Dataset's Source:

https://www.unwto.org/tourism-statistics/tourism-statistics-database

The most complete collection of statistical data on the tourist industry is provided by UN tourist, which methodically compiles tourism statistics from nations and territories worldwide.

Through a series of annual questionnaires, UN Tourism gathers data from nations in accordance with the United Nations-approved International Recommendations for Tourism Statistics (IRTS 2008) standard.

The provided UN Tourism dataset comprises multiple files, each focusing on a specific aspect of tourism data. Below is a detailed description of the columns found in each of these datasets. Please note that the "INDEX" column appears to be a sequential identifier, and years (e.g., 1995-2022) represent annual data for various indicators across the datasets.

Domestic Tourism - Trips

This dataset contains information related to domestic tourism trips.

C., S., C. & S.: These columns likely represent categorization or classification codes for the data entries. 'C.' could stand for Country Code, 'S.' for Series, and 'C. & S.' for a combined Country and Series identifier.

Basic data and indicators: This column describes the specific tourism indicator being measured (e.g., 'Total trips', 'Overnights visitors (tourists)', 'Same-day visitors (excursionists)').

Units: The unit of measurement for the data (e.g., 'Thousands').

Notes: Any specific notes or disclaimers related to the data for that row.

1995 - 2022: These columns represent the recorded values for the respective tourism indicators for each year.

Domestic Tourism - Accommodation

This dataset provides statistics on accommodation used for domestic tourism.

C., S., C. & S.: Similar to the "Trips" sheet, these are likely categorization or classification codes.

Basic data and indicators: This column specifies the type of accommodation data (e.g., 'Guests', 'Overnights' in total, or specifically for 'Hotels and similar establishments').

Units: The unit of measurement for the data (e.g., 'Thousands').

Notes: Any specific notes or disclaimers related to the data for that row.

1995 - 2022: These columns represent the recorded values for the accommodation indicators for each year.

Inbound Tourism - Arrivals

This dataset details the number of international tourist arrivals.

C., S., C. & S.: Categorization or classification codes.

Basic data and indicators: This column describes the type of arrival data (e.g., 'Total arrivals', 'Overnights visitors (tourists)', 'Same-day visitors (excursionists)', and 'of which, cruise passengers').

Units: The unit of measurement for the data (e.g., 'Thousands').

Notes: Any specific notes or disclaimers related to the data for that row.

Series: This column likely indicates the type of statistical series or methodology used for data collection (e.g., 'VF' for Visitor Flow, 'TF' for Tourist Flow).

1995 - 2022: These columns represent the recorded values for the arrival indicators for each year.

Inbound Tourism - Expenditure

This dataset focuses on the expenditure by inbound tourists within the country.

C., S., C. & S.: Categorization or classification codes.

Basic data and indicators: This column specifies the type of expenditure data (e.g., 'Tourism expenditure in the country', 'Travel', 'Passenger transport').

Units: The unit of measurement for the data (e.g., 'US$ Millions').

Notes: Any specific notes or disclaimers related to the data for that row.

Series: This column indicates the data source or methodology (e.g., 'IMF' for International Monetary Fund).

1995 - 2022: These columns represent the recorded values for the expenditure indicators for each year.

Inbound Tourism - Regions

This dataset breaks down inbound tourism arrivals by the region of origin.

C., S., C. & S.: Categorization or classification codes.

Basic data and indicators: This column describes the regional breakdown of arrivals (e.g., 'Total', 'Africa', 'Americas', 'East Asia and the Pacific', 'Europe', 'Middle East', 'South Asia', 'Other not classified').

Units: The unit of measurement for the data (e.g., 'Thousands').

Notes: Any specific notes or disclaimers related to the data for that row.

Series: This column likely indicates the type of statistical series or methodology used for data collection.

1995 - 2022: These columns represent the recorded values for arrivals from each region for each year.

Inbound Tourism - Purpose

This dataset categorizes inbound tourism arrivals by their main purpose of visit.

C., S., C. & S.: Categorization or classification codes.

