Daily vehicle miles traveled (VMT) is a distance- and volume-based measure of driving on roadways for all motorized vehicle types—car, bus, motorcycle, and truck—on an average day. Per capita VMT is the same measure divided by the same area's population for the same year. Per vehicle VMT divides VMT by the number of household vehicles available by residents of that geography in the same year. These three value types can be selected in the dropdown in the first chart below. Use the legend items to explore various geographies. The second chart below shows per capita and total personal vehicles available to the region’s households from the American Community Survey.

Normalizing VMT by a county or region's population, or household vehicles, is helpful for context, but does not have complete parity with what is measured in VMT estimates. People and vehicles come into the region from other places, just as people and vehicles leave the region to visit other places. VMT per capita compares all miles traveled on the region's roads to the region's population (for all ages) from the U.S. Census Bureau's latest population estimates. Vehicle counts for VMT are classified by vehicle types, but not by vehicle ownership. In 2017, statewide estimates for VMT by motorcycles, passenger cars, and two-axle single-unit trucks with four wheels made up 88% of Pennsylvania's VMT, and 95% of New Jersey's. These vehicle types are highly likely to be personal vehicles, owned by households, but a small percent could be fleet vehicles of companies or governments. The remaining VMT is made up of vehicle types like school and commercial buses and trucks with more than two axles so they are highly likely to be commercial vehicles.

Graph and download economic data for Moving 12-Month Total Vehicle Miles Traveled (M12MTVUSM227NFWA) from Dec 1970 to Sep 2025 about miles, travel, vehicles, and USA.

The number of vehicle-miles traveled on all roads in the United States decreased by some 1.55 percent to approximately 3.17 trillion in 2022. Records for 2019 reported the highest annual level on record, at just under 3.3 trillion vehicle-miles traveled.

United States Ave Vehicle Miles Traveled per Household: 1 Person data was reported at 7,100.000 Mile in 2009. This records a decrease from the previous number of 7,500.000 Mile for 2001. United States Ave Vehicle Miles Traveled per Household: 1 Person data is updated yearly, averaging 7,500.000 Mile from Dec 1991 (Median) to 2009, with 3 observations. The data reached an all-time high of 11,400.000 Mile in 1991 and a record low of 7,100.000 Mile in 2009. United States Ave Vehicle Miles Traveled per Household: 1 Person data remains active status in CEIC and is reported by Center for Transportation Analysis. The data is categorized under Global Database’s USA – Table US.TA005: Vehicles Miles Traveled per Household.

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.

The attached files include the R code that executes the analysis in the paper and the subset of the data used in the paper that is public. With the public data only, the code will execute some of the analysis fully and produce error messages where non-public data are needed. Proprietary data used in the analysis may be purchased from IHS/Polk (https://ishmarkit.com/products/products/automotive-market-data-analysis.html) and Ward's Automotive (https://subscribers.wardsintelligence.com/data-center) to run the full analysis.

US State Transportation & Health

Modes, Fatalities, and Physical Activity

By Department of Transportation [source]

About this dataset

This dataset provides a comprehensive look at the transportation and health of each US state. Included are important indicators such as commute mode share (auto, transit, bicycle and walk), complete streets policies, person miles of travel by private vehicle and walking, physical activity from transportation sources, road traffic fatalities exposure rates (auto, bicycle and pedestrian), seat belt use, transit trips per capita, use of federal funds for bicycle/pedestrian efforts, vehicle miles traveled per capita and proximity to major roadways. All these parameters allow for a comprehensive evaluation of the health state in regards to transportation. Thus allowing users to gain insights into the way different states go about their fundamental transport practices that may have implications on their overall health. This tool will allow you to compare different states across these variables in order to make correlations between policy choices and public health outcomes over time – equipping decision makers with crucial information that could help make data-driven decisions in the future

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How to use the dataset

This dataset contains transportation and health information for every state in the US. This data can be used to gain a better understanding of how transportation affects our health and quality of life.

To use this dataset, you first need to understand what each column means. The columns are: State, Commute Mode Share - Auto, Commute Mode Share - Transit, Commute Mode Share - Bicycle, Commute Mode Share - Walk , Complete Streets Policies, Person Miles of Travel by Private Vehicle , Person Miles of Travel by Walking , Physical Activity from Transportation , Road Traffic Fatalities Exposure Rate- Auto , Road Traffic Fatalities Exposure Rate- Bicycle , Road Traffic Fatalities Exposure Rate-Pedestrian , Seat Belt Use Transit Trips per Capita Use of Federal Funds for Bicycle and Pedestrian Efforts Vehicle Miles Traveled per Capita Proximity to Major Roadways . Each column describes a different aspect related to transportation and health in the US states such as the number commuters who drive their own car or those who use the public transit system.

Once you understand what each column represents you can start exploring different states’ data on that particular feature with statistics such as mean value or maximum/minimum value or visualize it in charts/graphs. Additionally, you can look at correlations between different features across multiple states and try to see if they have any relationship or not. You may also want to combine multiple columns together in order create new metrics (or score) that can be compared across all the states (e.g., calculate a “Commuting Score” based on commute mode share for private vehicle/transit/bicycle). Once your analysis is complete you should have an idea about which state has better (or worse) conditions concerning transportation & health indicators and draw conclusions from there!

Research Ideas

• Creating an interactive map of the US illustrating transportation and health data from each state.
• Developing predictive models to forecast the impact of different transportation policies on health outcomes in various states.
• Identifying correlations between changes in transit mode share and road traffic fatalities/injuries based on locations/states within the US over a particular period of time

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: THT_Data_508.csv | Column name | Description | |:----------------------------------------------|:------------------------------------------------------------------------------| | State | The name of the US state. (String) | | Commute Mode Share - Auto | The score assigned to the commute mode share for auto. (Number) | | **Commute Mode Share ** | Score | | Commute Mode Share - Transit | The score assigned to the commute mode share for transit. (Number) ...

This statistic shows the average person trip length in the U.S. in 2017. Commuting to or from work is on average **** miles. On average, this category accounted for over one out of every six person trips per household in the United States.

Estimates of average weekday household person trips, vehicle trips, person miles traveled, and vehicle miles traveled (per day), for all Census tracts in the United States for 2017.

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.

The graph displays the average miles driven per person in the United States from 1980 to 2023. The x-axis represents the years, while the y-axis shows the average miles driven annually by one person. The data shows that the lowest average was 10,511 miles in 1980, and the highest was 14,906 miles in 2004. A notable drop occurred in 2020, with the average falling to 12,724 miles, likely reflecting reduced travel during the COVID-19 pandemic. Overall, the data highlights a long-term increase in driving over the decades, with fluctuations in recent years.

Estimates of average weekday household person trips, vehicle trips, person miles traveled, and vehicle miles traveled (per day), for all Census tracts in the United States for 2009. For latest data (2017), see https://data.bts.gov/Research-and-Statistics/Local-Area-Transportation-Characteristics-by-House/va72-z8hz For methodology, see attachments

Estimates of average weekday household person trips, vehicle trips, person miles traveled, and vehicle miles traveled (per day), for all Census tracts in the United States for 2009.

平均每户行车里程：1人在12-01-2009达7,100.000Mile，相较于12-01-2001的7,500.000Mile有所下降。平均每户行车里程：1人数据按年更新，12-01-1991至12-01-2009期间平均值为7,500.000Mile，共3份观测结果。该数据的历史最高值出现于12-01-1991，达11,400.000Mile，而历史最低值则出现于12-01-2009，为7,100.000Mile。CEIC提供的平均每户行车里程：1人数据处于定期更新的状态，数据来源于Center for Transportation Analysis，数据归类于全球数据库的美国 – 表US.TA005：每户行车里程。

One measure used to analyze roadway reliability is the Planning Time Index (PTI). It is the ratio of the 95th percentile travel time relative to the free-flow (uncongested) travel time. PTI helps in understanding the impacts of nonrecurring congestion from crashes, weather, and special events. It approximates the extent to which a traveler should add extra time to their trip to ensure on-time arrival at their destination. A value of 1.0 indicates a person can expect free-flow speeds along their route. A 2.0 index value indicates a traveler should expect that the trip could be twice as long as free-flow conditions. PTI values from 2.0 to 3.0 indicate moderate unreliability, and ones greater than 3.0 are highly unreliable.

