Annual vehicle miles of travel by functional system for each of the 50 states, DC, and Puerto Rico from the Highway Statistics table VM-2. (Note: In 2009, the Urban functional class of Collectors became Major Collectors and Minor Collectors. Also in 2009, the system added the Rural functional class of Other Freeways and Expressways.)

1) Data Introduction • The Traveler Trip Dataset details a variety of travel-related information, including travel destinations, duration, demographics, accommodation and transportation and costs.

2) Data Utilization (1) Traveler Trip Dataset has characteristics that: • This dataset offers a variety of attributes covering the entire journey, including travel ID, destination, start/end date of travel, duration of travel, traveler's name, age, gender, nationality, accommodation type and cost, transportation, and cost. (2) Traveler Trip Dataset can be used to: • Analysis of travel patterns and preferences: It can be used to plan customized travel products by analyzing travelers' preferred destinations, types of accommodation, and means of transportation by age, gender, and nationality. • Pricing Strategy and Marketing: Accommodation and transportation cost data can be used by travel agencies or related industries to establish pricing policies and develop targeted marketing strategies.

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 ---

1) Data Introduction • The Expedia Travel Dataset is a large travel data that contains information about users' clicks and bookings on Expedia.

2) Data Utilization (1) Expedia Travel Dataset has characteristics that: • This dataset includes information such as destinations, hotels, and the number of visitors, as well as details about the type of reservation. (2) Expedia Travel Dataset can be used to: • Development of travel recommendation system and reservation prediction model: User search patterns and accommodation selection data can be used to develop travel platform recommendation systems and consumer behavior prediction models, such as custom hotel recommendations, click and reservation predictions. • Analysis of tourism trends and consumer behavior: By analyzing various variables and actual selection data such as travel timing, region, type of accommodation, price range, and filter use, it can be used for research on the tourism industry, such as global tourism trends, changing consumer preferences, and establishing marketing strategies.

The Seattle Household Travel Survey Wave 2, conducted in 1990, was the second wave in a 10-part longitudinal panel survey of the travel patterns of households in the Puget Sound region of Washington state. The survey series was initiated in 1989 by the Puget Sound Regional Council. Data collection for the second wave took place in the fall of 1990, which included full interviews and travel diaries, as well as some panel refreshment. Demographic and work trip data updates, but no travel diaries, were gathered for 2,023 households in four counties in the Seattle area. The survey relied on the willingness of study area residents to 1) provide demographic information about their household, its members, and its vehicles; 2) document all travel for each household member, aged 15 years or older, for an assigned two-day period; and 3) agree to participate in additional survey waves. After an initial telephone screening, survey participants received mailed travel diaries to aid in documenting travel information for the two-day assessment period. Respondents were instructed to record their mode of transportation, trip purpose, number of passengers, departure and arrival times, ride fare, and parking costs. Demographic information for this study includes age, gender, education, employment status, and household income.

Revenue in the Travel Agencies industry is expected to grow at a compound annual rate of 12.3% over the five years through 2025 to €121.5 billion. The focus of the travel industry in the last five years has been recovering from the COVID-19 pandemic. Travel demand plunged during 2020 and 2021, when COVID-19 outbreak grounded flights and confined people to their homes. While domestic travel could continue in some countries, most travel agencies had no trips to sell. Since restrictions were lifted across Europe and globally (which happened at each country’s own pace), the travel sector has seen a resurgence in demand by trends characterised as revenge travel and responsible travel. People made up for lost time by taking more trips after COVID-19 restrictions had been lifted. In 2024 and 2025, consumers are still keen for trips but want value-for-money adventures instead as they’re cautious of their spending amid disposable income squeezes. International travel to Europe has also resurged, especially from the US, thanks to the more favourable dollar-to-Europe rate – a welcome trend for agencies. There’s concerns that President Trump’s administration and US tariffs could see a drop in US visitors, but in early 2025 numbers have been strong. Pent-up demand combined with savings built up during COVID-19 has kept bookings high, defying high inflation across Europe that would usually signal lower trip spending. Travel remains a high priority for many households, driving up bookings. As a result, revenue is expected to mount by 4.4% in 2025. That being said, the Russia-Ukraine war has plagued tourism in Eastern Europe, with countries like Finland and the Baltic states continuing to record much lower tourist numbers than pre-pandemic because of fewer Russian tourists and lower travel confidence to the region. Revenue is anticipated to climb at a compound annual rate of 8.9% in the five years through 2030 to €186.3 billion. Online travel agencies will continue to cement their position in the industry, with most traditional agencies adapting by now or already closing. Climate change will disrupt travel agencies and the destination packages they offer. The last few years have already seen wildfires across Greece that spelt disaster for many trips and travel agencies will need to plan for the shift from southern European beaches to northern European destinations as temperatures rise. Travel agencies across Europe will also keep trying to carve out more of a niche by specialising in trips for certain age demographics.

