In 2024, more than *********** passengers traveled by air in China, an increase of **** percent compared to the previous year. This indicated a significant recovery in the aviation industry since the end COVID-19 pandemic control measures. Air travel in China As a significant international hub for both business and leisure travel, China’s airports are sites of bustling activity with cargo and passengers being transported in and out of the nation every day. Air travel is a very time-efficient mode of transportation. More and more people in China's socio-economic strata can afford air travel, which has led to the expansion of the nation’s air fleet. Prior to the COVID-19 outbreak, the number of flight hours surged by a significant degree with China Southern which is owned by China Southern Air Holding Group, reaching nearly ************* flight hours in 2019. The aviation industry shows recovery The outbreak of the COVID-19 pandemic in 2020 led to massive losses for the Chinese aviation industry, with the cancellation of flights to and from China. For the first time in the last decade or so, China experienced a decline in the volume of air passengers and air cargo. The industry recovered in 2021 with a *** percent year-over-year increase in air passenger numbers and an *** percent year-over-year increase in air cargo. Furthermore, the industry was predicted to have enhanced employment opportunities between 2021 and 2040, with a growing demand for cabin crews, technicians, and pilots.

The statistic illustrates the air travel frequency in the United States in 2019. During the survey, ** percent of Americans said they travel a few times a year.

Since 2006, the total number of air traffic passengers increased steadily. In 2021, due to the coronavirus pandemic, it is estimated that the number of air passengers traveling to and from the U.S. was only 99 million. Passenger aviation in the U.S. After the 2008 financial crisis, the U.S. passenger aviation industry experienced a profound evolution. Between 2009 and 2019, the aviation industry in the U.S. experienced an increasing concentration amongst the leading airline operators. In 2021, there were 18 major air carriers in the U.S. Moreover, between 2009 and 2019, passenger revenue generated by the U.S. airline industry increased exponentially, and then dropped sharply in 2020, due to the pandemic, reaching 49 billion U.S. dollars. Size of U.S. aviation industry Thanks to the efficiency-enhancing measures, major competing aviation firms provided a steady improvement in the aviation industry. Five out of the ten most profitable airlines worldwide are U.S.-based, indicating how large the aviation market in the U.S. is compared to the rest of the world. Besides this, the U.S. had the highest airport connectivity in 2019, reaching a score of 7.29 million.

United Kingdom based airlines transported 139.6 million passengers in 2024, up from approximately 129.4 million registered in the previous year. Over the period given, the passenger traffic peaked at 176.22 million in 2018, before decreasing significantly due to the coronavirus pandemic. EasyJet: king of the skies EasyJet is the largest airline company based in the United Kingdom. In 2022, the low-cost carrier transported 33.12 million passengers in UK via its operating company EasyJet UK Ltd. EasyJet was founded in 1995 and has seen an astonishing market growth since then, overtaking the country’s flag carrier British Airways as the leading airline in 2022. Air traffic worldwide Globally, the number of scheduled passengers is expected to reach 1.17 billion users by the end of 2027. After continuing its recovery from the COVID-19 pandemic in 2023, the sector is expected to continue growing between 5.5 and 4 percent annually between 2024 and 2027.

Daily data showing UK flight numbers and rolling seven-day average, including flights to, from, and within the UK. These are official statistics in development. Source: EUROCONTROL.

Over the given period, the number of revenue passengers boarded by United Airlines increased gradually before dropping dramatically due to the coronavirus pandemic. In the following years, passenger numbers rose again, reaching ***** million passengers in 2023.