Basic data and indicators: This column specifies the purpose of visit (e.g., 'Total', 'Personal', 'Business and professional'). 'Personal' can be further broken down into sub-categories such as 'Holiday, leisure and recreation', 'Visiting fr...

Dataset Overview 📝

This dataset includes key tourism and economic indicators for over 200 countries, spanning the years from 1999 to 2023. It covers a wide range of data related to tourism arrivals, expenditures, receipts, GDP, unemployment, and inflation, helping to explore the relationship between tourism and economic growth globally.

Tourism & Economic Indicators:

• tourism_receipts 💰: The total income a country generates from international tourism, measured in current US dollars.
• tourism_arrivals 🌍: The total number of international tourists who arrive in a country, measured in count.
• tourism_expenditures 🛍️: The amount of money spent by international tourists within the country, measured in current US dollars.
• tourism_exports 📈: The percentage of a country’s total exports derived from international tourism receipts.
• tourism_departures ✈️: The number of citizens or residents of a country who travel abroad for tourism.
• tourism_expenditures 🛫: The percentage of a country’s total imports spent on international tourism.
• gdp 📊: The total value of all goods and services produced in a country, expressed in current US dollars.
• inflation 📉: The annual percentage change in the average price of goods and services in a country.
• unemployment 👷‍♂️: The percentage of people within the labor force who are unemployed but actively seeking work.

Data Source 🌐

• World Bank: The dataset is sourced from the World Bank’s economic and tourism databases, offering reliable and up-to-date statistics on global tourism and economic indicators.

Potential Use Cases 🔍

• Tourism & Economic Impact Analysis 🌏: Analyzing the contribution of tourism to a country’s GDP, as well as how tourism spending impacts national economies.
• Cross-Country Comparison 📊: Comparing tourism arrivals, expenditures, and receipts relative to GDP and unemployment across countries and regions.
• Predictive Modeling 🤖: Building models to predict the future impact of tourism on economic growth and identify emerging trends.
• Policy Evaluation 🏛️: Helping policymakers assess the role of tourism in economic planning, especially regarding inflation and unemployment.
• Economic Forecasting 📈: Using historical data to forecast how tourism will influence economic conditions, helping in the development of economic policies.

Key Questions You Can Explore 🤔

• How do tourism receipts correlate with GDP growth in different countries? 💵
• What is the relationship between tourism expenditure and inflation rates? 📉
• How do tourism arrivals and departures vary by region and what are the key drivers? 🌍
• What role does tourism play in shaping unemployment rates across countries? 👥
• Which countries have seen the largest increase in tourism receipts over the past two decades? 📊

Important Notes ⚠️

• Missing Data 🚨: Some values may be missing for certain years or countries, especially for specific tourism indicators. Techniques like forward filling, backward filling, or interpolation may help in handling missing values during time series analysis.
• Currency & Inflation Adjustments 💲: When comparing tourism receipts and expenditures across countries, consider adjusting for inflation and exchange rates to ensure accurate year-on-year comparisons.

Monthly U.S. citizen departures are collected and reported in Tourism Industries U.S. International Air Travel Statistics (I-92 data) Program.

Success.ai’s Tourism Data for the Global Hospitality Sector offers a robust and reliable dataset tailored for businesses aiming to connect with professionals and organizations in the global tourism and hospitality industry. Covering roles such as hotel managers, travel consultants, tour operators, and decision-makers, this dataset provides verified profiles, business insights, and actionable data.

With access to over 700 million verified profiles globally, Success.ai ensures your marketing, outreach, and strategic initiatives are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is ideal for thriving in the competitive and dynamic global hospitality market.

Why Choose Success.ai’s Tourism Data?

1. Verified Profiles for Precision Outreach

   • Access verified business locations, employee counts, work emails, and decision-maker profiles in the global tourism and hospitality sector.
   • AI-driven validation ensures 99% accuracy, enhancing engagement and reducing inefficiencies in communication.

2. Comprehensive Global Coverage

   • Includes hospitality professionals and businesses from top tourism hubs such as North America, Europe, Asia-Pacific, the Middle East, and Africa.
   • Gain insights into regional hospitality trends, customer preferences, and emerging tourism markets.

