Tourist Arrivals in the United States increased to 5957985 in April from 5410331 in March of 2025. This dataset provides - United States Tourist Arrivals- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

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.

United States US: International Tourism: Number of Departures data was reported at 73,453,000.000 Person in 2015. This records an increase from the previous number of 68,176,000.000 Person for 2014. United States US: International Tourism: Number of Departures data is updated yearly, averaging 61,061,000.000 Person from Dec 1995 (Median) to 2015, with 21 observations. The data reached an all-time high of 73,453,000.000 Person in 2015 and a record low of 51,285,000.000 Person in 1995. United States US: International Tourism: Number of Departures 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 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.; ; World Tourism Organization, Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files.; Gap-filled total;

This dataset contains data and analysis from the article Do State Department Travel Warnings Reflect Real Danger?

Key findings

• On the whole, there is a significant relationship between the number of American deaths abroad per capita and the number of travel warnings a country receives
• Mexico, Mali, and Israel have been targeted by the most travel warnings in recent years, but Americans are more likely to be killed in Thailand, Pakistan, and the Philippines
• Several countries with relatively high rates of American death have not been issued a single travel warning in ~7 years, including Belize, Guyana, and Guatemala
• Several countries with relatively low rates of American death have been issued a relatively high number of travel warnings in ~7 years, including Israel, Turkey, and Saudi Arabia
• Overall, countries subject to travel warnings do not see notable declines in American visitors in the 6 months after a warning is issued

Data sources

• BTSOriginUS_10_09_to_06_16.csv Air Carrier Statistics Database export, Bureau of Transportation Statistics
• SDamerican_deaths_abroad_10_09_to_06_16.csv U.S. State Department
• SDwarnings_10_09to06_16.csv U.S. State Department via Internet Archive

Charts / data visualizations

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Monthly estimates of overseas residents’ visits and spending and visits and spending abroad by UK or Great Britain residents. Also includes data on purpose of visit, area visited by UK residents and area of residence for overseas residents.

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;

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

Introduction

The dataset comprises over 12,000 chat conversations, each focusing on specific Travel related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.

Topic Diversity

The chat dataset covers a wide range of conversations on Travel topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various Travel use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.

Language Variety & Nuances

The conversations in this dataset capture the diverse language styles and expressions prevalent in English Travel interactions. This diversity ensures the dataset accurately represents the language used by English speakers in Travel contexts.

The dataset encompasses a wide array of language elements, including:

This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to English Travel interactions.

Conversational Flow and Interaction Types

The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of Travel customer-agent interactions.

Each of these conversations contains various aspects of conversation flow like:

Context

This dataset contains countries against years containing incoming international tourists to countries. This dataset i used to understand effect of cross-country travel on spread of COVID-19.

Content

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.

Acknowledgements

https://www.e-unwto.org/doi/suppl/10.5555/unwtotfb0156250119952018202001 https://data.worldbank.org/indicator/ST.INT.ARVL

Pakistan PK: International Tourism: Number of Arrivals data was reported at 966,000.000 Person in 2012. This records a decrease from the previous number of 1,161,000.000 Person for 2011. Pakistan PK: International Tourism: Number of Arrivals data is updated yearly, averaging 602,500.000 Person from Dec 1995 (Median) to 2012, with 18 observations. The data reached an all-time high of 1,161,000.000 Person in 2011 and a record low of 369,000.000 Person in 1996. Pakistan PK: 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 Pakistan – Table PK.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 table contains 45 series, with data for years 2014 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) State visited (15 items: Florida; New York; Washington; California; ...) Travel characteristics (3 items: Visits; Nights; Spending in country).

IE: International Tourism: Number of Departures data was reported at 7,646,000.000 Person in 2016. This records an increase from the previous number of 7,094,000.000 Person for 2015. IE: International Tourism: Number of Departures data is updated yearly, averaging 6,313,500.000 Person from Dec 1995 (Median) to 2016, with 22 observations. The data reached an all-time high of 7,877,000.000 Person in 2008 and a record low of 2,547,000.000 Person in 1995. IE: International Tourism: Number of Departures data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ireland – Table IE.World Bank.WDI: Tourism Statistics. 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.; ; World Tourism Organization, Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files.; Gap-filled total;

These surveys were conducted to collect data on travel origins and destinations, trip purposes, and travel characteristics of New York City Transit, Metro-North Railroad, and Long Island Rail Road customers with the aim of upgrading the MTA's travel forecasting tools and gaining a better understanding of how people travel. --LIRR origin-destination survey (2012-14) --Metro-North origin-destination survey (2007) --Metro-North origin-destination survey (2017) --MTA New York City travel survey (2008) --MTA New York City travel survey (2018)

Dial-a-Ride is a free door-to-door service for disabled and older people who can't use buses, trains or the Tube. Those eligible for membership have a permanent or long term disability which means they are unable or virtually unable to use mainstream public transport. This report details the usage for the specified quarterly, as well as the same quarter of the previous year, to allow for comparison.

There are a number of figures provided:

• The number of passengers registered to use the service
• The number of requests made for the service within the period
• The percentage of requests scheduled (accepted)
• The percentage of trips cancelled by passengers
• The percentage of trips cancelled owing to service (operational) reasons
• The number of completed trips

Find out more about the feeds available from Transport for London here

Tourist Arrivals in Turkey increased to 5037447 in May from 3900546 in April of 2025. This dataset provides the latest reported value for - Turkey Tourist Arrivals - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Tourist Arrivals in Vietnam decreased to 1528.35 Thousand in May from 1654.68 Thousand in April of 2025. This dataset provides - Vietnam Tourist Arrivals- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Analysis of ‘Travel Review Rating Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/wirachleelakiatiwong/travel-review-rating-dataset on 30 September 2021.

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

Context

This data set has been sourced from the Machine Learning Repository of University of California, Irvine (UC Irvine) : Travel Review Ratings Data Set. This data set is populated by capturing user ratings from Google reviews. Reviews on attractions from 24 categories across Europe are considered. Google user rating ranges from 1 to 5 and average user rating per category is calculated.

Content

Attribute 1 : Unique user id Attribute 2 : Average ratings on churches Attribute 3 : Average ratings on resorts Attribute 4 : Average ratings on beaches Attribute 5 : Average ratings on parks Attribute 6 : Average ratings on theatres Attribute 7 : Average ratings on museums Attribute 8 : Average ratings on malls Attribute 9 : Average ratings on zoo Attribute 10 : Average ratings on restaurants Attribute 11 : Average ratings on pubs/bars Attribute 12 : Average ratings on local services Attribute 13 : Average ratings on burger/pizza shops Attribute 14 : Average ratings on hotels/other lodgings Attribute 15 : Average ratings on juice bars Attribute 16 : Average ratings on art galleries Attribute 17 : Average ratings on dance clubs Attribute 18 : Average ratings on swimming pools Attribute 19 : Average ratings on gyms Attribute 20 : Average ratings on bakeries Attribute 21 : Average ratings on beauty & spas Attribute 22 : Average ratings on cafes Attribute 23 : Average ratings on view points Attribute 24 : Average ratings on monuments Attribute 25 : Average ratings on gardens

Acknowledgements

This data set has been sourced from the Machine Learning Repository of University of California, Irvine (UC Irvine) : Travel Review Ratings Data Set

The UCI page mentions the following publication as the original source of the data set: Renjith, Shini, A. Sreekumar, and M. Jathavedan. 2018. Evaluation of Partitioning Clustering Algorithms for Processing Social Media Data in Tourism Domain. In 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 12731. IEEE

Inspiration

I'm kind of people who love traveling. But sometimes I've problems like where should I visit? Are there somewhere interesting places matched with my lifestyle? Often I spent hours to search for interesting place to go out. Such a waste of time.

What if we can build a recommender system which can recommend you several interesting venue based on your preferences. With information from Google review, I'll try to divide Google review user into cluster of similar interest for further work of building recommender system based on thier preference.

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