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This spreadsheet summarises of the key travel patterns and trends relating to the TFL network and Airports around London. Some of the borough level data has been analysed using these interactive charts. Click on the image to open. The first stack graph shows the spread of mode of travel for each borough. In Kensington and Chelsea over 44 per cent of journeys are made on foot, in Bexley 59 per cent of journeys are in a car/motorcycle, and in Hackney over a 25 per cent of journeys are by bus. The second graph shows the proportion of all road casualties by road user type and borough in 2010. The City of London has the highest proportion of casualties for pedestrians, and cyclists. Some of the airports data has been presented in this one page factsheet that highlights some important facts about flights and passengers at London’s airports. The number one country where people have either come from or going to is the United States. List of tables included in the spreadsheet: 1 Aggregate travel volumes in Greater London. Estimated daily average number of journey stages, 1993-2009 2 Modal shares of daily journey stages in London, 2009 3 Annual passenger kilometres travelled by public transport (millions), London, 2008/09 - 2010/11 4 Annual journey stages by public transport (millions), 1991/92 - 2010/11 5 Index of London road traffic, major and minor roads, all motor vehicles, 2000-2009 6 Trends in road casualties, by personal injury severity, London and GB, 1991-2010 7 London road casualties by mode of travel, 2010 8 Trends in cycle flows on the TLRN, annualised indices, 2000/01 - 2010/11 9 People entering central London in the weekday morning peak, 1978 - 2009 10 Car ownership in Inner and Outer London, 2009/10 11 Hours of serious and severe disruption London-wide, 2009/10 12 Number of road works undertaken on the TLRN, Sep 2009 - Oct 2010 13 London Underground: scheduled and operated train kilometres, 1995/96 - 2009/10 14 London Underground - operated train kilometres (millions) by line, 2009/10 15 Average number of passengers per bus, train or tram, 2001/02 - 2009/10 16 Public transport fares - UK and London compared, 1994/95 - 2009/10 17 Air freight moved through London's principal airports, 1993 - 2009 18 Terminal passengers by London area airport, in millions, 2000, 2010 19 Terminal passengers by London area airport, 1990 - 2010 20 Terminal passengers by flight's country of origin or destination, 2010 21 Road Casualties by Severity and Road User Type, by Borough 2010 22 Data used in the Interactive Chart - Number of trips, distribution of trips by mode and average travel time from home to work, 2007/2008 to 2009/10 (3-year moving average) 23 Data used for Air Transport Factsheet - International terminal passengers at London airports, 2010 - All terminal passengers at London airports, in millions, 2000, 2010 - Terminal passengers at London airports by origin or destination of the flight, 2010 - Number of UK flights and passenger by London airport, 2010 Most data is from the annual report Travel in London 3, Transport for London. Further information, reports and data from the Travel in London series can be found on the TFL website.

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There were approximately **** billion domestic trips taken in the United States in 2023. Around *** billion of these were taken by leisure travelers. Both the number of business and leisure trips were forecast to increase annually between 2023 and 2028.

One measure used to analyze roadway reliability is the Planning Time Index (PTI). It is the ratio of the 95th percentile travel time relative to the free-flow (uncongested) travel time. PTI helps in understanding the impacts of nonrecurring congestion from crashes, weather, and special events. It approximates the extent to which a traveler should add extra time to their trip to ensure on-time arrival at their destination. A value of 1.0 indicates a person can expect free-flow speeds along their route. A 2.0 index value indicates a traveler should expect that the trip could be twice as long as free-flow conditions. PTI values from 2.0 to 3.0 indicate moderate unreliability, and ones greater than 3.0 are highly unreliable.

The data comes from aggregated Global Positioning System probe data—anonymized data from mobile apps, connected vehicles, and commercial fleets—provided to the Probe Data Analytics (PDA) Suite by INRIX, a travel data technology company. The PDA Suite was created by a consortium of sponsors, including the Eastern Transportation Coalition and the University of Maryland.

PTI values by region, subregion, and county are grouped either as highway facilities or local roads. Highways are roadway segments classified as interstates, turnpikes, and expressways in the PDA Suite. Local roads are segments classified as U.S. routes, state routes, parkways, frontages, and others. The PDA Suite reports weekday hourly averages by facility type and direction. Average weekday values are reported by facility type and direction, within the following time periods:

• Morning (AM): 6:00 AM–9:59 AM;
• Midday (MD): 10:00 AM–2:59 PM;
• Evening (PM): 3:00 PM–6:59 PM;
• Nighttime (NT): 7:00 PM–5:59 AM; and
• Daily: 12:00 AM–11:59 PM.

