In June 2025, the number of visits to the travel and tourism website booking.com declined over the previous month, totaling approximately *** million. In 2025, Booking's web page was the most visited travel and tourism website worldwide.

booking.com is ranked #176 in US with 445.68M Traffic. Categories: Hospitality, Online Services, Travel and Tourism. Learn more about website traffic, market share, and more!

Booking.com was the most-visited travel and tourism website worldwide in 2024, with an average of 562.6 million visits per month during the measured period. Tripadvisor.com ranked second, with 150.2 million visits, while Airbnb.com registered over 105.4 million average accesses.

Less than *********** Russians visited the online travel website Booking.com in September 2022, which was over **** times less than in the corresponding month of 2019, prior to the COVID-19 pandemic. The platform suspended its operations in Russia in response to the war in Ukraine in 2022. Most Booking.com visitors in Russia were between 25 and 34 years old.

In June 2025, the United States accounted for the highest share of visits to the travel and tourism website booking.com. During that month, website visits from the U.S. represented almost ** percent of total visits to Booking's web page. In 2025, booking.com was the most visited travel and tourism website worldwide.

In June 2025, the number of visits to the travel and tourism website booking.com totaled roughly *** million. That month, mobiles accounted for the most views, with over *** million visits coming from such devices. Over the period considered, booking.com's visits peaked at nearly *** million in July 2024, with mobile visits reaching *** million that month.

In June 2025, booking.com was the most visited travel and tourism website worldwide. That month, Booking’s web page recorded around *** million visits. Tripadvisor.com and airbnb.com followed in the ranking, with roughly *** million and ** million visits, respectively. Popular online travel agencies in the U.S. Online travel agencies (OTAs), such as Booking.com and Expedia, offer a wide variety of services, including online hotel bookings, flight reservations, and car rentals. According to the Statista Consumer Insights Global survey, when looking at flight search engine online bookings by brand in the United States, Booking.com and Expedia were the most popular options when it came to making online flight reservations in 2025. When focusing on hotel and private accommodation online bookings in the U.S., Booking.com was again the most popular brand, followed by Airbnb, Expedia, and Hotels.com. Booking Holdings vs. Expedia Group Booking.com is one of the most popular sites of the online travel group Booking Holdings, the leading online travel agency worldwide based on revenue, that also owns brands like Priceline, Kayak, and Agoda. In 2024, Booking Holdings' revenue amounted to almost ** billion U.S. dollars, the highest figure reported by the company to date. Meanwhile, global revenue of Expedia Group, which manages brands like Expedia, Hotels.com, and Vrbo, reached nearly ** billion U.S. dollars that year.

About columns

1- ID : unique identifier of each booking

2- n_adults : Number of adults

3- n_children : Number of Children

4- weekend_nights : Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel

5- week_nights : Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel

6- meal_plan : Type of meal plan booked by the customer

7- car_parking_space : Does the customer require a car parking space? (0 - No, 1- Yes)

8- room_type: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels.

9- lead_time: Number of days between the date of booking and the arrival date

10- year : Year of arrival date

11- month : Month of arrival date

12- date : Date of the month

13- market_segment : Market segment designation.

14- repeated_guest : Is the customer a repeated guest? (0 - No, 1- Yes)

15- previous_cancellations : Number of previous bookings that were canceled by the customer prior to the current booking

16- previous_bookings_not_canceled : Number of previous bookings not canceled by the customer prior to the current booking

17- avg_room_price : Average price per day of the reservation; prices of the rooms are dynamic. (in euros)

18- special_requests : Total number of special requests made by the customer (e.g. high floor, view from the room, etc)

19- status : Flag indicating if the booking was canceled or not.

According to 2023 estimates, Booking Holdings' global revenue was evenly split between mobile and desktop bookings. As estimated, the online travel agency (OTA) generated revenue of roughly **** billion U.S. dollars through mobile devices and **** billion U.S. dollars via desktop bookings. In contrast, it was estimated that most of the Expedia Group and Airbnb's revenue came from desktop users that year. What are the most visited travel and tourism websites? In January 2024, booking.com topped the ranking of the most visited travel and tourism websites worldwide, ahead of tripadvisor.com and airbnb.com. When breaking down the visits to booking.com by country that month, the United States emerged as the leading market, followed by the United Kingdom and Germany. What are the most popular online travel agency apps worldwide? In 2024, Airbnb, Booking.com, and Expedia were among the most downloaded online travel agency apps worldwide. Booking.com is one of the leading brands of Booking Holdings, along with Priceline, Agoda, and Kayak. Meanwhile, Expedia is among the most popular brands of the Expedia Group, together with Vrbo, Hotels.com, and Trivago.

