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 Hotel Dataset: Rates, Reviews & Amenities(6k+) dataset includes hotel rates, guest reviews, and available amenities from two popular travel websites, TripAdvisor and Booking.com. The dataset can be used to analyze trends and insights in the hospitality industry, and inform decisions related to pricing, marketing, and customer service. Booking.com: Founded in 1996 in Amsterdam, Booking.com has grown from a small Dutch start-up to one of the world’s leading digital travel companies. Part of Booking Holdings Inc. (NASDAQ: BKNG), Booking.com’s mission is to make it easier for everyone to experience the world.

By investing in technology that takes the friction out of travel, Booking.com seamlessly connects millions of travelers to memorable experiences, a variety of transportation options, and incredible places to stay – from homes to hotels, and much more. As one of the world’s largest travel marketplaces for both established brands and entrepreneurs of all sizes, Booking.com enables properties around the world to reach a global audience and grow their businesses.

Booking.com is available in 43 languages and offers more than 28 million reported accommodation listings, including over 6.6 million homes, apartments, and other unique places to stay. Wherever you want to go and whatever you want to do, Booking.com makes it easy and supports you with 24/7 customer support. Tripadvisor, the world's largest travel guidance platform*, helps hundreds of millions of people each month** become better travelers, from planning to booking to taking a trip. Travelers across the globe use the Tripadvisor site and app to discover where to stay, what to do and where to eat based on guidance from those who have been there before. With more than 1 billion reviews and opinions of nearly 8 million businesses, travelers turn to Tripadvisor to find deals on accommodations, book experiences, reserve tables at delicious restaurants and discover great places nearby. As a travel guidance company available in 43 markets and 22 languages, Tripadvisor makes planning easy no matter the trip type. The subsidiaries of Tripadvisor, Inc. (Nasdaq: TRIP), own and operate a portfolio of travel media brands and businesses, operating under various websites and apps.

Context

This dataset was created by our in house teams at PromptCloud and DataStock. This dataset holds up to 30K records in it. You can download the full dataset here.

Content

This dataset contains the following: - Total Records Count: 457541  - Domain Name:: makemytrip.com  - Date Range: 01st Sep 2019 - 30th Sep 2019   - File Extension :: csv

• Available Fields : -- Uniq Id, -- Crawl Timestamp, -- Package Name, -- Page Url, -- Package Type, -- Company, -- Destination, -- Itinerary, -- Places Covered, -- Travel Date, -- Hotel Details, -- Start City, -- Airline, -- Flight Stops, -- Onwards Return Flight Time, - -- Meals, -- Price Per Two Persons, -- Per Person Price, -- Sightseeing Places Covered, -- Initial Payment For Booking, -- Cancellation Rules, -- Date Change Rules 

Acknowledgements

We wouldn't be here without the help of our in the house web scraping team at PromptCloud and DataStock.

Inspiration

The data that has been posted here we came up with this idea of posting hotel listings for various data scientists and different researchers. We hope that you use this data for analysis.

In a context of numerous projects, Montreal has made every effort to improve the mobility of users, all modes of transport combined. The [MTL Trajet] application (https://ville.montreal.qc.ca/mtltrajet/) is one of the many measures put forward to better understand and facilitate travel in Montreal. This set contains the filtered data collected during this study. The data is divided into two different sets to offer the maximum amount of data without compromising the confidentiality of users of the MTL Trajet application. Points The “points_mtl_trip” dataset includes each of the points, with an acceptable positioning quality, collected during the MTL Trajet travel survey. These points have been treated and filtered to remove all sensitive items on a user. Journeys This file includes each of the trips obtained using the filtered data. This file is therefore an agglomeration of points into segments making it possible to define the routes of users. These segments were produced using the OSRM routing engine in order to identify points on the networks (lanes, bike paths, subway, suburban train). In order to protect the privacy of users of the application, the information allowing trips to be linked to users has been removed. These trips are then complete; on the other hand, it is impossible to determine whether two trips were made by the same user.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

About Dataset Uber and Ola Play Store Reviews Dataset Description: This dataset contains Play Store reviews for two prominent car services, Uber 🚗 and Ola 🚕. It provides valuable insights into user experiences, feedback, and sentiments towards these ride-sharing apps. The dataset includes various attributes such as review ID, user name, review title, review description, rating, thumbs up count, review date, developer response, developer response date, app version, language code, and country code. The dataset is a valuable resource for analyzing user sentiments, trends, and contributing factors that impact user satisfaction.