Although INRIX data collection precedes years reported in Tracking Progress, early years of reporting are highly variable based on a lack of facility coverage. The years from 2011 onward show higher stability for highway facilities for most counties and for the region. For local facilities, 2014 and beyond is where values seem most stable due to more widespread facility coverage.

Historic data for the federal Transportation Performance Management (TPM) system performance reporting requirements is shown. These are Level of Travel Time Reliability (LOTTR), Level of Truck Travel Time Reliability (TTTR), and Annual Hours of Peak-Hour Excessive Delay (AHPHED). The entire states of Pennsylvania and New Jersey are included for LOTTR and TTTR, so the region’s figures can be compared with statewide data.

LOTTR is used to calculate the percentage of roadway segments that are considered reliable. A road segment with an LOTTR of less than 1.5 is considered reliable. Reliable segment lengths in miles are multiplied by their Annual average daily traffic (AADT) values times the average number of people in a vehicle. Then, this sum is then divided by the exact same product for all road segments, to get the resulting percentage of roadway that is reliable for the geography.

TTTR measures how consistent travel times are for trucks on interstates. This can be helpful with analyzing goods movement along the region’s interstates. TTTR is calculated by dividing the 95th percentile of travel times by the 50th percentile of travel times, using the highest value over the Morning (AM), Midday (MD), Evening (PM), Nighttime (NT), and weekend. Each interstate segment multiplies its length by the travel time ratio, the results are summed and then divided by total Interstate length in the geography to determine the area’s TTTR value.

AHPHED is the average number of hours per year spent by motorists driving in congestion during peak periods. This can be useful for analyzing the impact of congestion from an individual’s perspective, since it analyzes how many hours the average person spends stuck in congestion. The figures used are based on the 2010 urbanized area boundaries in the Census. In 2020, they were renamed to urban areas. There are only Mercer County PHED values from 2021 onward, because they only apply to the second four-year TPM performance period, when the Trenton, NJ Urban Area was required to track metrics and set performance targets. AHPHED per capita is that figure divided by the urban area’s population during that year.

Congestion is susceptible to external forces like the economy. A downturn can reduce congestion, but this reflects fewer and shorter trips for households and businesses during lean times and may not represent an improvement. Therefore, it may be useful to correlate these results with the Miles Driven indicator.