Analysis of ‘Employee Travel Data (Non-Local)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4a84e72e-1a32-4627-9cf4-2eef62d55114 on 27 January 2022.

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

‘This dataset provides information regarding the total approved actual expenses incurred by Montgomery County government employees traveling non-locally (over 75 miles from the County’s Executive Office Building at 101 Monroe St. Rockville, MD) for official business, beginning on or after August 12, 2015. The dataset includes the name of traveling employee; the employee’s home department; travel start and end dates; destination; purpose of travel; and actual total expenses funded by the County. Update Frequency: Monthly

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

Anonymous travel time data is provided by a third-party vendor, INRIX. This data measures speed and travel time along Austin streets. Travel times along street segments are measured over the course of each year and are analyzed along Austin’s arterial roadways.

Analysis of ‘Travel in Guatemala with Hostelworld Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lexipetzold/hostels-in-guatemala-hostelworld-data on 28 January 2022.

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

Context

Guatemala is a beautiful country to visit and can accommodate a variety of travelers. From traveling on a budget to none at all. This datasets give an overview of the hostels available for booking through hostelworld.com. Travelers should note that this dataset is for budget backpackers!

Content

This data was acquired from a popular hostel booking site, hostelworld.com. Data is organized by hostel name including the hostel popular backpacking location, starting cost per night, breakfast and wifi included, the average rating out of 10 (10 being the best), and the number of ratings. Data acquired for only hostelworld.com listings categorized as "hostel."

Acknowledgements

Big thanks to hostelworld.com for giving travelers a different way to book their travel! Whether it for the social environment, cost effective, or experience itself. Hostelworld.com provides solid booking advice for hostel goers.

Inspiration

I was inspired to pull some Guatemalan hostel data because it is a place close to my heart! Plus backpacking had such a profound impact on my own life. I thought it would be cool to play out scenarios of a backpacker's travel itinerary options. Have you backpacked before?

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

Comprehensive dataset of 2 Travel in Rhode Island, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This statistic shows the events that could affect travel agency revenue in the next two years in the United States as of August 2017. During the survey, 34 percent of respondents said that internet competition could affect travel agency revenue over the next two years.

Revenue in the Travel Agencies industry is expected to grow at a compound annual rate of 12.3% over the five years through 2025 to €121.5 billion. The focus of the travel industry in the last five years has been recovering from the COVID-19 pandemic. Travel demand plunged during 2020 and 2021, when COVID-19 outbreak grounded flights and confined people to their homes. While domestic travel could continue in some countries, most travel agencies had no trips to sell. Since restrictions were lifted across Europe and globally (which happened at each country’s own pace), the travel sector has seen a resurgence in demand by trends characterised as revenge travel and responsible travel. People made up for lost time by taking more trips after COVID-19 restrictions had been lifted. In 2024 and 2025, consumers are still keen for trips but want value-for-money adventures instead as they’re cautious of their spending amid disposable income squeezes. International travel to Europe has also resurged, especially from the US, thanks to the more favourable dollar-to-Europe rate – a welcome trend for agencies. There’s concerns that President Trump’s administration and US tariffs could see a drop in US visitors, but in early 2025 numbers have been strong. Pent-up demand combined with savings built up during COVID-19 has kept bookings high, defying high inflation across Europe that would usually signal lower trip spending. Travel remains a high priority for many households, driving up bookings. As a result, revenue is expected to mount by 4.4% in 2025. That being said, the Russia-Ukraine war has plagued tourism in Eastern Europe, with countries like Finland and the Baltic states continuing to record much lower tourist numbers than pre-pandemic because of fewer Russian tourists and lower travel confidence to the region. Revenue is anticipated to climb at a compound annual rate of 8.9% in the five years through 2030 to €186.3 billion. Online travel agencies will continue to cement their position in the industry, with most traditional agencies adapting by now or already closing. Climate change will disrupt travel agencies and the destination packages they offer. The last few years have already seen wildfires across Greece that spelt disaster for many trips and travel agencies will need to plan for the shift from southern European beaches to northern European destinations as temperatures rise. Travel agencies across Europe will also keep trying to carve out more of a niche by specialising in trips for certain age demographics.