3. Continuously Updated Datasets

   • Real-time updates ensure the accuracy of professional profiles, business expansions, and organizational changes.
   • Stay ahead of industry trends to capitalize on new opportunities in the hospitality sector.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Access tourism and hospitality data for professionals and organizations worldwide.
• Verified Contact Details: Gain work emails, phone numbers, and decision-maker profiles for targeted engagement.
• Business Insights: Understand employee counts, geographic locations, and operational details of hospitality businesses.
• Leadership Profiles: Connect with hotel executives, travel agency leaders, and tour company managers driving the global tourism sector.

Key Features of the Dataset:

1. Comprehensive Hospitality Profiles

   • Identify and connect with key players in hotels, resorts, travel agencies, and tour operators worldwide.
   • Target professionals managing operations, customer experience, and business strategy in the tourism industry.

2. Advanced Filters for Precision Campaigns

   • Filter businesses and professionals by location, industry focus (hotels, travel agencies, tour operators), or job roles.
   • Tailor campaigns to align with specific needs, such as marketing strategies, software adoption, or customer engagement.

3. Regional and Industry-specific Insights

   • Leverage data on global tourism trends, customer preferences, and market growth in hospitality.
   • Refine strategies to align with specific geographic or sector-specific opportunities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Outreach

   • Promote travel solutions, hospitality software, or customer engagement tools to professionals in the tourism sector.
   • Use verified contact data for multi-channel outreach, including email, phone, and digital campaigns.

2. Partnership Development and Collaboration

   • Build relationships with travel agencies, hotel chains, and tour operators exploring strategic alliances.
   • Foster collaborations that enhance customer experiences, expand market reach, or improve operational efficiencies.

3. Market Research and Competitive Analysis

   • Analyze global tourism trends, hospitality demands, and emerging markets to refine strategies.
   • Benchmark against competitors to identify growth opportunities and high-demand solutions.

4. Recruitment and Workforce Optimization

   • Target HR professionals and hiring managers recruiting for hospitality roles, from hotel managers to travel consultants.
   • Provide workforce management platforms or training solutions tailored to the hospitality industry.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality tourism and hospitality data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.

2. Seamless Integration

   • Integrate verified data into CRM systems, analytics tools, or marketing platforms via APIs or downloadable formats, enhancing productivity and simplifying workflows.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to guide data-driven decisions, refine targeting, and imp...

Justification:

Columns:

• Location: A ten- -character random string that represents the location's name.
• Nation: Selectively selected from a roster of nations.
• Type: Selectively selected from a range of travel-related categories.
• Visitor count: A random integer value that indicates how many people have visited.
• Rating: A random float number in the range of 1.0 and 5.0 is used to indicate the rating.
• Revenue: A float value chosen at random to indicate the revenue received.
• Accommodation_Available: Returns a value of "Yes" or "No" depending on the availability of accomodation.
• Target Size: During the loop, the file size is measured and rows are added until the file size reaches roughly 310.43 KB.

You will have a tourism_dataset.csv file, roughly 310.43 KB in size, after executing this code. Depending on the data distribution and file overhead, adjustments can be required.

1) Data Introduction • The Hotels from Around the World Dataset provides over 1,000 hotel data (including ratings, reviews, and room rates) provided by Booking.com .

2) Data Utilization (1) Hotels from Around the World Dataset has characteristics that: • This dataset is a list of over 10 major city hotels worldwide. This includes ratings, city, country, and number of customer reviews. • This dataset was extracted on February 18, 2025 and is based on a one-night reservation from March 18-19, 2025. (2) Hotels from Around the World Dataset can be used to: • Analysis of hotel ratings and reviews : Using hotel-specific ratings and review data, it can be used for text mining and emotional analysis studies such as customer satisfaction analysis, hotel service quality assessment, and classification of positive and negative reviews. • Tourism and Location Strategy Research : It can be used for research on the tourism industry and real estate market, including comparing characteristics by popular area, location strategy, and hotel rating by analyzing various characteristics such as hotel location, rating, convenience facilities, and number of reviews.

