavior in SF-CHAMP is calibrated based on observed behavior from the California Household Travel Survey 2010-2012, Census data regarding automobile ownership rates and county-to-county worker flows, and observed vehicle counts and transit boardings. SF-CHAMP uses a synthetic population, which is a set of individual actors that represents the Bay Area’s actual population, who make simulated travel decisions for a complete day. VMT estimates are provided for existing (2012) and future (2040) for three land use types: office, residential, and retail. For these estimates, Transportation Authority used tour-based analysis for office and residential uses and a trip-based analysis for retail uses. The aforementioned VMT estimates are provided for each transportation analysis zone within San Francisco and compared to regional averages. Refer to March 3, 2016 San Francisco Planning Commission Transportation Sustainability Program staff report for further details. The data is in a zipped file geodatabase (GIS) format. Geographic boundaries are Traffic Analysis Zones (TAZ's).

In 2018, test drivers in Waymo vehicles in California covered almost *** million miles in autonomous mode and had to take over control of their vehicles some *** times, meaning that there were **** disengagements per 1,000 miles driven. Meanwhile, test drivers in Uber vehicles had a much more interesting job: Uber reported over ***** disengagements per 1,000 miles driven.

In this study, we primarily use a cohort survey of PEV owners in California administered by the authors in November 2019. Respondents had been previously surveyed by the PH&EV Research Center between 2015 and 2018 as part of the four phases of the ‘eVMT survey’ when they originally bought the PEV tracked in the repeat survey in 2019 (households may have other PEVs in their fleet). Respondents for the four phases of the eVMT survey were sampled from the pool of PEV buyers who had applied for the state rebate from the California Vehicle Rebate Program (CVRP). More than 25,000 PEV owners were surveyed between 2015 and 2018. 15,000 of these respondents gave consent to be re-contacted and were invited for the repeat survey in 2019. 4,925 PEV owners responded to the repeat survey. The sample is a convenience sample. Most survey respondents have several vehicles in their household. For the purpose of this study we consider the vehicle most frequently used by the survey taker. We ...

This study helps understand how the anticipated emergence of autonomous vehicles will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants. The study begins with a literature review on connected and automated vehicle (CAV) technology for light-duty vehicles, the factors likely to affect CAV adoption, expected impacts of CAVs, and approaches to modeling these impacts. The study then uses a set of modifications in the California Statewide Travel Demand Model (CSTDM) to simulate the following scenarios for the deployment of passenger light-duty CAVs in California by 2050: (0) Baseline (no automation); (1) Private CAV; (2) Private CAV + Pricing; (3) Private CAV + Zero emission vehicles (ZEV); (4) Shared CAV; (5) Shared CAV + Pricing; (6) Shared CAV + ZEV. The modified CSTDM is used to forecast travel demand and mode share for each scenario, and this output is used in combination with the emission factors from the EMission FACtor model (EMFAC) and Vision model to calculate energy consumption and criteria pollutant emissions. The modeling results indicate that the mode shares of public transit and in-state air travel will likely sharply decrease, while total vehicle miles traveled and emissions will likely increase, due to the relative convenience of CAVs. The study also reveals limitations in models like the CSTDM that primarily use sociodemographic factors and job/residence location as inputs for the simulation of activity participation and tour patterns, without accounting for some of the disruptive effects of CAVs. The study results also show that total vehicle miles traveled and vehicle hours traveled could be substantially impacted by a modification in future auto travel costs. This means that the eventual implementation of pricing strategies and congestion pricing policies, together with policies that support the deployment of shared and electric CAVs, could help curb tailpipe pollutant emissions in future scenarios, though they may not be able to completely offset the increases in travel demand and road congestion that might result from CAV deployment. Such policies should be considered to counteract and mitigate some of the undesirable impacts of CAVs on society and on the environment.

The data is based upon traffic volume trends data collected by the United States Department of Transportation data from January 1971 to February 2013.Since June 2005, vehicle miles driven have fallen 8.75 percent. This decline has remained steady for the past 92 months. There are several reasons that may be causing this steady downward trend. It has been suggested that due to rising gas prices, the Great Recession, an aging population led by the Baby Boom generation which is comprised of Americans over the age of 55 who tend to drive less, and quite possibly younger Americans choosing to drive less. Between 2001 and 2009, the average yearly number of miles driven by 16- to 34-year-olds has dropped 23 percent.Researchers indicate that this trend may be linked to five principal factors:The cost of Driving has increasedThe recent recessionIt is harder to get a license in many statesMore younger people are choosing to live in transit-oriented areas andTechnology is making it easier to go car-freeData Source Information: Traffic Volume Trends is a monthly report based on hourly traffic count data reported by the States. These data are collected at approximately 4,000 continuous traffic counting locations nationwide and are used to estimate the percent change in traffic for the current month compared with the same month in the previous year. Estimates are re-adjusted annually to match the vehicle miles of travel from the Highway Performance Monitoring System and are continually updated with additional data.