Abstract copyright UK Data Service and data collection copyright owner.The National Travel Survey (NTS) is a series of household surveys designed to provide regular, up-to-date data on personal travel and monitor changes in travel behaviour over time. The first NTS was commissioned by the Ministry of Transport in 1965. Further periodic surveys were carried out in 1972/73, 1975/76, 1978/79 and 1985/86 (the UK Data Service holds End User Licence data from 1972 onwards and Special Licence and Secure Access data from 2002). Since July 1988 the NTS has been carried out as a continuous survey with field work being carried out in every month of the year, and an annual set sample of over 5,000 addresses. From 2002, the NTS sample was increased approximately threefold, to approximately 15,000 per year. The advantage of the continuous study is that users will be able to discern seasonal and cyclical movements as well as trend changes over time. The NTS is carried out primarily for the purposes of government. The most fundamental use of the National Travel Survey within the Department for Transport (DfT) is as core base data for key transport models. These are critical to the assessment and appraisal of transport scheme proposals (national and local), transport policy proposals, and contribute to the development of our long-term strategy. The NTS data is used to develop consistent sets of transport policies. Because it relates travel to travellers, it makes it possible to relate policies to people and to predict their impact. The survey provides detailed information on different types of travel: where people travel from and to, distance, purpose and mode. The NTS records personal and socio-economic information to distinguish between different types of people, and the differences in the way they travel and how often they do so. The NTS is the only source of national information on subjects such as walking which provide a context for the results of more local studies. Further information may be found on the gov.uk National Travel Survey web page. End-User Licence, Special Licence and Secure Access NTS data The UK Data Archive also holds End User Licence (EUL) NTS data from 1972 onwards (see SNs 2852, 2853, 2855, 3288, 4108, 4583-4585, 6108 and 5340) and Special Licence (SL) NTS data from 1995 onwards (SNs 7804 and 7553). The EUL versions contain a comprehensive range of NTS data at Government Office Region geographic level and should be sufficient for most research needs. The SL versions contain more detailed travel (including accidents), demographic and socio-economic data, and the geographic level is Local Authority/Unitary Authority. These data are subject to more restricted access conditions than EUL. The Secure Access version contains more detailed information and postcode sector geographies. Secure Access data are subject to further restricted access conditions, including the completion of a training course. For full details of the differences between the EUL, SL and Secure Access NTS, see the document '7559_nts_table_structures.xls' in the documentation. Users should always check whether the EUL and SL versions are suitable for their research needs before considering making an application for the Secure Access version. 2020 and 2021 Disclaimer: Due to changes in the methodology of data collection, changes in travel behaviour and a reduction of data collected during 2020 and 2021, as a result of the coronavirus (COVID-19) pandemic, care should be taken when interpreting this data and comparing to other years, due to the small sample sizes. Please see the background documentation for further details of these changes.Latest edition information:For the eleventh edition (September 2024), data and documentation for 2023 have been added to the study.Data labelsUsers should note that the SPSS and Stata files for 2023 have been converted from CSV format and do not currently contain variable or value labels. Complete metadata information can be found in the Excel Lookup table files and the NTS Data Extract User Guide within the documentation. Main Topics: The 2002-2022 NTS includes: attitudinal variables: in 2016 a split-sample experiment was conducted to explore the feasibility of moving attitudinal questions from the household level questionnaire to the individual level questionnaire. From 2017, a CASI module for transport satisfaction questions was added, where one adult from those present during the household interview is randomly selected to complete the satisfaction questions;household variables: address type information, accessibility of public transport, access to amenities, household vehicle access, household composition and household socio-economic information;individual information: age, gender and marital status, social and economic information, frequency of use of various methods of transport, driving licences and type of vehicle driven, employment, occupation and industry details, income, place of work and travel to work, season ticket details, travel difficulties;vehicle information: vehicle type, registration details, parking, fuel type, mileage, engine capacity;trips: day, date and time, main mode, purpose, origin and destination information;stage: mode, number in party, distance, duration, costs;long-distance trips (over 50 miles): stage: mode, purpose, origin and destination Please see the Lookup Tables All Variables by Level and Licence document available from the Documentation tab for the full list of variables. Multi-stage stratified random sample Telephone interview Face-to-face interview Diaries 2002 2023 ACCESS TO FACILITIES ACCESS TO HEALTH SE... AGE AIR TRANSPORT ATTITUDES BICYCLES BUSES BUSINESS TRIPS CAR ENGINES CAR PARKING AREAS CAR SHARING CARBON DIOXIDE EMIS... CARS COMMUTING COMPANY CARS COMPUTERS COSTS CYCLE LANES DELIVERY SERVICES DIARIES DISABLED PERSONS DISTANCE MEASUREMENT DRIVING DRIVING LICENCES DRIVING TESTS ECONOMIC ACTIVITY EMPLOYEES EMPLOYMENT HISTORY ETHNIC GROUPS EXPENDITURE FOSSIL FUELS FULL TIME EMPLOYMENT GENDER GENERAL PRACTITIONERS HEADS OF HOUSEHOLD HIGHER EDUCATION IN... HOME BASED WORK HOSPITAL SERVICES HOUSEHOLD HEAD S EC... HOUSEHOLD INCOME HOUSEHOLDS HOUSING HOUSING TENURE ILL HEALTH INCOME INDUSTRIES INTERNET LEISURE TIME LOCAL COMMUNITY FAC... MARITAL STATUS MEDICAL CENTRES MOBILITY SCOOTERS MOTOR VEHICLE HIRE MOTOR VEHICLES MOTORCYCLES OCCUPATIONAL STATUS OCCUPATIONS ONLINE SHOPPING PART TIME EMPLOYMENT PEDESTRIAN FACILITIES PETROL PETROL CONSUMPTION PHARMACIES PHYSICAL DISABILITIES PHYSICAL MOBILITY PLACE OF BIRTH POST OFFICES PUBLIC TRANSPORT QUALIFICATIONS RAILWAY STATIONS RAILWAY TRANSPORT RESIDENTIAL MOBILITY ROAD ACCIDENTS ROAD VEHICLE MAINTE... SATELLITE NAVIGATIO... SATISFACTION SCHOOLS SELF EMPLOYED SHOPPING SHOPS SOCIAL ACTIVITIES L... SOCIAL DISADVANTAGE SOCIO ECONOMIC STATUS STATUS IN EMPLOYMENT STUDENT TRANSPORTATION SUPERVISORY STATUS TRAINS TRANSPORT TRANSPORT FARES TRANSPORT INFRASTRU... TRANSPORT SAFETY TRAVEL TRAVEL CONCESSIONS TRAVEL PASSES TRAVELLING TIME TRUCKS Transport and travel UNDERGROUND RAILWAYS UNEMPLOYED VEHICLES VELOCITY WALKING WALKING AIDS WORKPLACE