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
    allowing for precise market research and localized business strategies.



Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
  Market Research & Competitive Intelligence: Identify emerging travel trends, monitor competitor performance, and assess market demand.
  Travel & Hospitality App Development: Enhance travel platforms with accurate, up-to-date data on hotels, restaurants, and rental properties.
  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Stay ahead in the travel industry with high-quality, structured data that powers smarter decisions.

"This Data Report presents a curated dataset capturing global and regional trends in event and recreation tourism from 2019 to 2024, with a particular focus on Uzbekistan’s emerging market. Data were derived from industry reports (e.g., World Bank, UNESCO, Statista, Grand View Research, Greenpeace), government publications (Uzbekistan State Tourism Committee), and academic sources, compiling economic impact values, tourist preferences, and sustainability indicators. The dataset covers global event tourism market growth, economic valuation, tourist preferences (including event-driven travel, millennial participation, sustainability demand), and comparative impacts (CO2 emissions, overtourism risk, infrastructure investment). Data collection occurred Dec 2023–Mar 2024. Duplications and pre-2019 data were excluded; only verified sources retained. Normalization applied where needed. This dataset provides a foundation for research in tourism economics, cultural tourism development, sustainability integration, and policy analysis, comparing Uzbekistan with global benchmarks."

📍 Looking for high-quality global data on tourism industry? ISTARI.AI provides comprehensive, ready-to-use datasets covering hotels, tourist agencies, travel agents, travel magazine, bars, and restaurants worldwide – including location, contact, and detailed business information.

📊 Our Tourism data includes: - Organizational structure & key personnel - Products, services & partnerships - Verified contact & domain information - Technology stack & business descriptions - Detailed geographic data (address, region, country)

Our datasets are ideal for: - Location-based services & apps - Market analysis & competitive intelligence - Retail expansion & site planning - Ad targeting & geofencing - Lead generation & marketing outreach

All data is machine-generated, frequently updated, and sourced from publicly available web data, ensuring high freshness and consistency.

✅ Ensuring Data Quality - Developed in close collaboration with academic experts to guarantee expert-level accuracy - Created together with researchers at the University of Mannheim - Validated in the award-winning academic study: "When is AI Adoption Contagious? Epidemic Effects and Relational Embeddedness in the Inter-Firm Diffusion of Artificial Intelligence" - Co-authored by scholars from the University of Mannheim, University of Giessen, University of Hohenheim, and ETH Zurich

With ISTARI.AI, you get structured, high-quality tourism datasets from across the globe – ready for direct integration into your systems.

The number of international tourist arrivals in Asia was forecast to continuously increase between 2024 and 2029 by in total 174.7 million arrivals (+33.49 percent). After the ninth consecutive increasing year, the arrivals is estimated to reach 696.34 million arrivals and therefore a new peak in 2029. Depicted is the number of inbound international tourists. According to World Bank this refers to tourists travelling to a country which is not their usual residence, whereby the main purpose is not work related and the planned visitation period does not exceed 12 months. The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the number of international tourist arrivals in countries like North America and Caribbean.

--- DATASET OVERVIEW --- This dataset captures detailed information about each vacation rental property listing across multiple OTAs. This report provides performance metrics and ranking insights that help users benchmark their rental properties and key in on performance drivers across all global vacation markets Key Data has to offer.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Identifiers: Unique identifiers for each property with OTA-specific IDs - Historic Performance Metrics: Revenue, ADR, guest occupancy and more over the last 12 months. - Forward Looking Performance Metrics: Revenue, ADR, guest occupancy and more over the next 6 months. - Performance Tiering and Percentile Ranking amongst peer listings within the specified performance ranking groups. --How Listings Are Grouped: Listing Source (e.g., Airbnb vs. Vrbo) Market (identified by uuid) - Market type = vacation areas Property Type (house, apartment, unique stays, etc.) Number of Bedrooms (0, 1, 2, 3, 4, 5, 6, 7, 8+)

--- USE CASES --- Market Research and Competitive Analysis: VR professionals and market analysts can use this dataset to conduct detailed analyses of vacation rental supply across different markets. The data enables identification of property distribution patterns, amenity trends, pricing strategies, and host behaviors. This information provides critical insights for understanding market dynamics, competitive positioning, and emerging trends in the short-term rental sector.

