Updates are delayed due to technical difficulties. How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics. The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day. Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air. The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed. These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

United States US: International Tourism: Number of Arrivals data was reported at 75,608,000.000 Person in 2016. This records a decrease from the previous number of 77,465,000.000 Person for 2015. United States US: International Tourism: Number of Arrivals data is updated yearly, averaging 51,107,500.000 Person from Dec 1995 (Median) to 2016, with 22 observations. The data reached an all-time high of 77,465,000.000 Person in 2015 and a record low of 41,218,000.000 Person in 2003. United States US: International Tourism: Number of Arrivals data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Tourism Statistics. International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead. Sources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival.; ; World Tourism Organization, Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files.; Gap-filled total;

Tourist Arrivals in the United States increased to 5410331 in March from 4636160 in February of 2025. This dataset provides - United States Tourist Arrivals- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset is sourced from the U.S. Department of Transportation Bureau of Transportation Statistics. All data and metadata is sourced from the page linked below. Metadata is not updated automatically; data updates weekly.

Source Data Link: https://data.bts.gov/Research-and-Statistics/Trips-by-Distance/w96p-f2qv

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics.

The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our mobility statistics program.The "Trips by Distance" data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air.Text from webpage.

This table contains information regarding the mobility of the residents of the Netherlands aged 6 or older in private households, so excluding residents of institutions and homes. The table contains per person per day /year an overview of the average number of trips, the average distance travelled and the average time travelled. These are regular trips on Dutch territory, including domestic holiday mobility. The distance travelled is based on stage information. Excluded in this table is mobility based on series of calls trips. The mobility behaviour is broken down by modes of travel, purposes of travel, population and region characteristics. The data used are retrieved from The Dutch National travel survey named Onderweg in Nederland (ODiN). Data available from: 2018

Status of the figures: The figures in this table are final.

Changes as of 4 July 2024: The figures for year 2023 are added.

When will new figures be published? Figures for the 2024 research year will be published in mid-2025

Tourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists). An increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities.

Data Source - WorldBank

Data Description -

International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they usually reside, and outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on the number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead.

International outbound tourists are the number of departures that people make from their country of usual residence to any other country for any purpose other than a remunerated activity in the country visited. The data on outbound tourists refer to the number of departures, not to the number of people traveling. Thus a person who makes several trips from a country during a given period is counted each time as a new departure.

International tourism expenditures are expenditures of international outbound visitors in other countries, including payments to foreign carriers for international transport. These expenditures may include those by residents traveling abroad as same-day visitors, except in cases where these are important enough to justify separate classification. For some countries, they do not include expenditures for passenger transport items. Data are in current U.S. dollars.

France FR: International Tourism: Number of Arrivals data was reported at 82,570,000.000 Person in 2016. This records a decrease from the previous number of 84,452,000.000 Person for 2015. France FR: International Tourism: Number of Arrivals data is updated yearly, averaging 76,888,000.000 Person from Dec 1995 (Median) to 2016, with 22 observations. The data reached an all-time high of 84,452,000.000 Person in 2015 and a record low of 60,033,000.000 Person in 1995. France FR: International Tourism: Number of Arrivals data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank.WDI: Tourism Statistics. International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead. Sources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival.; ; World Tourism Organization, Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files.; Gap-filled total;

Mexico MX: International Tourism: Number of Arrivals data was reported at 39,291,000.000 Person in 2017. This records an increase from the previous number of 35,079,000.000 Person for 2016. Mexico MX: International Tourism: Number of Arrivals data is updated yearly, averaging 21,606,000.000 Person from Dec 1995 (Median) to 2017, with 23 observations. The data reached an all-time high of 39,291,000.000 Person in 2017 and a record low of 18,665,000.000 Person in 2003. Mexico MX: International Tourism: Number of Arrivals data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Tourism Statistics. International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead. Sources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival.; ; World Tourism Organization, Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files.; Gap-filled total;

**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.

International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead. Sources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival.

This spreadsheet replicates selected data tables from the ACT & Queanbeyan Household Travel Survey dashboard. Please refer to the attached spreadsheet on this page.

About the Time of Travel theme Time of travel shows the number of people moving around the transport network at specific (half-hourly) time points across the day.

An area analysis is not possible for this theme as the sample size is limited for each time point (particularly outside of the peak periods). Furthermore, the location of the person at each time point is not known - only that they were travelling. A spatial cut based on 'home region' could therefore misrepresent the location of the travel peaks. Note that the tables provided represent a small subset of data available. Use of the dashboard or raw survey datasets allow more complex descriptions of travel to be developed.

Source data The data shown is not a Census of travel, but a large survey of several thousand households from across the ACT and Queanbeyan. As with any survey there will be some variability in the accuracy of the results, and how well they reflect the movement of the entire population. For instance, if the survey were to be completed on another day, or with a different subset of households, the results would be slightly different. Interpretations of the data should keep this variability in mind: these are estimates of the broad shape of travel only. Even for the same person, travel behaviour will vary according to many factors: day of week, month of year, season, weather, school holidays, illness, family responsibilities, work from home opportunities, etc. Again, by summarising the travel of many different people, the data provides a view of average weekday patterns.

In interpreting the data, it is worth noting the following points: - A zero cell does not necessarily mean the travel is never made, but rather that the survey participants did not make this travel on their particular survey day. - Values are rounded, and may not sum to the totals shown. Trip time periods are assigned using the mid point of travel: - AM peak (8am to 9am), PM peak (5pm to 6pm), Interpeak (9am to 5pm), Off-peak (after 6pm)

The survey is described on the Transport Canberra and City Services' website: [Household Travel Survey homepage]

Cell annotations and notes Some cells have annotations added to them, as follows: * : Statistically significant difference across survey years (at the 95% confidence level). Confidence intervals indicate where the true measure would typically fall if the survey were repeated multiple times (i.e., 95 times out of 100), recognising that each survey iteration may produce slightly different outcomes. ~ : Unreliable estimate (small sample or wide confidence interval)

Additional information Analysis by Sift Research, March 2025. Contact research@sift.group for further information. Enclosed data tables shared under a 'CC BY' Creative Commons licence. This enables users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. [>More information about CC BY]

SY: International Tourism: Number of Arrivals data was reported at 5,070,000.000 Person in 2011. This records a decrease from the previous number of 8,546,000.000 Person for 2010. SY: International Tourism: Number of Arrivals data is updated yearly, averaging 2,661,000.000 Person from Dec 1995 (Median) to 2011, with 17 observations. The data reached an all-time high of 8,546,000.000 Person in 2010 and a record low of 815,000.000 Person in 1995. SY: International Tourism: Number of Arrivals data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Syrian Arab Republic – Table SY.World Bank.WDI: Tourism Statistics. International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead. Sources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival.; ; World Tourism Organization, Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files.; Gap-filled total;

This spreadsheet replicates selected data tables from the ACT & Queanbeyan Household Travel Survey dashboard. Please refer to the attached spreadsheet on this page.

About the Local Travel theme The local travel theme examines just the trips which are made in a survey participant's local area. This measure can reveal whether local activity centres are appropriate, desirable and accessible, or whether people are having to travel to other regions to meet their travel needs.

While it adds a different perspective to the overall travel picture, it is best used as a simple descriptive measure only. For example, a low proportion of shopping trips in an area may indeed be linked to inadequate retail activities. Alternatively, it may be a secondary outcome of other behaviour (e.g., people shopping near their main workplace).

Notes: - travel 'back home' is excluded in this estimate of local trips. Travel back home is, by definition, local. Instead, the focus is on activities made across the day as these are the trips where locations or choices could potentially change.

• the tables provided represent a small subset of data available. Only the number and proportion of trips are shown, by time period and household region. Use of the dashboard or raw survey datasets allow more complex descriptions of travel to be developed.

Source data The data shown is not a Census of travel, but a large survey of several thousand households from across the ACT and Queanbeyan. As with any survey there will be some variability in the accuracy of the results, and how well they reflect the movement of the entire population. For instance, if the survey were to be completed on another day, or with a different subset of households, the results would be slightly different. Interpretations of the data should keep this variability in mind: these are estimates of the broad shape of travel only. Even for the same person, travel behaviour will vary according to many factors: day of week, month of year, season, weather, school holidays, illness, family responsibilities, work from home opportunities, etc. Again, by summarising the travel of many different people, the data provides a view of average weekday patterns.

In interpreting the data, it is worth noting the following points: - A zero cell does not necessarily mean the travel is never made, but rather that the survey participants did not make this travel on their particular survey day. - Values are rounded, and may not sum to the totals shown.

The survey is described on the Transport Canberra and City Services' website: [Household Travel Survey homepage]

Cell annotations and notes Some cells have annotations added to them, as follows: * : Statistically significant difference across survey years (at the 95% confidence level). Confidence intervals indicate where the true measure would typically fall if the survey were repeated multiple times (i.e., 95 times out of 100), recognising that each survey iteration may produce slightly different outcomes. ~ : Unreliable estimate (small sample or wide confidence interval)

Additional information Analysis by Sift Research, March 2025. Contact research@sift.group for further information. Enclosed data tables shared under a 'CC BY' Creative Commons licence. This enables users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. [>More information about CC BY]

Analysis of ‘Vehicle Miles Traveled During Covid-19 Lock-Downs ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/vehicle-miles-travelede on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

**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:

This dataset was created by Angeliki Kastanis and contains around 0 samples along with Date At Low, Mean7 County Vmt At Low, technical information and other features such as: - County Name - County Fips - and more.