The major objective of SATS is to determine the transportation needs of the Sydney region through to the year 2000, propose a series of alternatives to meet those needs, evaluate the costs and benefits of each and recommend transportation systems which will effectively serve the Sydney region. The recommended systems must be operational and effective, financially feasible and environmentally sensitive and compatible with the ultimate development of the region. This volume relates to the development of urban travel models and forecasts of future land use and travel patterns.

The Travel Time Tool was created by the MN DNR to use GIS analysis for calculation of hydraulic travel time from gridded surfaces and develop a downstream travel time raster for each cell in a watershed. This hydraulic travel time process, known as Time of Concentration, is a concept from the science of hydrology that measures watershed response to a precipitation event. The analysis uses watershed characteristics such as land-use, geology, channel shape, surface roughness, and topography to measure time of travel for water. Described as Travel Time, it calculates the elapsed time for a simulated drop of water to migrate from its source along a hydraulic path across different surfaces of the replicated watershed landscape, ultimately reaching the watershed outlet. The Travel Time Tool creates a raster whereas each cell is a measure of the length of time (in seconds) that it takes water to flow across it, and then accumulates the time (in hours) from the cell to the outlet of the watershed.

The Travel Time Tool creates an impedance raster from Manning's Equation that determines the velocity of water flowing across the cell as a measure of time (in feet per second). The Flow Length Tool uses the travel time Grid for the impedance factor and determines the downstream flow time from each cell to the outlet of the watershed.

The toolbox works with ArcMap 10.6.1 and newer and ArcGIS Pro.

For step-by-step instructions on how to use the tool, please view MN DNR Travel Time Guidance.pdf

Revenue in the Travel Agencies industry is expected to grow at a compound annual rate of 12.3% over the five years through 2025 to €121.5 billion. The focus of the travel industry in the last five years has been recovering from the COVID-19 pandemic. Travel demand plunged during 2020 and 2021, when COVID-19 outbreak grounded flights and confined people to their homes. While domestic travel could continue in some countries, most travel agencies had no trips to sell. Since restrictions were lifted across Europe and globally (which happened at each country’s own pace), the travel sector has seen a resurgence in demand by trends characterised as revenge travel and responsible travel. People made up for lost time by taking more trips after COVID-19 restrictions had been lifted. In 2024 and 2025, consumers are still keen for trips but want value-for-money adventures instead as they’re cautious of their spending amid disposable income squeezes. International travel to Europe has also resurged, especially from the US, thanks to the more favourable dollar-to-Europe rate – a welcome trend for agencies. There’s concerns that President Trump’s administration and US tariffs could see a drop in US visitors, but in early 2025 numbers have been strong. Pent-up demand combined with savings built up during COVID-19 has kept bookings high, defying high inflation across Europe that would usually signal lower trip spending. Travel remains a high priority for many households, driving up bookings. As a result, revenue is expected to mount by 4.4% in 2025. That being said, the Russia-Ukraine war has plagued tourism in Eastern Europe, with countries like Finland and the Baltic states continuing to record much lower tourist numbers than pre-pandemic because of fewer Russian tourists and lower travel confidence to the region. Revenue is anticipated to climb at a compound annual rate of 8.9% in the five years through 2030 to €186.3 billion. Online travel agencies will continue to cement their position in the industry, with most traditional agencies adapting by now or already closing. Climate change will disrupt travel agencies and the destination packages they offer. The last few years have already seen wildfires across Greece that spelt disaster for many trips and travel agencies will need to plan for the shift from southern European beaches to northern European destinations as temperatures rise. Travel agencies across Europe will also keep trying to carve out more of a niche by specialising in trips for certain age demographics.

Final TMR Data. Alternative 2 Modified Proposed Action. Purpose of Data:For use in Travel Management Implementation Story Map.Life Cycle: Until TMR has been signed and implemented.The motorized as shown in table 1 of the Record of Decision, and described in the EIS on pages 12 to 18, designates one 17 acre area designated for motorized use on the Black Mesa Ranger District.

All off-highway vehicles are required to remain on these designated routes and area, and no other cross-country travel is allowed unless authorized by a permit.

Purpose and Need for Action

The purpose of this project is to comply with the TMR by providing a system of roads, trails, and areas designated for motor vehicle use (36 CFR 212) and for that system to reduce impacts to biological, physical, and cultural resources in the Apache-Sitgreaves National Forests. At 36 CFR 261.13, national forests are required to prohibit motor vehicle use off the system of designated roads, trails, and areas, and motor vehicle use that is not in accordance with the designations.

There is a need for a transportation system for public use, Forest Service administration, and resource protection, while recognizing historic and current uses of the Apache-Sitgreaves. Specifically, there is a need for:

identifying the system of roads that would be open to motor vehicle use;

identifying the system of motorized trails for vehicles 50 inches or less in width; and

designating the limited use of motor vehicles within a specified distance of certain designated routes solely for the purposes of dispersed camping or retrieval of big game by an individual who has legally killed the animal.

There is a need to counter adverse effects to resources from continued use of some roads and motorized trails, as well as cross-country travel. Some detrimental effects from motorized use of the Apache-Sitgreaves include increased sediment deposits in streams, which degrade water quality and fish habitat; the spread of invasive plants; disturbances to a variety of plant and wildlife species; and the continued risk of damaging cultural resource sites.

Travel agents are responsible for helping travelers select and organize a trip according to a specific budget. They can be found in both brick-and-mortar establishments and online. The travel agency industry's global revenue was estimated at over *** billion U.S. dollars as of March 2025. As of that month, roughly ******* businesses operated in this market, with employment reaching approximately ***** million.

The domestic and international travel expenditure in the United States reached approximately ************ U.S. dollars in 2022, with domestic travelers contributing the majority of the spending. This figure is expected to reach as much as *** trillion U.S. dollars by 2027.

The 1996 Bay Area Travel Study, conducted by the Metropolitan Transportation Commssion, collected demographic, socioeconomic, and travel data for 3,618 households in California's nine-county Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, and Sonoma counties. The goal of the study was to collect information on activities for all people in each household, regardless of age or relationship. Respondents were asked to record all activities, including trips, over a two-day period. The data gathered during the survey will be used for the area's long-term transportation and air quality planning needs. The survey collected both weekday and weekend multi-day data and was the first activity-based survey conducted in the Bay Area. It also included separate sub-projects. A stated preference congestion pricing survey was administered to 150 of the respondents with a follow-up survey conducted with 110 participants. A follow-up survey was also completed by over half of the 3,618 respondent households to update contact and demographic information. These participants were then used as a panel sample for the 2000 Bay Area Travel Survey (NuStats Research and Consulting 1999).

The 2018 Census commuter view dataset contains the employed census usually resident population count aged 15 years and over by statistical area 2 for the main means of travel to work variable from the 2018 Census. The geography corresponds to 2018 boundaries.

This dataset is the base data for the ‘There and back again: our daily commute’ competition.

This 2018 Census commuter view dataset is displayed by statistical area 2 geography and contains from-to (journey) information on an individual's usual residence and workplace address* by main means of travel to work.

* Workplace address is coded from information supplied by respondents about their workplaces. Where respondents do not supply sufficient information, their responses are coded to ‘not further defined’. The 2018 Census commuter view datasets excludes these ‘not further defined’ areas, as such the sum of the counts for each region in this dataset may not be equal to the total employed census usually resident population count aged 15 years and over for that region.

It is recommended that this dataset be downloaded as either a CSV or a file geodatabase.

This dataset can be used in conjunction with the following spatial files by joining on the statistical area 2 code values:

· Statistical Area 2 2018 (generalised)

· Statistical Area 2 2018 (Centroid Inside)

The data uses fixed random rounding to protect confidentiality. Counts of less than 6 are suppressed according to 2018 confidentiality rules. Values of -999 indicate suppressed data.

Data quality ratings for 2018 Census variables, summarising the quality rating and priority levels for 2018 Census variables, are available.

For information on the statistical area 2 geography please refer to the Statistical standard for geographic areas 2018.

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