The data comes from aggregated Global Positioning System probe data—anonymized data from mobile apps, connected vehicles, and commercial fleets—provided to the Probe Data Analytics (PDA) Suite by INRIX, a travel data technology company. The PDA Suite was created by a consortium of sponsors, including the Eastern Transportation Coalition and the University of Maryland.

PTI values by region, subregion, and county are grouped either as highway facilities or local roads. Highways are roadway segments classified as interstates, turnpikes, and expressways in the PDA Suite. Local roads are segments classified as U.S. routes, state routes, parkways, frontages, and others. The PDA Suite reports weekday hourly averages by facility type and direction. Average weekday values are reported by facility type and direction, within the following time periods:

• Morning (AM): 6:00 AM–9:59 AM;
• Midday (MD): 10:00 AM–2:59 PM;
• Evening (PM): 3:00 PM–6:59 PM;
• Nighttime (NT): 7:00 PM–5:59 AM; and
• Daily: 12:00 AM–11:59 PM.

Although INRIX data collection precedes years reported in Tracking Progress, early years of reporting are highly variable based on a lack of facility coverage. The years from 2011 onward show higher stability for highway facilities for most counties and for the region. For local facilities, 2014 and beyond is where values seem most stable due to more widespread facility coverage.

Historic data for the federal Transportation Performance Management (TPM) system performance reporting requirements is shown. These are Level of Travel Time Reliability (LOTTR), Level of Truck Travel Time Reliability (TTTR), and Annual Hours of Peak-Hour Excessive Delay (AHPHED). The entire states of Pennsylvania and New Jersey are included for LOTTR and TTTR, so the region’s figures can be compared with statewide data.

LOTTR is used to calculate the percentage of roadway segments that are considered reliable. A road segment with an LOTTR of less than 1.5 is considered reliable. Reliable segment lengths in miles are multiplied by their Annual average daily traffic (AADT) values times the average number of people in a vehicle. Then, this sum is then divided by the exact same product for all road segments, to get the resulting percentage of roadway that is reliable for the geography.

TTTR measures how consistent travel times are for trucks on interstates. This can be helpful with analyzing goods movement along the region’s interstates. TTTR is calculated by dividing the 95th percentile of travel times by the 50th percentile of travel times, using the highest value over the Morning (AM), Midday (MD), Evening (PM), Nighttime (NT), and weekend. Each interstate segment multiplies its length by the travel time ratio, the results are summed and then divided by total Interstate length in the geography to determine the area’s TTTR value.

AHPHED is the average number of hours per year spent by motorists driving in congestion during peak periods. This can be useful for analyzing the impact of congestion from an individual’s perspective, since it analyzes how many hours the average person spends stuck in congestion. The figures used are based on the 2010 urbanized area boundaries in the Census. In 2020, they were renamed to urban areas. There are only Mercer County PHED values from 2021 onward, because they only apply to the second four-year TPM performance period, when the Trenton, NJ Urban Area was required to track metrics and set performance targets. AHPHED per capita is that figure divided by the urban area’s population during that year.

It is also important to measure PTIs along the roads buses travel, to measure how reliable the roads are that commuters travel on. To calculate the agency and division type combination PTIs, for each route, all their segments’ planning times from 7-8 AM, 8-9 AM, 4-5 PM, and 5-6 PM are first summed. Then, those are divided by the sums of those segments' free-flow travel times for those same time periods, to get one PTI per time period for each route. Then, the highest of those four PTIs is taken to get one maximum peak hour PTI per route. Then, for each agency and division type combination, all of their routes’ maximum peak hour PTIs are averaged for each year to get the PTIs. Since all NJ Transit routes in the DVRPC region are part of their Southern Division, NJ Transit only has one agency and division mode combination. SEPTA has two: “City” and “Suburban”. SEPTA splits their bus routes into their urban routes, all within their City Transit Division, and their suburban routes, which are in their Victory and Frontier divisions. The Victory and Frontier divisions have been grouped into their own “Suburban” division type.

Congestion is susceptible to external forces like the economy. A downturn can reduce congestion, but this reflects fewer and shorter trips for households and businesses during lean times and may not represent an improvement. Therefore, it may be useful to correlate these results with the Miles Driven indicator.