As of July 2025, Booking Holdings recorded the highest market cap among the selected online travel companies worldwide. As of that month, Booking Holdings – the leading online travel agency (OTA) worldwide by revenue – recorded a market cap of almost *** billion U.S. dollars. Airbnb and Trip.com Group followed in the ranking, with a market cap of roughly ** billion and ** billion U.S. dollars, respectively. What are the most visited travel and tourism websites? Booking.com, Booking Holdings' flagship brand, was the most visited travel and tourism website worldwide in 2025, ranking ahead of tripadvisor.com and airbnb.com. When looking at the geographical distribution of booking.com's visits, the United States accounted for the highest traffic, followed by Germany and Italy. How big is the online travel market? As shown by a breakdown of travel and tourism's global revenue by sales channel, online transactions play a fundamental role in this market, representing over ********** of total travel and tourism's revenue in 2024. That year, the online travel market size worldwide was estimated at over *** billion U.S. dollars, recording an annual increase in revenue.

Customer behavior and booking possibilities have been radically changed by online hotel reservation channels. Cancellations or no-shows cause a significant number of hotel reservations to be canceled. Cancellations can be caused by a variety of factors, such as scheduling conflicts, changes in plans, etc. In many cases, this is made easier by the possibility of doing so free or at a low cost, which is beneficial for hotel guests but less desirable and possibly revenue-diminishing for hotels.

As a Data Scientist, your job is to build a Machine Learning model to help the Hotel Owners better understand if the customer is going to honor the reservation or cancel it ?

Dataset Description The file contains the different attributes of customers' reservation details. The detailed data dictionary is given below Booking_ID: unique identifier of each booking No of adults: Number of adults No of children: Number of Children noofweekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel noofweek_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel typeofmeal_plan: Type of meal plan booked by the customer: requiredcarparking_space: Does the customer require a car parking space? (0 - No, 1- Yes) roomtypereserved: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels. lead_time: Number of days between the date of booking and the arrival date arrival_year: Year of arrival date arrival_month: Month of arrival date arrival_date: Date of the month Market segment type: Market segment designation. repeated_guest: Is the customer a repeated guest? (0 - No, 1- Yes) noofprevious_cancellations: Number of previous bookings that were canceled by the customer prior to the current booking noofpreviousbookingsnot_canceled: Number of previous bookings not canceled by the customer prior to the current booking avgpriceper_room: Average price per day of the reservation; prices of the rooms are dynamic. (in euros) noofspecial_requests: Total number of special requests made by the customer (e.g. high floor, view from the room, etc) booking_status: Flag indicating if the booking was canceled or not.

--- DATASET OVERVIEW --- This dataset aggregates reservation-level data from property management systems to identify the flow of guests from origin markets to destination markets. By capturing the geographic source of bookings for each destination, it provides a comprehensive view of demand patterns, market connections, and guest preferences across different regions and time periods.

The data is sourced directly from property management system integrations, capturing the geographic origin of actual bookings rather than just inquiry or search data. This direct access to reservation information ensures that the origin market analysis reflects true booking behavior rather than just travel intent.

--- KEY DATA ELEMENTS --- Our dataset includes the following metrics for origin-destination market pairs: - Geographic Dimensions: Origin market and destination market at multiple geographic levels - Temporal Dimensions: Monthly, quarterly, and annual booking patterns by origin market - Booking Volume: Number of reservations from each origin market to each destination - Revenue Contribution: Total revenue and average booking value from each origin market - Stay Patterns: Average length of stay for guests from different origin markets - Booking Behaviors: Lead time distributions and booking channel preferences by origin market - Seasonality Metrics: Seasonal variations in demand from different origin markets - Historical and Forward Looking Trends: Year-over-year changes in booking patterns from different origin markets - Market Connections: Strength of relationship between origin-destination pairs

--- USE CASES --- Targeted Marketing Strategy Development: Property managers and destination marketing organizations can use this dataset to identify their most valuable guest origin markets and develop targeted marketing campaigns. By understanding which geographic markets generate the highest booking volumes, longest stays, or greatest revenue contribution, marketing teams can allocate resources to the markets with the highest potential return on investment. The detailed origin market insights enable precise geographic targeting for advertising campaigns, content development, and promotional efforts.