Abstract copyright UK Data Service and data collection copyright owner.The International Passenger Survey (IPS) aims to collect data on both credits and debits for the travel account of the Balance of Payments, provide detailed visit information on overseas visitors to the United Kingdom (UK) for tourism policy, and collect data on international migration. The data cover four subject areas, termed 'Airmiles', 'Alcohol', 'Qregtown' and 'Qcontact'. One file is produced each quarter per subject area, and the dataset updated quarterly. These files can be joined together using the variables YEAR, SERIAL, FLOW and QUARTER. The depositor recommends that only expert users who are very familiar with the coding and weighting structures use these data, as limited support is available. Some considerable understanding of the data is required before meaningful analyses can be made; care must be taken when performing time series operations as codes can vary from year to year and not all variables from one year's dataset are used in other years. Latest edition informationFor the ninth edition (September 2022), a revised Qcontcust 2009-2019 data file was added to the study, with additional category labels added. The data file includes all years from 2009-2019 and users will need to filter the data by year to see cases for individual years. Main Topics: The 2011 IPS dataset covers:'Airmiles': quarter; flow; serial; UK port or route; direct leg overseas port; final overseas port; distance from UK port to first port; from first to second port; from UK port to second port'Alcohol': year; quarter; month; flow; serial; money spent on spirits; wine; beer; champagne; cigarettes; hand-rolled and other tobacco'Qreg' (covers 2009-2019): year; quarter; month; flow; serial; towns stayed in overnight; details of type of accommodation; number of nights spent in towns; expenditure in towns; regional stay weight; regional visit weight; regional expenditure weight; various validation checks'Qcontact': year; quarter; month; flow; serial; nationality; country of visit/residence; UK counties; date visit began; purpose of visit; intended length of stay; number of people; package tour and cost; expenditure pre-, post- and during visit; flight prefix and suffix; first carrier air or shipping line; direct leg overseas port; final overseas port; long- or short-haul; type of vehicle; number travelling in vehicle; fare type and cost; class of travel; business trip; type of flight; flight origin or destination; gender; age group; UK port or route; quality of response; date of interview; money transfer, net and total expenditure; type of transport; arrivals (number of adults); departures (type of travelling group, number of adults and children); weighting variables; various validation checks Multi-stage stratified random sample Face-to-face interview

In 2023, the number of passengers carried by Air Canada amounted to approximately 44.8 million passengers. Air Canada Air Canada is the national carrier of Canada, being founded by the Canadian government in 1937. The airline was fully privatized in 1989 and in 2001 acquired Canada’s then second largest airline – Canadian Airlines – to become the dominant airline in the Canadian market. The company experienced financial difficulties throughout the 2000’s and into 2010's but has consolidated their financial position in last ten years, recording strong revenue growth in 2018 and 2019, before affected severely by the coronavirus pandemic .Amid the global pandemic, the company's operating revenue decreased dramatically from 19 billion to around six billion Canadian dollars in 2020. As of 2022, the total operating revenue of Air Canada was 13.6 billion Canadian dollars. This was considerably higher than the previous year but still lower than the pre-pandemic year. Air travel in Canada The two main airlines in the Canadian market are Air Canada and low-cost carrier WestJet, who held domestic market shares of 46 percent and 34 percent respectively in 2018. In terms of passenger numbers, WestJet historically carries around half as many as Air Canada. This discrepancy can be explained by the far greater range of international destinations offered by Air Canada. However, the number of customer complaints received about Air Canada is disproportionately, coming in at 1,997 in 2019 – compared to 369 for WestJet.

Context

You are a Data Scientist for a tourism company named "Lets Travel". The Policy Maker of the company wants to enable and establish a viable business model to expand the customer base. A viable business model is a central concept that helps you to understand the existing ways of doing the business and how to change the ways for the benefit of the tourism sector. One of the ways to expand the customer base is to introduce a new offering of packages. Currently, there are 5 types of packages the company is offering - Basic, Standard, Deluxe, Super Deluxe, King. Looking at the data of the last year, we observed that 18% of the customers purchased the packages. However, the marketing cost was quite high because customers were contacted at random without looking at the available information. The company is now planning to launch a new product i.e. Wellness Tourism Package. Wellness Tourism is defined as Travel that allows the traveler to maintain, enhance or kick-start a healthy lifestyle, and support or increase one's sense of well-being. However, this time company wants to harness the available data of existing and potential customers to make the marketing expenditure more efficient. You as a Data Scientist at "Visit with us" travel company have to analyze the customers' data and information to provide recommendations to the Policy Maker and Marketing Team and also build a model to predict the potential customer who is going to purchase the newly introduced travel package.

Objective

To predict which customer is more likely to purchase the newly introduced travel package

About the Data

Customer details: 1. CustomerID: Unique customer ID 2. ProdTaken: Whether the customer has purchased a package or not (0: No, 1: Yes) 3. Age: Age of customer 4. TypeofContact: How customer was contacted (Company Invited or Self Inquiry) 5. CityTier: City tier depends on the development of a city, population, facilities, and living standards. The categories are ordered i.e. Tier 1 > Tier 2 > Tier 3 6. Occupation: Occupation of customer 7. Gender: Gender of customer 8. NumberOfPersonVisiting: Total number of persons planning to take the trip with the customer 9. PreferredPropertyStar: Preferred hotel property rating by customer 10. MaritalStatus: Marital status of customer 11. NumberOfTrips: Average number of trips in a year by customer 12. Passport: The customer has a passport or not (0: No, 1: Yes) 13. OwnCar: Whether the customers own a car or not (0: No, 1: Yes) 14. NumberOfChildrenVisiting: Total number of children with age less than 5 planning to take the trip with the customer 15. Designation: Designation of the customer in the current organization 16. MonthlyIncome: Gross monthly income of the customer

Customer interaction data: 1. PitchSatisfactionScore: Sales pitch satisfaction score 2. ProductPitched: Product pitched by the salesperson 3. NumberOfFollowups: Total number of follow-ups has been done by the salesperson after the sales pitch 4. DurationOfPitch: Duration of the pitch by a salesperson to the customer

Abstract copyright UK Data Service and data collection copyright owner.The National Travel Survey (NTS) is a series of household surveys designed to provide regular, up-to-date data on personal travel and monitor changes in travel behaviour over time. The first NTS was commissioned by the Ministry of Transport in 1965. Further periodic surveys were carried out in 1972/73, 1975/76, 1978/79 and 1985/86 (the UK Data Service holds End-User Licence data from 1972 onwards and Special Licence and Secure Access data from 2002 onwards). Since July 1988 the NTS has been carried out as a continuous survey with field work being carried out in every month of the year, and an annual set sample of over 5,000 addresses. From 2002, the NTS sample was increased approximately threefold, to approximately 15,000 per year. The advantage of the continuous study is that users will be able to discern seasonal and cyclical movements as well as trend changes over time. The NTS is carried out primarily for the purposes of government. The most fundamental use of the National Travel Survey within the Department for Transport (DfT) is as core base data for key transport models. These are critical to the assessment and appraisal of transport scheme proposals (national and local), transport policy proposals, and contribute to the development of our long-term strategy. The NTS data is used to develop consistent sets of transport policies. Because it relates travel to travellers, it makes it possible to relate policies to people and to predict their impact. The survey provides detailed information on different types of travel: where people travel from and to, distance, purpose and mode. The NTS records personal and socio-economic information to distinguish between different types of people, and the differences in the way they travel and how often they do so. The NTS is the only source of national information on subjects such as walking which provide a context for the results of more local studies. Further information may be found on the gov.uk National Travel Survey webpage. End-User Licence, Special Licence and Secure Access NTS data The UK Data Archive holds three versions of the NTS:the End User Licence (EUL) versions (SNs 5340 and 6108) contain a comprehensive range of NTS data at Government Office Region geographic level and should be sufficient for most research needs. EUL data are available to registered users of the UK Data Service (see the Administrative and Access section below for details).The Special Licence versions (SNs 7553 and 7804) contain more detailed travel (including accidents), demographic and socio-economic data, and the geographic level is Local Authority/Unitary Authority. Special Licence data are subject to more restricted access conditions.The Secure Access version (SN 7559) contains more detailed information and postcode sector geographies. Secure Access data are subject to further restricted access conditions, including the completion of a training course.Full information about the variables contained at each level are available in the NTS Table Structures spreadsheet, available in the documentation. 2020 and 2021 Disclaimer: Due to changes in the methodology of data collection, changes in travel behaviour and a reduction of data collected during 2020 and 2021, as a result of the coronavirus (COVID-19) pandemic, care should be taken when interpreting this data and comparing to other years, due to the small sample sizes. Please see the background documentation for further details of these changes.Latest edition information:For the thirteenth edition (September 2024), data and documentation for 2023 have been added to the study.Data labelsUsers should note that the SPSS and Stata files for 2023 have been converted from CSV format and do not currently contain variable or value labels. Complete metadata information can be found in the Excel Lookup table files and the NTS Data Extract User Guide within the documentation. Main Topics: The NTS data includes: attitudinal variables: in 2016 a split-sample experiment was conducted to explore the feasibility of moving attitudinal questions from the household level questionnaire to the individual level questionnaire. In one half of the sample, the attitudinal questions were asked as part of the household questionnaire (as has been in the case in previous years) and in the other half one randomly selected adult per household was asked the attitudinal questions;household variables: address type information, accessibility of public transport, access to amenities, household vehicle access, household composition and household socio-economic information;individual information: age, gender and marital status, social and economic information, frequency of use of various methods of transport, driving licences and type of vehicle driven, employment, occupation and industry details, income, place of work and travel to work, season ticket details, travel difficulties;vehicle information: vehicle type, registration details, parking, fuel type, mileage, engine capacity;trips: day, date and time, main mode, purpose, origin and destination information;stage: mode, number in party, distance, duration, costs;long-distance trips (over 50 miles): stage: mode, purpose, origin and destination; Please see the Lookup Tables documentation for the full list of variables. Multi-stage stratified random sample Telephone interview Face-to-face interview Diaries

