Tourist Arrivals in the United States decreased to 5634382 in May from 5957985 in April of 2025. This dataset provides - United States Tourist Arrivals- actual values, historical data, forecast, chart, statistics, economic calendar and news.

**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

Overview

Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

Findings

• Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
• Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
• New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least. ## About This Data The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

Included Data

01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

Additional Data Queries

* Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

* Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

* Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

Interactive

The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

@(https://interactives.ap.org/vmt-map/)

Interactive Embed Code

Using the Data

This data can help put your county's mobility in context with your state and over time. The data set contains different measures of change - daily comparisons and seven day rolling averages. The rolling average allows for a smoother trend line for comparison across counties and states. To get the full picture, there are also two available baselines - vehicle miles traveled in January 2020 (pre-pandemic) and vehicle miles traveled at each geography's low point during the pandemic.

Caveats

• The data from StreetLight Data, Inc does not include data for some low-population counties with low VMT (<5,000 miles/day in their baseline month of January 2020). In our analyses, we only include the 2,779 counties that have daily data for the entire period (March 1, 2020 to current).
• In some cases, a lack of decline in mobility from March to April can indicate that movement in the county is essential to keeping the larger economy going or that residents need to drive further to reach essentials businesses like grocery stores compared to other counties.
• The VMT includes both passenger and commercial miles, so truck traffic is included. However, the proxy is based on the "total number of trip starts and ends for all devices whose most frequent location is in this county". It does not count the VMT of trucks cutting through a county.
• For those instances where travel begins in one county and ends in another, the county where the miles are recorded is always the vehicle’s home county. ###### Contact reporter Angeliki Kastanis at akastanis@ap.org.

Number of vehicles travelling between Canada and the United States, by trip characteristics, length of stay and type of transportation. Data available monthly.

Employed persons include people aged 16 years and older in the civilian noninstitutional population who did any work at all as paid employees; worked in their own business, profession, or on their own farm, or worked 15 hours or more as unpaid workers in an enterprise operated by a member of the family; and all those who were not working but who had jobs or businesses from which they were temporarily absent. The Bureau of Labor Statistics produces industry estimates of nonfarm payroll employment as part of the Current Population Survey.

Abstract copyright UK Data Service and data collection copyright owner.The International Passenger Survey (IPS) aims to collect data on both credits and debits for the travel account of the Balance of Payments, provide detailed visit information on overseas visitors to the United Kingdom (UK) for tourism policy, and collect data on international migration. There are three versions of the IPS data, the travelpac version the simplest both in format and ease of use, the reduced version used for fuller queries but restricted in the number of variables and the main data for expert users which contain all analysable variables. Travelpac dataset - for simple data queries, users may prefer to use these data as they are extremely simple and can be joined together to form a time series. The results should be consistent with those published by ONS in Travel Trends. Reduced dataset - the depositor recommends using this dataset for fuller queries as it contains most of the important analysable information and will due to their nature be much easier to understand and tabulate. Although the variable list is the same from year to year, care must be taken with these files when trying to perform time series operations as codes can also vary from year to year for some variables. Main dataset - The depositor recommends that only expert users who are very familiar with the coding and weighting structures use this dataset as limited support is available. Some considerable understanding of the data is required before meaningful analyses can be made, care must be taken when performing time series operations as codes can vary from year to year and not all variables from one years dataset are used in other years. The data comprises four files, ALCOHOL, AIRMILES, QREGTOWN and QCONTACT with four quarters per file, with the exception of ALCOHOL which covers all four quarters in one file. These can be joined together using the variables YEAR, SERIAL, FLOW and QUARTER. The weighting of IPS data is complex and done in several stages. When working with the system weights, great care should be taken to read the documentation concerning weighting procedures as not all records are treated in exactly the same way (this does not apply to the smaller datasets). Main Topics: The travelpac dataset for 1997 contains the following 14 variables: Year; quarter; ukos; mode of travel; purpose of visit; package tour; country of visit/residence; age group; gender; length of stay; visits; expenditure; number of nights spent on trip; sample. The reduced dataset for 1997 contains the following 24 variables: Year; quarter; month; flow; purpose of visit; package tour; country of visit/residence; states within countries for overseas residents; United Kingdom counties; nationality of contact; age group; gender; people; United Kingdom airport, sea route or tunnel mode; direct leg overseas port; final overseas port; ossport; mode of travel used; number in vehicle; type of flight; class of travel; number of nights spent on trip; expenditure; weight of contact. The main dataset for 1997 contains 128 variables and covers: AIRMILES - quarter; month; flow; serial; United Kingdom port or route; direct leg overseas port; final overseas port; distance from United Kingdom port to first port; from first to second port; from United Kingdom port to second port. ALCOHOL - year; quarter; month; flow; serial; money spent on spirits; wine; beer; tobacco. QREGTOWN - year; quarter; month; flow; serial; towns stayed in overnight; number of nights spent in towns; expenditure in towns; regional stay weight; regional visit weight; regional expenditure weight; various validation checks. QCONTACT - year; quarter; month; flow; serial; nationality; country of visit/residence; United Kingdom counties; date visit began; purpose of visit; business reason; intended length of stay; number of people; package tour and cost; expenditure pre, post and during visit; flight prefix and suffix; first and second carrier air or shipping line; direct leg overseas port; final overseas port; long or short haul; United Kingdom airports; type of vehicle; number travelling in vehicle; fare type and cost; class of travel; business trip; type of flight; flight origin and destination; gender; age group; United Kingdom port or route; quality of response; date of interview; money transfer, total expenditure; weighting variables; various validation checks. Multi-stage stratified random sample Face-to-face interview

The Bureau of Transportation Statistics (BTS) Border Crossing Data provide summary statistics for inbound crossings at the U.S.-Canada and the U.S.-Mexico border at the port level. Data are available for trucks, trains, containers, buses, personal vehicles, passengers, and pedestrians. Border crossing data are collected at ports of entry by U.S. Customs and Border Protection (CBP). The data reflect the number of vehicles, containers, passengers or pedestrians entering the United States. CBP does not collect comparable data on outbound crossings. Users seeking data on outbound counts may therefore want to review data from individual bridge operators, border state governments, or the Mexican and Canadian governments.

Abstract copyright UK Data Service and data collection copyright owner.The International Passenger Survey (IPS) aims to collect data on both credits and debits for the travel account of the Balance of Payments, provide detailed visit information on overseas visitors to the United Kingdom (UK) for tourism policy, and collect data on international migration. There are two versions of the IPS data for 1999, the reduced version used for fuller queries but restricted in the number of variables and the main data for expert users which contain all analysable variables. Reduced dataset - the depositor recommends using this dataset for fuller queries as it contains most of the important analysable information and, due to their nature, will be much easier to understand and tabulate. Although the variable list is the same from year to year, care must be taken with these files when trying to perform time series operations as codes can also vary from year to year for some variables. Main dataset - the depositor recommends that only expert users who are very familiar with the coding and weighting structures use this dataset as limited support is available. Some considerable understanding of the data is required before meaningful analyses can be made, care must be taken when performing time series operations as codes can vary from year to year and not all variables from one years dataset are used in other years. The data covers four subject areas, AIRMILES (held as a complete year) ALCOHOL, QREGTOWN and QCONTACT (held quarterly in four files per subject area). These can be joined together using the variables YEAR, SERIAL, FLOW and QUARTER. The weighting of IPS data is complex and done in several stages. When working with the system weights, great care should be taken to read the documentation concerning weighting procedures as not all records are treated in exactly the same way (this does not apply to the smaller dataset). Main Topics: The reduced dataset for 1999 contains the following 25 variables: Year; quarter; month; flow; purpose of visit; package tour; country of visit/residence; states within countries for overseas residents; United Kingdom counties; nationality of contact; age group; gender; people; United Kingdom airport, sea route or tunnel mode; direct leg overseas port; final overseas port; ossport; mode of travel used; number in vehicle; type of flight; class of travel; number of nights spent on trip; expenditure; weight of contact; university degree. The main dataset for 1999 contains 136 variables and covers: AIRMILES - quarter; flow; serial; United Kingdom port or route; direct leg overseas port; final overseas port; distance from United Kingdom port to first port; from first to second port; from United Kingdom port to second port. ALCOHOL - year; quarter; month; flow; serial; money spent on spirits; wine; beer; cigarettes; hand-rolled and other tobacco. QREGTOWN - year; quarter; month; flow; serial; towns stayed in overnight; number of nights spent in towns; expenditure in towns; regional stay weight; regional visit weight; regional expenditure weight; various validation checks. QCONTACT - year; quarter; month; flow; serial; nationality; country of visit/residence; United Kingdom counties; date visit began; purpose of visit; Belgian spoken; business reason; organised conference; intended length of stay; number of people; package tour and cost; expenditure pre, post and during visit; business expenditure; flight prefix and suffix; first carrier air or shipping line; direct leg overseas port; final overseas port; long or short haul; type of vehicle; number travelling in vehicle; fare type and cost; class of travel; business trip; type of flight; flight origin or destination; gender; age group; United Kingdom port or route; quality of response; date of interview; money transfer, total expenditure; university degree; foreign driver; distance driven by foreign driver; miles or kilometres; income group; weighting variables; various validation checks. Multi-stage stratified random sample Face-to-face interview

Tourism Revenues in the United States decreased to 21229 USD Million in May from 21636 USD Million in April of 2025. This dataset provides - United States Tourism Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

1990 to present (approximate 2 month lag) Virginia Labor Force and Unemployment estimates by Month by County.

Special data considerations: Period values of "M01-M12" represent Months of Year; "M13" is the Annual Average.

U.S. Bureau of Labor Statistics; Local Area Unemployment Statistics, table la.data.54.Virginia Data accessed from the Bureau of Labor Statistics public database LABSTAT (https://download.bls.gov/pub/time.series/la/)

Supporting documentation can be found on the U.S. Bureau of Labor Statistics website under Local Area Unemployment Statistics, Handbook of Methods (https://www.bls.gov/opub/hom/lau/home.htm)

Survey Description: Labor force and unemployment estimates for States and local areas are developed by State workforce agencies to measure local labor market conditions under a Federal-State cooperative program. The Department of Labor develops the concepts, definitions, and technical procedures which are used by State agencies for preparation of labor force and unemployment estimates.

These estimates are derived from a variety of sources, including the Current Population Survey, the Current Employment Statistics survey, the Quarterly Census of Employment and Wages, various programs at the Census Bureau, and unemployment insurance claims data from the State workforce agencies.

To establish uniform labor force concepts and definitions in all States and areas consistent with those used for the U.S. as a whole, monthly national estimates of employment and unemployment from the Current Population Survey are used as controls (benchmarks) for the State labor force statistics.

Summary Data Available: Monthly labor force and unemployment series are available for approximately 7,500 geographic areas, including cities over 25,000 population, counties, metropolitan areas, States, and other areas.

For each area, the following measures are presented by place of residence:

• Total civilian labor force,
• Total number of people employed,
• Total number of people unemployed, and
• Unemployment rate

Data Characteristics: Rates are expressed as percents with one decimal place. Levels are measured as individual persons (not thousands) and are stored with no decimal places.

The FDOT Annual Average Daily Traffic feature class provides spatial information on Annual Average Daily Traffic section breaks for the state of Florida. In addition, it provides affiliated traffic information like KFCTR, DFCTR and TFCTR among others. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 07/12/2025.Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/aadt.zip

This dataset provides data on how fast buses are traveling between pairs of subsequent timepoints (the major stops on a bus route) for every bus route in the system. It will provide the average speed in miles per hour between pairs of timepoints, the average travel time in minutes, the road distance in miles, and the number of bus trips for each bus route aggregated by month, day of the week, and hour of day. It also provides the type of bus trip (Local, Limited, SBS, Express, School), the borough of the bus route, the names of the timepoints, their coordinates, and the stop sequence order of the first two timepoints in a pair.

The outbreak of the COVID-19 pandemic has prompted the German government and the 16 German federal states to announce a variety of public health measures in order to suppress the spread of the coronavirus. These non-pharmaceutical measures intended to curb transmission rates by increasing social distancing (i.e., diminishing interpersonal contacts) which restricts a range of individual behaviors. These measures span moderate recommendations such as physical distancing, up to the closures of shops and bans of gatherings and demonstrations. The implementation of these measures are not only a research goal for themselves but have implications for behavioral research conducted in this time (e.g., in form of potential confounder biases). Hence, longitudinal data that represent the measures can be a fruitful data source. The presented data set contains data on 14 governmental measures across the 16 German federal states. In comparison to existing datasets, the data set at hand is a fine-grained daily time series tracking the effective calendar date, introduction, extension, or phase-out of each respective measure. Based on self-regulation theory, measures were coded whether they did not restrict, partially restricted or fully restricted the respective behavioral pattern. The time frame comprises March 08, 2020 until May 15, 2020. The project is an open-source, ongoing project with planned continued updates in regular (approximately monthly) intervals. New variables include restrictions on travel and gastronomy. The variable trvl (travel) comprises the following categories: fully restricted (=2) reflecting a potential general ban to travel within Germany (except for sound reasons like health or business); partially restricted (=1): travels are allowed but may be restricted through prohibition of accommodation or entry ban for certain groups (e.g. people from risk areas); free (=0): no travel and accommodation restrictions in place). The variable gastr (gastronomy) comprises: fully restricted (=2): closure of restaurants or bars; partially restricted (=1): Only take-away or food delivery services are allowed; free (=0): restaurants are allowed to open without restrictions). Further, the variables msk (recommendations to wear a mask) and zoo (restrictions of zoo visits) have been adjusted.:

This dataset is historical. For recent data, we recommend using https://chicagotraffictracker.com. -- Average Daily Traffic (ADT) counts are analogous to a census count of vehicles on city streets. These counts provide a close approximation to the actual number of vehicles passing through a given location on an average weekday. Since it is not possible to count every vehicle on every city street, sample counts are taken along larger streets to get an estimate of traffic on half-mile or one-mile street segments. ADT counts are used by city planners, transportation engineers, real-estate developers, marketers and many others for myriad planning and operational purposes. Data Owner: Transportation. Time Period: 2006. Frequency: A citywide count is taken approximately every 10 years. A limited number of traffic counts will be taken and added to the list periodically. Related Applications: Traffic Information Interactive Map (http://webapps.cityofchicago.org/traffic/).

Annual estimates of visits and spending by UK residents abroad. Also includes data on nights, purpose, main country visited and mode of travel. Breakdowns by length of stay and nationality are covered. In 2019, new methods were introduced for this dataset. The 2009 to 2019 edition supersedes all previous time series editions of this dataset. We advise against using all editions listed before the 2019 edition.

Data extracted from records of tickets on file with NYS DMV. The tickets were issued to motorists for violations of: NYS Vehicle & Traffic Law (VTL), Thruway Rules and Regulations, Tax Law, Transportation Law, Parks and Recreation Regulations, Local New York City Traffic Ordinances, and NYS Penal Law pertaining to the involvement of a motor vehicle in acts of assault, homicide, manslaughter and criminal negligence resulting in injury or death.

The number of new and used vehicles and the sales dollars respectively sold by month.

MDOT MVA’s Customer Connect modernization project, implemented in July 2020, has increased the amount of data that is collected and used to calculate car sales. This data is updated in real time and may fluctuate based on external factors, including electronic submissions from dealers and other vendors.

International travel by non-Canadians visitors coming to Canada for a trip, by Canadians returning from a visit abroad and by other non-tourism travellers (e.g. crew), by port of entry (e.g. airport, border crossing). This table includes breakdowns by mode of transportation (e.g. plane, automobile (car), boat) and by duration (same-day, overnight). Data come from Frontier Counts, part of the Tourism Statistics Program.

Number of international travellers entering or returning to Canada by province of entry and traveller category (seasonally adjusted). Data available monthly.

This dataset provides data showing the number of vehicles (including cars, buses, trucks and motorcycles) that pass through each of the bridges and tunnels operated by the MTA each hour of the day. The data is updated weekly.