Demand Driver Analysis: Market analysts can leverage this dataset to understand the fundamental drivers of demand for specific destinations. By analyzing the correlation between origin market characteristics and booking patterns, analysts can blend external data around the demographic, economic, and geographic factors that influence travel decisions to specific destinations. This deeper understanding of demand drivers supports more accurate forecasting and strategic planning.

Emerging Market Identification Tourism authorities and property investors can use this dataset to identify emerging origin markets that show increasing demand for specific destinations. By tracking year-over-year growth in bookings from different geographic sources, stakeholders can identify early-stage market opportunities before they become widely recognized. This early identification of emerging markets provides a competitive advantage in developing targeted marketing and investment strategies.

Competitive Positioning Analysis Destination marketers can analyze how their market share from specific origin markets compares to competing destinations. The dataset enables comparison of booking patterns across different destinations from the same origin markets, revealing competitive strengths and weaknesses in capturing demand from specific geographic segments.

Crisis Recovery Planning Tourism authorities and property managers can use origin market data to develop targeted recovery strategies following travel disruptions. By understanding which origin markets historically recover more quickly after disruptions and which markets show the strongest booking resilience, stakeholders can focus recovery efforts on the markets most likely to drive initial demand recovery.

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

Dataset Options: • Destination Area Coverage: North America + Top Global Tourism Markets with Strong Coverage in Europe and Australia • Historic Data: Available (2019 for most areas) • Future Looking Data: Available (Current date + 180 days+) • Point-in-Time: Available (with weekly as of dates) • Aggregation and Filtering Options: • Area/Market (required) • Time Scales (daily, weekly, monthly) • Property Characteristics (property types, bedroom counts, performance tiers, etc.)

Contact us to learn about all options.

--- DATA QUALITY AND PROCESSING --- Our data processing methodology ensures high-quality, reliable origin...

The Future Schedules API is perfect for: • Travel agency and flight booking websites where users are expected to submit a date and view available flights • Websites, tools or apps to display scheduled flights on a given date • Flight schedule and airway traffic analysis based on region or dates

We have developed many filters you can use in the input to request the exact data you need without having to filter the data on your end.

The data includes: - Departure and arrival airport information: IATA codes - Weekday: The day of the week of the flight, "1" being Monday - Terminal and gate: The most common terminal and the gate number of the departing/arriving flight - Take-off information: Scheduled departure or arrival time of the flight - Aircraft details: Model code and text - Airline details: Name, IATA and ICAO codes - Flight information: Flight number with flight IATA and ICAO codes

1) Request For the departure schedule of a certain airport on a certain future date.

GET http://aviation-edge.com/v2/public/flightsFuture?key=[API_KEY]&type=departure&iataCode=BER&date=YYYY-MM-DD

For the arrival schedule of a certain airport on a certain future date.

GET http://aviation-edge.com/v2/public/flightsFuture?key=[API_KEY]&type=arrival&iataCode=BER&date=YYYY-MM-DD

For the flights that are scheduled to arrive at a certain airport on a certain date (out of a departure schedule).

GET http://aviation-edge.com/v2/public/flightsFuture?key=[API_KEY]&type=departure&iataCode=BER&arr_iataCode=ORY&date=YYYY-MM-DD

For the flights that are scheduled to depart from a certain airport on a certain date (out of an arrival schedule).

GET https://aviation-edge.com/v2/public/flightsFuture?key=[API_KEY]&type=arrival&iataCode=BER&dep_iataCode=ory&date=YYYY-MM-DD

2) Filters &iata_code= (obligatory) Departure or arrival airport IATA code depending on the "&type=" value &type= (obligatory) Either "departure" or "arrival" as both within the same query is not possible &date= (obligatory) Future date in YYYY-MM-DD format

&dep_iataCode= filter of departure airport if "arrival" for "&type=" was chosen, based on the airport IATA code &dep_icaoCode= filter of departure airport if "arrival" for "&type=" was chosen, based on the airport ICAO code &arr_iataCode= filter of arrival airport if "departure" for "&type=" was chosen, based on the airport IATA code &arr_icaoCode= filter of arrival airport if "departure" for "&type=" was chosen, based on the airport ICAO code &airline_iata= option to filter airline based on airline IATA code &airline=icao= option to filter airline based on airline ICAO code &flight_num= option to filter a specific flight based on its flight number

3) Example Output: [ {"weekday": "1", "departure": { "iataCode": "mty", "icaoCode": "mmmy", "terminal": "c", "gate": "f2", "scheduledTime": "20:35" }, "arrival": {"iataCode": "iah", "icaoCode": "kiah", "terminal": "d", "gate": "d12", "scheduledTime": "22:00" }, "aircraft": {"modelCode": "a320", "modelText": "airbus a320-232" }, "airline": {"name": "vivaaerobus", "iataCode": "vb", "icaoCode": "viv"}, "flight": {"number": "616", "iataNumber": "vb616", "icaoNumber": "viv616"} } ]

Note: Schedules that are up to 1 year ahead of the current date are available.

--- DATASET OVERVIEW --- Our Vacation Rental Area KPIs from Direct PM Reservation Data Integrations provides comprehensive market performance metrics for professionally managed vacation rentals sourced directly from property management systems. This dataset delivers authoritative insights into market performance based on actual reservation data rather than listing information, offering an accurate view of booking patterns, revenue generation, and operational metrics across different markets.

The data is sourced directly from property management system integrations, capturing actual reservation details rather than OTA listing information. This direct access to booking data ensures that the performance metrics reflect true market activity rather than just advertised availability or pricing. Our coverage is particularly strong in North America, Europe and Australia, with growing global representation.

--- KEY DATA ELEMENTS --- Our dataset includes the following market-level performance indicators for professionally managed vacation rentals: - Geographic Identifiers: Multiple geographic levels (vacation area, vacation region, county, etc) - Temporal Dimensions: Daily, weekly, monthly, and quarterly performance metrics - Occupancy Metrics: Actual occupancy rates based on confirmed reservations - Revenue Metrics: Total revenue, average daily rate (ADR), and revenue per available rental night (RevPAR) - Booking Patterns: Lead time distribution, length of stay patterns, and booking frequency - Reservation Channel Mix: Distribution of bookings across different reservation channels - Seasonality Indicators: Performance variations across seasons, months, and days of week - Performance Segmentation: Metrics broken down by property type, size, and price tier - Historical Pacing: Snapshots into how stay date ranges developed for tracking pacing trends - Forward Looking Trends: Area KPIs 180-365 days into the future

--- USE CASES --- Performance Benchmarking for Professional Managers: Property management companies can benchmark their portfolio performance against market-wide metrics for professionally managed properties. By comparing company-specific occupancy rates, ADR, and RevPAR against market averages for similar property types, managers can assess relative performance and identify areas for improvement. These benchmarks provide crucial context for performance evaluation and goal setting specific to professional management operations.

Operational Strategy Development: Property management operators can leverage this dataset to develop operational strategies based on industry benchmarks. The reservation patterns, lead time distributions, and cancellation metrics provide insights into optimal staffing levels, maintenance scheduling, and operational workflows. This information supports the development of efficient operational practices aligned with actual booking patterns.

Revenue Management Optimization: Revenue managers can use this dataset to develop sophisticated revenue optimization strategies based on actual booking patterns to benchmark broader, inferred information from OTAs. The detailed revenue metrics and booking patterns provide insights into rate elasticity, optimal minimum stay requirements, and the revenue impact of different pricing approaches. This information supports the development of data-driven revenue management strategies tailored to specific markets and property types.

Distribution Channel Strategy: Property managers can analyze reservation channel performance across different markets to optimize their distribution strategy. By understanding which channels deliver the highest value bookings in specific markets, managers can focus their efforts and investment on the most productive channels for their target areas and property types.

Investment Decision Support: Real estate investors focused on professionally managed vacation rentals can analyze market performance across different regions to identify investment opportunities. The dataset provides insights into revenue potential, seasonality impacts, and overall market health based on actual booking data, supporting data-driven acquisition and portfolio expansion decisions.

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

Dataset Options: • Coverage: North America + Top Global Tourism Markets with Strong Coverage in Europe and Australia • Historic Data: Available (2019 for most areas) • Future Looking Data: Available (Current date + 180 days+) • Point-in-Time: Available (with weekly as of dates) • Aggregation and Filtering Options: • Area/Market (required) • Time Scales (daily, weekly, monthly) • Property Characteris...

The airports the API returns include airports that have the submitted letters anywhere in them. For example, when the letters "ist" are requested, the API returns both "Istanbul Airport" and "Bristol Airport" (among other items).

The API is particularly useful for travel agency and flight booking websites, tools or apps to display the relevant airport and cities as a list for the user to choose from.

The city and airport data the Autocomplete API returns include: - IATA code and name, - Country code and name, - Airport location (latitude and longitude) - Time zone

1) Example Fields of Use - Travel agency and flight booking/schedule tracking websites, tools, apps where users are expected to submit a departure or arrival city or airport - Any autocomplete feature related to cities or airports

2) Example Input Airports and cities with the letters containing "xyz" in them:

GET http://aviation-edge.com/v2/public/autocomplete?key=[API_KEY]&city=xyz

3) Example Output [ { "code": "AMS", "name": "Amsterdam", "cityCode": "AMS", "cityName": "Amsterdam", "countryCode": "NL", "countryName": "Netherlands", "lat": 52.3730556, "lng": 4.8922222, "timezone": "Europe/Amsterdam", "type": "city" } ], "airports": [ { "code": "ZYA", "name": "Amsterdam Centraal Railway Station", "cityCode": "AMS", "cityName": "Amsterdam", "countryCode": "NL", "countryName": "Netherlands", "lat": 52.3730556, "lng": 4.8922222, "timezone": "Europe/Amsterdam", "type": "rail_station", "isRailRoad": 1, "isBusStation": 0 }, { "code": "AMS", "name": "Schiphol", "cityCode": "AMS", "cityName": "Amsterdam", "countryCode": "NL", "countryName": "Netherlands", "lat": 52.30907, "lng": 4.763385, "timezone": "Europe/Amsterdam", "type": "airport", "isRailRoad": 0, "isBusStation": 0 } ], "airportsByCities": }, ... ]

Performance metrics of a client using an SEO Retainer over a 12-month period, showing growth in organic traffic, bookings, keyword rankings, page speed, and backlinks.

In June 2025, booking.com was the most visited travel and tourism website in France, with over ** million visits. That month, lachainemeteo.com and airbnb.fr followed in the ranking, with around **** million and **** million visits, respectively.

Analysis of ‘Central Processing Unit (CPU) Processing/Booking of Arrestees’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e2b0beba-5ed0-44cf-9815-119896f40e68 on 27 January 2022.

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

MCDC is primarily responsible for the intake and law enforcement processing of adult male and female offenders arrested in Montgomery County and has a facility capacity to accommodate approximately 200 inmates. Over 13,000 offenders annually arrive at MCDC’s Central Processing Unit (CPU). This dataset captures the monthly count of those processed per Shift at our Central Processing Unit located at Montgomery County Detention Services (MCDC). There are total of three shifts (Shift 2: 6:30am-3pm, Shift 3: 2:30pm-11pm, Shift 1: 10:30pm-7am)- half hour overlap is shift change/roll call. The dataset further identifies the data as either “Criminal” or “Traffic.” Update Frequency : Monthly

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

MCDC is primarily responsible for the intake and law enforcement processing of adult male and female offenders arrested in Montgomery County and has a facility capacity to accommodate approximately 200 inmates. Over 13,000 offenders annually arrive at MCDC’s Central Processing Unit (CPU). This dataset captures the monthly count of those processed per Shift at our Central Processing Unit located at Montgomery County Detention Services (MCDC). There are total of three shifts (Shift 2: 6:30am-3pm, Shift 3: 2:30pm-11pm, Shift 1: 10:30pm-7am)- half hour overlap is shift change/roll call. The dataset further identifies the data as either “Criminal” or “Traffic.” Update Frequency : Monthly

In 2007, a cash-strapped Brian Chesky came up with a shrewd way to pay his $1,200 San Francisco apartment rent. He would offer “Air bed and breakfast”, which consisted of three airbeds,...