This table contains information regarding the mobility of the residents of the Netherlands aged 6 or older in private households, so excluding residents of institutions and homes. The table contains per person per day /year an overview of the average number of trips, the average distance travelled and the average time travelled. These are regular trips on Dutch territory, including domestic holiday mobility. The distance travelled is based on stage information. Excluded in this table is mobility based on series of calls trips. The mobility behaviour is broken down by personal characteristics, purposes of travel, gender, population and regions. The data used are retrieved from The Dutch National travel survey named Onderweg in Nederland (ODiN). As of thf 5 July 2023 in the tables of 2018-2020 in some places dots (figure is unknown, insufficiently reliable or confidential) are replaced by empty cells (figure not applicable) Data available from: 2018 Status of the figures: The figures in this table are final. Changes as of 4 July 2024: The figures for year 2023 are added. Starting with the publication of 2023 data, the figures on people’s background will no longer be available. The data in the table has been replaced by dots. The data on background from previous years are still available. When will new figures be published? Figures for the 2024 research year will be published in mid-2025

Private Jets Rental Market Outlook

The global private jets rental market size was valued at approximately USD 25 billion in 2023, and it is projected to reach around USD 40 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.1% during the forecast period. The market is being driven by a combination of increasing wealth among high-net-worth individuals (HNWIs), the growing demand for enhanced in-flight experiences, and the need for time-efficient travel.

One of the key growth factors for the private jets rental market is the rising number of HNWIs and ultra-high-net-worth individuals (UHNWIs) globally. As disposable incomes increase, more individuals are opting for the convenience and luxury that private jets offer. Additionally, the COVID-19 pandemic has significantly boosted the demand for private air travel due to safety and hygiene concerns associated with commercial flights, further propelling market growth. The flexibility in travel schedules and the ability to access remote locations not serviced by commercial airlines are also crucial factors driving the market.

Corporations are increasingly recognizing the value of private jets for business travel. The ability to conduct meetings in the air, save time, and ensure the confidentiality of business discussions are significant advantages prompting companies to invest in private jet rentals. Additionally, the rise in globalization and international business dealings necessitates swift and reliable travel options, further accelerating the demand in this segment. Moreover, advancements in aviation technology, such as the development of more fuel-efficient jets and enhanced cabin features, are making private jet travel more appealing and accessible.

The emergence of various service models, such as on-demand charters, jet card programs, and fractional ownership, has also played a pivotal role in market growth. These models provide flexibility and cost-effectiveness, catering to different customer preferences and needs. For instance, on-demand charters offer the convenience of booking a jet as per requirement, while jet card programs provide a more structured and cost-effective approach for frequent flyers. Fractional ownership, on the other hand, offers a more permanent solution by allowing individuals or businesses to own a share of a jet, thus reducing overall costs while enjoying the benefits of private jet travel.

Private Jet Charter Services have become an integral part of the private jets rental market, offering a bespoke travel experience tailored to the unique needs of each client. These services provide unparalleled flexibility, allowing travelers to choose their aircraft, schedule, and destination with ease. As the demand for personalized travel solutions continues to grow, private jet charter services are increasingly being sought after by both individuals and corporations. The ability to bypass the complexities of commercial air travel, coupled with the assurance of privacy and luxury, makes charter services an attractive option for those seeking a seamless travel experience. Furthermore, advancements in digital platforms have made booking charter services more accessible, enhancing the overall customer experience and driving market growth.

Regionally, North America holds the largest share of the private jets rental market, driven by the presence of a significant number of HNWIs, UHNWIs, and a high concentration of corporate headquarters. Europe follows closely, with growing demand observed in countries like the UK, Germany, and France. The Asia Pacific region is emerging as a lucrative market, primarily due to the increasing number of affluent individuals and the expanding corporate sector in countries like China, India, and Japan. The Middle East & Africa and Latin America are also witnessing a gradual increase in demand, attributed to rising economic growth and increased international business engagements.

Aircraft Type Analysis

The private jets rental market can be segmented by aircraft type into light jets, mid-size jets, heavy jets, and others. Light jets are particularly popular among individuals and small corporate groups due to their cost-effectiveness and ability to access smaller airports. These jets typically offer seating for four to eight passengers and are ideal for short to medium-haul flights. The demand for li