Data Source: California Office of Traffic Safety

This data biography shares the how, who, what, where, when, and why about this dataset. We, the epidemiology team at Napa County Health and Human Services Agency, Public Health Division, created it to help you understand where the data we analyze and share comes from. If you have any further questions, we can be reached at epidemiology@countyofnapa.org.

Data dashboard featuring this data: https://data.countyofnapa.org/stories/s/abqu-wcty

Why was the data collected?  California Office of Traffic Safety (OTS) ranking metric is a tool used to compare similarly sized cities on traffic safety statistics. A smaller the assigned number means that the city is ranked higher, and a higher ranking means the city has worse traffic safety compared to similar locations.

How was the data collected? Crash data comes from Statewide Traffic Records System (SWITRS). This system collects and processes data gathered from a collision scene. Population estimates come from California Department of Finance (DoF), which are based on changes in births, deaths, domestic migration, and international migration. Estimates are developed using aggregate data from a variety of sources, including birth and death counts provided by the Department of Public Health, driver's license data from the Department of Motor Vehicles, housing unit data from local governments, school enrollment data from the Department of Education, and federal income tax return data from the U.S. Internal Revenue Service. Daily Vehicle Miles Traveled (DVMT) come from California Department of Transportation (Caltrans). The Traffic Data Branch at Caltrans estimates the number of vehicle miles that motorists traveled on California State Highways using a sampling of up to 20 traffic monitoring sites and reports on that data. Crash rankings are based on a ranking method that assigns statistical weights to categories including observed crash counts, population, and vehicle miles traveled. Counties are assigned statewide rankings, while cities are assigned population group rankings. DUI arrests data comes from the Department of Justice.

Who was included and excluded from the data & Where was the data collected? Data for the rankings is taken from Incorporated cities only. This includes local streets and state highways within city limits that share jurisdiction with the CHP. DUI arrest data is only available for cities that report it to the Department of Justice. Data from the OTS crash was sources specifically for Napa County, the City of Napa, American Canyon, Calistoga, St. Helena and Yountville.

When was the data collected? 2017-2022

Where can I learn more about this data? Office of traffic safety: https://www.ots.ca.gov/media-and-research/crash-rankings/ Methodology: https://rosap.ntl.bts.gov/view/dot/24410

The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body and fuel type to project future VMT changes and mobile source emission levels. The current report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. We use the 2019 California Vehicle Survey data here that allows us to analyze the driving behavior associated with more recent EV models (with potentially longer ranges). Important findings from the model include:

Household characteristics like size or having children have an expected impact on vehicle preference: larger vehicles are preferred. College education, rooftop solar ownership, and the number of employed workers in a household affect the preference for BEVs and PHEVs in the small car segment dominated by the Leaf, Bolt, Prius-Plug-in and the Volt often used as a commuter car. Among built environment factors, population density and the walkability index of a neighborhood have a statistically significant impact on the type of vehicle choice and VMT. It is observed that a 10% increase in population density reduces the preference for ICEV pickup trucks by 0.34% and VMT by 0.4%. However, if the increase in population density is 25%, the reduction in preference for pickup trucks is 8.4% and VMT is 8.6%. The other built environment factor we consider is the walkability index. If the walkability index of a neighborhood increases by 25%, it reduces the preference for ICEV pickup trucks by 15% and their VMT by 16%. Overall, these results suggest that if policies encourage mixed development of neighborhoods and increase density, it can have an important impact on ownership and usage of gas guzzlers like pickup trucks and help in the process of electrification of the transportation sector.

Methods The dataset used in this report was created using the following public data sources:

2019 California Vehicle Survey: "Transportation Secure Data Center." ([2019]). National Renewable Energy Laboratory. Accessed [04/26/2023]: www.nrel.gov/tsdc. The Smart Mapping Tool by EPA: https://www.epa.gov/smartgrowth/smart-location-mapping

American Community Survey: https://www.census.gov/programs-surveys/acs

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.

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.

Vehicle miles traveled increased 4.2% year-over-year in July 2015, continuing an upward trajectory that started in March 2014. This ended an unprecedented five-year period of flat rolling 12-month totals following the ‘Great Recession’ of December 2007-June 2009. Cheaper gasoline prices likely account for some of the increase, with the national average per gallon declining from $3.69 in July 2014 to $2.88 in July 2015. The rebound of the national economy, increased job growth, and expanding population are additional factors.

The project needs data for macroscopic statistical modeling, which are OTS rankings and historical crash data. OTS crash ranking data California Office of Traffic Safety (OTS) provides a crash ranking dataset that was developed so that individual cities could compare their city’s traffic safety statistics to those of other cities with similar-sized populations. The OTS crash rankings are based on the Empirical Bayesian Ranking Method. It adds weights to different crash statistical categories including observed crash counts, population and daily vehicle miles traveled (DVMT). In addition, the OTS crash rankings include different types of crashes with larger percentages of total victims and areas of focus for the OTS grant program. In conjunction with the research context, two types of crash rankings are focused on, namely pedestrians and bicyclists. SWITRS crash data The Transportation Injury Mapping System (TIMS) to provide the project quick, easy, and free access to California crash d..., The Safe Transportation Research and Education Center (SafeTREC) at the University of California, Berkeley, develops the Transportation Injury Mapping System (TIMS) to provide a quick, easy and free access to California crash data provided by the Statewide Integrated Traffic Records System (SWITRS). We collect five-year-long crash data, which are from 01/01/2014 to 12/31/2018. The crash data includes bicyclist and pedestrian collisions with vehicles resulting in injuries across four types of crash severity: fatal, severe injury, visible injury, and complaint of injury. The data consists of three tables including the collision dataset, the involved parties dataset, and the victims dataset. In particular, we use the collision and parties datasets that contain enough information for modeling. The rows in the crash data are built based on each case of a crash and includes information such as weather, road surface, road condition, control device, and lighting. The parties dataset includes in..., The data files can be viewed by Excel.Â

Context

Each company which hold a permit to test self-driving vehicles in California must report disengagements to the state's Department of Motor Vehicles. The department defines a disengagement as "a deactivation of the autonomous mode when a failure of the autonomous technology is detected or when the safe operation of the vehicle requires that the autonomous vehicle test driver disengage the autonomous mode and take immediate manual. control of the vehicle." This dataset of disengagement reports can highlight where the technology is still struggling.

Content

"The data files below contain the disengagements and autonomous miles traveled for permit holders who reported testing on California’s public roads between December 1, 2018 and November 30, 2019. Separate data files contain information on companies who received their permit in 2018 and are reporting testing activity in California for the first time (beyond the normal 12-month cycle)." Each report includes:

Manufacturer Permit Number DATE VIN NUMBER VEHICLE IS CAPABLE OF OPERATING WITHOUT A DRIVER(Yes or No) DRIVER PRESENT(Yes or No) DISENGAGEMENT INITIATED BY(AV System, Test Driver, Remote Operator, or Passenger) DISENGAGEMENTLOCATION (Interstate, Freeway, Highway, Rural Road, Street, or Parking Facility) DESCRIPTION OF FACTS CAUSING DISENGAGEMENT

Acknowledgements

Original data from State of California Department of Motor Vehicles. https://www.dmv.ca.gov/portal/dmv/detail/vr/autonomous/disengagement_report_2019

Banner photo by Andrew Roberts on Unsplash https://unsplash.com/photos/6lqk_bNnw_c

This study investigates how stakeholders throughout the state of California view the potential impacts of ridehailing services such as Uber or Lyft, to transportation systems, and how to address such impacts. Ridehailing is one of several emerging shared use mobility alternatives, poised to impact transportation systems, for better or worse. For better if these new services catalyze the development and maturation of well-integrated multi-model transportation systems that serve all travelers and reduce vehicle miles travelled (VMT) and transportation emissions. For worse if these new services serve merely as a less expensive taxi, allowing more people to forego alternative modes of transportation like public transit and biking, thereby leading to increases in VMT and emissions and worsening congestion impacts. The high degree of uncertainty surrounding the impacts of these services presents challenges to stakeholders involved in transportation planning and policymaking. How transportation stakeholders view the potential positive and negative impacts of ridehailing and what to do about them is an open question, and one that warrants investigation as these services become more popular and their impacts begin to be understood. Through interviews, we investigate the viewpoints of 42 transportation stakeholders throughout the state of California. We find the diversity of interviewees is reflected in the sentiments they have about ridehailing, what issues are important and potential obstacles to achieving positive outcomes. Nonetheless, interviewees agree that regulations should balance local control with state level guidance.

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.