Property Management Optimization: Property managers can leverage this dataset to benchmark their properties against competitors in the same geographic area. By analyzing listing characteristics, amenity offerings and guest reviews of similar properties, managers can identify optimization opportunities for their own portfolio. The dataset helps identify competitive advantages, potential service gaps, and management optimization strategies to improve property performance.

Investment Decision Support: Real estate investors focused on the vacation rental sector can utilize this dataset to identify investment opportunities in specific markets. The property-level data provides insights into high-performing property types, optimal locations, and amenity configurations that drive guest satisfaction and revenue. This information enables data-driven investment decisions based on actual market performance rather than anecdotal evidence.

Academic and Policy Research: Researchers studying the impact of short-term rentals on housing markets, urban development, and tourism trends can use this dataset to conduct quantitative analyses. The comprehensive data supports research on property distribution patterns and the relationship between short-term rentals and housing affordability in different markets.

Travel Industry Analysis: Travel industry analysts can leverage this dataset to understand accommodation trends, property traits, and supply and demand across different destinations. This information provides context for broader tourism analysis and helps identify connections between vacation rental supply and destination popularity.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: monthly | quarterly | annually • Delivery Method: scheduled file loads • File Formats: csv | parquet • Large File Format: partitioned parquet • Delivery Channels: Google Cloud | Amazon S3 | Azure Blob • Data Refreshes: monthly

Dataset Options: • Coverage: Global (most countries) • Historic Data: Last 12 months performance • Future Looking Data: Next 6 months performance • Point-in-Time: N/A

Contact us to learn about all options.

--- DATA QUALITY AND PROCESSING --- Our data collection and processing methodology ensures high-quality data with comprehensive coverage of the vacation rental market. Regular quality assurance processes verify data accuracy, completeness, and consistency.

The dataset undergoes continuous enhancement through advanced data enrichment techniques, including property categorization, geographic normalization, and time series alignment. This processing ensures that users receive clean, structured data ready for immediate analysis without extensive preprocessing requirements.

This dataset is about book subjects. It has 3 rows and is filtered where the books is Tourism and development in the developing world. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Introduction

This Russian Call Center Speech Dataset for the Travel industry is purpose-built to power the next generation of voice AI applications for travel booking, customer support, and itinerary assistance. With over 30 hours of unscripted, real-world conversations, the dataset enables the development of highly accurate speech recognition and natural language understanding models tailored for Russian -speaking travelers.

Created by FutureBeeAI, this dataset supports researchers, data scientists, and conversational AI teams in building voice technologies for airlines, travel portals, and hospitality platforms.

Speech Data

The dataset includes 30 hours of dual-channel audio recordings between native Russian speakers engaged in real travel-related customer service conversations. These audio files reflect a wide variety of topics, accents, and scenarios found across the travel and tourism industry.

Topic Diversity

Inbound and outbound conversations span a wide range of real-world travel support situations with varied outcomes (positive, neutral, negative).

These scenarios help models understand and respond to diverse traveler needs in real-time.

Transcription

Each call is accompanied by manually curated, high-accuracy transcriptions in JSON format.

Metadata

Extensive metadata enriches each call and speaker for better filtering and AI training:

Usage and Applications

This dataset is ideal for a variety of AI use cases in the travel and tourism space:

Dataset

This dataset was created by Abu Bakar Sayem

Contents

China Tourism Industry: Total Revenue data was reported at 5,290,000.000 RMB mn in 2023. This records a decrease from the previous number of 6,630,000.000 RMB mn for 2019. China Tourism Industry: Total Revenue data is updated yearly, averaging 1,428,749.462 RMB mn from Dec 1999 (Median) to 2023, with 22 observations. The data reached an all-time high of 6,630,000.000 RMB mn in 2019 and a record low of 399,908.000 RMB mn in 1999. China Tourism Industry: Total Revenue data remains active status in CEIC and is reported by Ministry of Culture and Tourism. The data is categorized under Global Database’s China – Table CN.QAA: Tourism Industry Overview.