How to use this dataset

• Analyze State Name in relation to Baseline Jan Vmt
• Study the influence of Date At Low on Mean7 County Vmt At Low
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Angeliki Kastanis

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Number of arrivals of Senegal jumped by 12.88% from 1,219,000 number in 2016 to 1,376,000 number in 2017. Since the 8.70% drop in 2014, number of arrivals shot up by 40.98% in 2017. International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead. Sources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival.

DRAKO is a Mobile Location Data provider with a programmatic trading desk specializing in geolocation analytics and programmatic advertising. Our Tourism Data has helped cities, counties, and states better understand who their visitors are so that they can effectively develop and deliver advertising campaigns. We’re in a unique position to deliver enriched insight beyond traditional surveying or other data sources because of our rich dataset, proprietary modelling capabilities, and analytical capabilities.

MAIDs (Mobile Advertising IDs) are unique device identifiers associated with consenting mobile devices that can be utilized for geolocation based analyses and audiences. Drako uses MAIDs to fuel our Tourism Data utilizing our Home Location Model. The Home Location of a MAID is determined based on where that MAID is seen most frequently between the hours of 11pm and 6am (local time). Using this we are able to determine the Home Location of a user which in turn allows us to identify when and where they are travelling.

Beyond identifying that users are tourists, we can also classify them into different bins by their frequency / dwell time over their estimated number of visits. Using our data and frequency, we can identify: overnight visitors, weekend visits, short-term stays, long-term stays, or frequent holiday visitors !

Beyond Tourism Data in your defined geography alone, we are also able to provide: - Home location - Find out where your audience is coming from using our home location technology - Movement - Quantify how far users have travelled between locations. - Demographics - Discover neighborhood level characteristics such as income, ethnicity, and more - Brand index - Learn which major brands and retailers your audience is visiting the most. - Visitation index - See which destinations your visitors are visiting the most - Addressable audience - Customize your audiences for your campaigns using our analytic insights

Moreover, if you’re looking to activate your Tourism Data for advertising, we’re always able to further refine or filter your desired audience with our other Audience Data, such as: Brand visits, Geodemographics, Ticketed Event visits, Purchase Intent (in Canada), Purchase History (in USA), and more !

Data Compliance: All of our Tourism Data is fully CCPA compliant and 100% sourced from SDKs (Software Development Kits), the most reliable and consistent mobile data stream with end user consent available with only a 4-5 day delay. This means that our location and device ID data comes from partnerships with over 1,500+ mobile apps. This data comes with an associated location which is how we are able to segment using geofences.

Data Quality: In addition to partnering with trusted SDKs, DRAKO has additional screening methods to ensure that our mobile location data is consistent and reliable. This includes data harmonization and quality scoring from all of our partners in order to disregard MAIDs with a low quality score.

This spreadsheet replicates selected data tables from the ACT & Queanbeyan Household Travel Survey data dashboard. Please refer to the attached spreadsheet on this page.

About the Travel Distances theme 'Distance of travel' shows the overall trip length distribution. This provides a quick reference of the ranges that trips tend to fall in.

For example, most travel in the ACT is of a relatively short nature. Across all modes, 16% of trips are under 1km in length and just over half (51%) are under 5km in length. An area analysis is not possible for this theme as the sample size is limited for each distance range, particularly once the mode filters are applied. Note that the tables provided represent a small subset of data available. Only the number and proportion of trips are shown. Use of the dashboard or raw survey datasets allow more complex descriptions of travel to be developed.

Source data The data shown is not a Census of travel, but a large survey of several thousand households from across the ACT and Queanbeyan. As with any survey there will be some variability in the accuracy of the results, and how well they reflect the movement of the entire population. For instance, if the survey were to be completed on another day, or with a different subset of households, the results would be slightly different. Interpretations of the data should keep this variability in mind: these are estimates of the broad shape of travel only. Even for the same person, travel behaviour will vary according to many factors: day of week, month of year, season, weather, school holidays, illness, family responsibilities, work from home opportunities, etc. Again, by summarising the travel of many different people, the data provides a view of average weekday patterns.

In interpreting the data, it is worth noting the following points: - A zero cell does not necessarily mean the travel is never made, but rather that the survey participants did not make this travel on their particular survey day. - Values are rounded, and may not sum to the totals shown. Trip time periods are assigned using the mid point of travel: - AM peak (8am to 9am), PM peak (5pm to 6pm), Interpeak (9am to 5pm), Off-peak (after 6pm)

The survey is described on the Transport Canberra and City Services' website: [Household Travel Survey homepage]

Cell annotations and notes Some cells have annotations added to them, as follows: ~ : Unreliable estimate (small sample or wide confidence interval)

Additional information Analysis by Sift Research, March 2025. Contact research@sift.group for further information. Enclosed data tables shared under a 'CC BY' Creative Commons licence. This enables users to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. [>More information about CC BY]