Columns: - review_id: A unique identifier for each review. - user_name: The username associated with the review. - review_title: The title of the review. - review_description: The main content of the review provided by the user. - rating: The user's rating for the app (e.g., on a scale of 1 to 5). - thumbs_up: The count of thumbs up 👍 given by other users for this review. - review_date: The date when the review was posted. - developer_response: Any response provided by the app developer to the review. - developer_response_date: The date of the developer's response. - appVersion: The version of the app when the review was submitted. - language_code: The code representing the language of the review. - country_code: The code representing the country of the user.

Preprocessing Steps: - Missing Values Handling: Rows with missing values in essential columns were dropped to ensure data integrity. - Text Cleaning: Text data in review_title and review_description were converted to lowercase, and special characters, punctuation, and extra white spaces were removed. - Rating Normalization: Ratings were scaled to a consistent 0-10 scale for better comparability. - Date Parsing: Dates in review_date and developer_response_date were parsed into a standardized format. - Categorical Encoding: Categorical variables (appVersion, language_code, country_code) were one-hot encoded for analysis. - Sentiment Analysis: Sentiment analysis was performed on review_description to categorize reviews as positive 😀, negative 😞, or neutral 😐. - Feature Engineering: A new feature indicating the presence of developer responses was created for further analysis.

Potential Use Cases: - Sentiment Analysis: Understand user sentiment towards Uber and Ola through sentiment analysis of reviews. - Rating Analysis: Analyze the distribution of ratings and trends in user satisfaction. - Top Contributors: Identify influential users based on review count and thumbs up count. - Developer Response Impact: Investigate whether developer responses influence user satisfaction. - Feature Request Analysis: Analyze recurring keywords to identify common feature requests. - Geographical Insights: Explore user sentiments and ratings across different countries. - App Version Analysis: Study user sentiments based on different app versions. - User Experience Trends: Extract insights from recurring keywords in review titles and descriptions.

This is the dataset that I use from the survey results related to user satisfaction with several online travel applications in Indonesia. I use this data for analysis purposes in my research.

CSL-Daily (Chinese Sign Language Corpus) is a large-scale continuous SLT dataset. It provides both spoken language translations and gloss-level annotations. The topic revolves around people's daily lives (e.g., travel, shopping, medical care), the most likely SLT application scenario.

[1] Improving Sign Language Translation with Monolingual Data by Sign Back-Translation, CVPR, 2021.

Our nation's roadway system is a vast network that connects places and people within and across national borders. Planners and engineers have developed elements of this network with particular travel objectives in mind. These objectives range from serving long-distance passenger and freight needs to serving neighborhood travel from residential developments to nearby shopping centers. The functional classification of roadways defines the role each element of the roadway network plays in serving these travel needs. Federal legislation continues to use functional classification in determining eligibility for funding under the Federal-aid program. Transportation agencies describe roadway system performance, benchmarks and targets by functional classification.

Dataset Overview: The “Chicago Divvy Bikeshare Data • Apr'23 - Oct'24” dataset consists of ********anonymized trip data as made monthly available by Lyft Bikes and Scooters, LLC, on its operation of City of Chicago’s Divvy bicycle sharing service.

Data Science & Analytics Applications: This dataset is recommended in Track A of Google Data Analytics Capstone Project. It is useful for analysts seeking to perform descriptive analysis, geographic analysis, trend visualization, and predictive modeling on real app data from bike-sharing services.

The Data: Each trip is anonymized and includes:

• Trip start day and time
• Trip end day and time
• Trip start station
• Trip end station
• Rider type (Member, Single Ride, and Day Pass)

According to the provider of data, Lyft:

« The data has been processed to remove trips that are taken by staff as they service and inspect the system; and any trips that were below 60 seconds in length (potentially false starts or users trying to re-dock a bike to ensure it was secure). »

Data License Agreement: https://divvybikes.com/data-license-agreement.

Acknowledgments: Thanks to Lyft Bikes and Scooters, LLC and The City of Chicago for making this data publicly available.

Source: Lyft Bikes and Scooters, LLC

Our nation's roadway system is a vast network that connects places and people within and across national borders. Planners and engineers have developed elements of this network with particular travel objectives in mind. These objectives range from serving long-distance passenger and freight needs to serving neighborhood travel from residential developments to nearby shopping centers. The functional classification of roadways defines the role each element of the roadway network plays in serving these travel needs. Federal legislation continues to use functional classification in determining eligibility for funding under the Federal-aid program. Transportation agencies describe roadway system performance, benchmarks and targets by functional classification.

The cab bookings data is from namma yatri ride-hailing services within the Bangalore region. It is downloaded from nammayatri.in

Namma Yatri has become Bengaluru's most loved auto app, since its formal launch in January 2023. It is a Direct-to-Driver app. There is no commission or middle-men. What one pays goes 100% to the Driver and his family.

Here is the github page: https://github.com/nammayatri

Descriptive summary of dataset using median values.