China Tourism Revenue: Domestic data was reported at 5,754,300.000 RMB mn in 2024. This records an increase from the previous number of 4,913,310.000 RMB mn for 2023. China Tourism Revenue: Domestic data is updated yearly, averaging 946,649.296 RMB mn from Dec 1990 (Median) to 2024, with 32 observations. The data reached an all-time high of 5,754,300.000 RMB mn in 2024 and a record low of 17,000.000 RMB mn in 1990. China Tourism Revenue: Domestic data remains active status in CEIC and is reported by Ministry of Culture and Tourism. The data is categorized under China Premium Database’s Tourism Sector – Table CN.QAA: Tourism Industry Overview.

China Travel Agency: Number of Enterprise data was reported at 39,580.000 Unit in 2023. This records an increase from the previous number of 32,603.000 Unit for 2022. China Travel Agency: Number of Enterprise data is updated yearly, averaging 20,399.000 Unit from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 40,682.000 Unit in 2020 and a record low of 3,826.000 Unit in 1995. China Travel Agency: Number of Enterprise data remains active status in CEIC and is reported by Ministry of Culture and Tourism. The data is categorized under China Premium Database’s Tourism Sector – Table CN.QAA: Tourism Industry Overview.

Introduction

This Indian English Call Center Speech Dataset for the Travel industry is purpose-built to power the next generation of voice AI applications for travel booking, customer support, and itinerary assistance. With over 30 hours of unscripted, real-world conversations, the dataset enables the development of highly accurate speech recognition and natural language understanding models tailored for English -speaking travelers.

Created by FutureBeeAI, this dataset supports researchers, data scientists, and conversational AI teams in building voice technologies for airlines, travel portals, and hospitality platforms.

Speech Data

The dataset includes 30 hours of dual-channel audio recordings between native Indian English speakers engaged in real travel-related customer service conversations. These audio files reflect a wide variety of topics, accents, and scenarios found across the travel and tourism industry.

Topic Diversity

Inbound and outbound conversations span a wide range of real-world travel support situations with varied outcomes (positive, neutral, negative).

These scenarios help models understand and respond to diverse traveler needs in real-time.

Transcription

Each call is accompanied by manually curated, high-accuracy transcriptions in JSON format.

Metadata

Extensive metadata enriches each call and speaker for better filtering and AI training:

Usage and Applications

This dataset is ideal for a variety of AI use cases in the travel and tourism space:

Tourism Revenues in Turkey increased to 24300 USD Million in the third quarter of 2025 from 16280 USD Million in the second quarter of 2025. This dataset provides - Turkey Tourism Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Introduction

This Vietnamese Call Center Speech Dataset for the Travel industry is purpose-built to power the next generation of voice AI applications for travel booking, customer support, and itinerary assistance. With over 30 hours of unscripted, real-world conversations, the dataset enables the development of highly accurate speech recognition and natural language understanding models tailored for Vietnamese -speaking travelers.

Created by FutureBeeAI, this dataset supports researchers, data scientists, and conversational AI teams in building voice technologies for airlines, travel portals, and hospitality platforms.

Speech Data

The dataset includes 30 hours of dual-channel audio recordings between native Vietnamese speakers engaged in real travel-related customer service conversations. These audio files reflect a wide variety of topics, accents, and scenarios found across the travel and tourism industry.

Topic Diversity

Inbound and outbound conversations span a wide range of real-world travel support situations with varied outcomes (positive, neutral, negative).

These scenarios help models understand and respond to diverse traveler needs in real-time.

Transcription

Each call is accompanied by manually curated, high-accuracy transcriptions in JSON format.

Metadata

Extensive metadata enriches each call and speaker for better filtering and AI training:

Usage and Applications

This dataset is ideal for a variety of AI use cases in the travel and tourism space: