This dataset provides a comprehensive overview of domestic airline routes within the United States. It includes valuable information for analyzing passenger travel patterns, market trends, and airline pricing strategies.

Data Features:

• Year
• Quarter
• City Market IDs
• Departure City
• Arrival City:
• Miles: The distance between the origin and arrival cities in miles.
• Average Daily Passengers: The average number of passengers flying this route per day.
• Average Fare: The average fare paid by passengers for this route (consider including currency information).

Potential Uses:

• Travel Demand Analysis: Identify popular routes, and understand seasonal variations in passenger traffic.
• Market Research: Analyze airline competition on specific routes and assess pricing strategies.
• Route Optimization: Airlines can use this data to evaluate existing routes and identify potential new routes with high passenger demand.
• Business Intelligence: Businesses can use this data to understand travel patterns relevant to their industry and make informed decisions.

Data Cleaning and Transformation Considerations:

• Ensure consistency in city names (consider using the city market ID to group nearby airports).
• Handle missing values appropriately.
• Consider converting categorical features to numerical representations for analysis.

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.

The number of flights performed globally by the airline industry has increased steadily since the early 2000s and reached **** million in 2019. However, due to the coronavirus pandemic, the number of flights dropped to **** million in 2020. The flight volume increased again in the following years and was forecasted to reach ** million in 2025.

Airlines Traffic Passenger Statistics

A New Look at an Old Problem

About this dataset

This dataset contains information on air traffic passenger statistics by the airline. It includes information on the airlines, airports, and regions that the flights departed from and arrived at. It also includes information on the type of activity, price category, terminal, boarding area, and number of passengers

How to use the dataset

Air traffic passenger statistics can be a useful tool for understanding the airline industry and for making travel plans. This dataset from Open Flights contains information on air traffic passenger statistics by airline for 2017. The data includes the number of passengers, the operating airline, the published airline, the geographic region, the activity type code, the price category code, the terminal, the boarding area, and the year and month of the flight

Research Ideas

• Air traffic passenger statistics could be used to predict future trends in air travel.
• The data could be used to generate heat maps of airline traffic patterns.
• The data could be used to study the effects of different factors on air traffic passenger numbers, such as the time of year or day, the price of airfare, or the number of flights offered by an airline

License

License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for non-commercial purposes only. - Adapt - remix, transform, and build upon the material for non-commercial purposes only. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - You may not: - Use the material for commercial purposes.

Columns

File: Air_Traffic_Passenger_Statistics.csv | Column name | Description | |:--------------------------------|:------------------------------------------------------------------------------| | Activity Period | The date of the activity. (Date) | | Operating Airline | The airline that operated the flight. (String) | | Operating Airline IATA Code | The IATA code of the airline that operated the flight. (String) | | Published Airline | The airline that published the fare for the flight. (String) | | Published Airline IATA Code | The IATA code of the airline that published the fare for the flight. (String) | | GEO Summary | A summary of the geographic region. (String) | | GEO Region | The geographic region. (String) | | Activity Type Code | The type of activity. (String) | | Price Category Code | The price category of the fare. (String) | | Terminal | The terminal of the flight. (String) | | Boarding Area | The boarding area of the flight. (String) | | Passenger Count | The number of passengers on the flight. (Integer) | | Adjusted Activity Type Code | The type of activity, adjusted for missing data. (String) | | Adjusted Passenger Count | The number of passengers on the flight, adjusted for missing data. (Integer) | | Year | The year of the activity. (Integer) | | Month | The month of the activity. (Integer) |

In 2023, the estimated number of scheduled passengers boarded by the global airline industry amounted to approximately *** billion people. This represents a significant increase compared to the previous year since the pandemic started and the positive trend was forecast to continue in 2024, with the scheduled passenger volume reaching just below **** billion travelers. Airline passenger traffic The number of scheduled passengers handled by the global airline industry has increased in all but one of the last decade. Scheduled passengers refer to the number of passengers who have booked a flight with a commercial airline. Excluded are passengers on charter flights, whereby an entire plane is booked by a private group. In 2023, the Asia Pacific region had the highest share of airline passenger traffic, accounting for ********* of the global total.

COVID-19(+) Interactions Within Air Travel

Modeling potential interactions between healthy individuals and those carrying COVID-19, denoted hereafter as (+), has been identified as a key methodology in the effort to predict, combat, and respond to COVID-19. In order to contribute to this effort within the domain of airline travel, this dataset allows users to see all flights during the time period from 01MAR-14APR where airline passengers may have come in contact with a COVID-19(+) TSA Screening Agent during their presumed incubation period, 7 days, before that agent went in quarantine.

Acknowledgements

• Federal Data Partner: Transportation Security Administration (TSA).
• Industry Data Partner: Airline Data Inc.

Inspiration

The CORD-19 Research Challenge has been a great inspiration for this effort. Its focus on natural language processing has prompted the need for additional efforts in other statistical machine learning methods, such as those used in the UNCOVER COVID-19 Challenge. With COVID-19 research as a global focal point, I hope that this dataset provides researchers with another set of features to help build models towards finding answers.

Methodology

Airline Data Inc. provided airline schedule information for the time period of 01MAR-14APR. This is one of the data products available as a part of their Data Hub. The airline schedule includes information on future and historical airline flights updated in real-time as it is filed by the airlines. This data provides access to origins and destinations, flight times, aircraft types, seats, customized route mapping, and much more. For this work, we focused on getting flight information to include terminals and carriers in order to determine potential contact of passengers and, at the time, unknowingly COVID-19(+) TSA agents. Airline Data Inc. additionally provided the T100 data from March and April of last year. The T100 provides information on particular routes (ORD->JFK) for U.S. domestic and international air service reported by carriers. This dataset includes passenger counts, available seats, load factors, equipment types, cargo, and other operating statistics. These datasets were combined to estimate the number of passengers flying various routes thought the time period in question. Undoubtedly these numbers are much lower than those of the previous year, but we make the assumption that airline travel declined in a relatively equal proportions across the US, making the load factors for last year comparatively accurate. Since the T100 data is only released on a monthly basis, these figures will not be able to be updated until the coming months.

The Transportation Security Administration posted publicly on their website a list of all Screening and Baggage Officers who tested positive for COVID-19. This list included the airport they worked in, their last day of work, and their work location with shift information. This data was taken and used to down-select the data from Airline Data Inc. to only include those flights that met the following criteria: - Origin airport with COVID-19(+) TSA Officer - Flight took off (the flight schedule data will show all potential flights even those that do not take off) - TSA Officer on shift at time of departure - TSA Officer working in terminal from which the flight departed

This dataset provides monthly totals of US airline passengers from 1949 to 1960. This dataset is taken from a built-in dataset of R called AirPassengers. Analysts often employ various statistical techniques, such as decomposition, smoothing, and forecasting models, to analyze patterns, trends, and seasonal fluctuations within the data. Due to its historical nature and consistent temporal granularity, the Air Passengers dataset serves as a valuable resource for researchers, practitioners, and students in the fields of statistics, econometrics, and transportation planning.

Airline on-time performance Have you ever been stuck in an airport because your flight was delayed or canceled and wondered if you could have predicted it if you'd had more data? This is your chance to find out.

The results We had a total of nine entries, and turn out at the poster session at the JSM was great, with plenty of people stopping by to find out why their flights were delayed.

The data The data consists of flight arrival and departure details for all commercial flights within the USA, from October 1987 to April 2008. This is a large dataset: there are nearly 120 million records in total and takes up 1.6 gigabytes of space when compressed and 12 gigabytes when uncompressed.

The challenge The aim of the data expo is to provide a graphical summary of important features of the data set. This is intentionally vague in order to allow different entries to focus on different aspects of the data, but here are a few ideas to get you started:

When is the best time of day/day of week/time of year to fly to minimise delays? Do older planes suffer more delays? How does the number of people flying between different locations change over time? How well does weather predict plane delays? Can you detect cascading failures as delays in one airport create delays in others? Are there critical links in the system? You are also welcome to work with interesting subsets: you might want to compare flight patterns before and after 9/11, or between the pair of cities that you fly between most often, or all flights to and from a major airport like Chicago (ORD). Smaller subsets may also help you to match up the data to other interesting datasets.

Columns | Name|Description| | --- | --- | |year| 1987-2008| |month| 1-12| |day of month| 1-31| |day of week| 1 (Monday) - 7 (Sunday)| |DepTime| actual departure time (minutes)| |CRSDepTime| scheduled departure time (minutes) |ArrTime| actual arrival time (minutes)| |CRSArrTime| scheduled arrival time (minutes)| |UniqueCarrier| unique carrier code| |FlightNum| flight number| |TailNum| plane tail number| |ActualElapsedTime| in minutes| |CRSElapsedTime| in minutes| |AirTime| in minutes| |ArrDelay| arrival delay, in minutes| |DepDelay| departure delay, in minutes| |Origin| origin IATA airport code| |Dest| destination IATA airport code| |Distance| in miles| |TaxiIn| taxi in time, in minutes| |TaxiOut| taxi out time in minutes| |Cancelled| was the flight cancelled?| |CancellationCode| reason for cancellation (A = carrier, B = weather, C = NAS, D = security)| |Diverted| 1 = yes, 0 = no| |CarrierDelay| in minutes| |WeatherDelay| in minutes| |NASDelay| in minutes| |SecurityDelay| in minutes| |LateAircraftDelay| in minutes|

Updates are delayed due to technical difficulties. How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics. The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day. Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air. The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed. These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

Passengers enplaned and deplaned at Canadian airports, annual.

Introduction

The English Travel Chat Dataset is a comprehensive collection of over 12,000 text-based conversations between customers and call center agents. Focused on real-life travel and tourism interactions, this dataset captures the language, tone, and service dynamics essential for building robust conversational AI, chatbots, and NLP solutions for the travel industry in English-speaking markets.

Participant & Chat Overview

Topic Diversity

The dataset encompasses a wide range of travel and tourism use cases across both customer-initiated and agent-initiated conversations:

This variety ensures wide applicability in both sales enablement and customer support automation.

Language Diversity & Realism

Conversations are crafted to reflect the everyday language and nuances of English-speaking travelers:

These linguistic and cultural cues enable the development of context-aware, natural-sounding AI systems.

Conversational Structure & Flow

The dataset captures a variety of interaction types, including:

Content

Male Flight Attendants

This folder contains the data behind the story Dear Mona, How Many Flight Attendants Are Men?

male-flight-attendants.tsv contains the percentage of U.S. employees that are male in 320 different job categories.

Source: IPUMS, 2012

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

The statistics on daily passenger traffic provides some relevant figures concerning daily statistics on inbound and outbound passenger trips at all control points (with breakdown by Hong Kong Residents, Mainland Visitors and Other Visitors).

Introduction

The Spanish Travel Chat Dataset is a comprehensive collection of over 10,000 text-based conversations between customers and call center agents. Focused on real-life travel and tourism interactions, this dataset captures the language, tone, and service dynamics essential for building robust conversational AI, chatbots, and NLP solutions for the travel industry in Spanish-speaking markets.

Participant & Chat Overview

Topic Diversity

The dataset encompasses a wide range of travel and tourism use cases across both customer-initiated and agent-initiated conversations:

This variety ensures wide applicability in both sales enablement and customer support automation.

Language Diversity & Realism

Conversations are crafted to reflect the everyday language and nuances of Spanish-speaking travelers:

These linguistic and cultural cues enable the development of context-aware, natural-sounding AI systems.

Conversational Structure & Flow

The dataset captures a variety of interaction types, including:

Overview

The Daily Travel data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

Notes

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

Data in the charts and graphs above is updated weekly on Mondays. The data lags one week behind the current date.

Data analysis is conducted at the aggregate national, state, and county levels. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips.

Key Features

1.Level : Indicates National, State, or County level metrics.

2.Date : The date when the data was recorded.

3.State FIPS : A two-digit code representing the FIPS state code.

4.State Postal Code : State postal code.

5.County FIPS : Five-digit FIPS county code.

6.County Name : County name.

7.Population Staying at Home : Number of residents staying at home, i.e., persons who make no trips with a trip end more than one mile away from home.

8.Population Not Staying at Home : Number of residents not staying at home.

9.Number of Trips : Number of trips made by residents, i.e., movements that include a stay of longer than 10 minutes at an anonymized location away from home.

10.Number of Trips <1 : Number of trips by residents shorter than one mile.

11.Number of Trips 1-3 : Number of trips by residents greater than one mile and shorter than 3 miles (1 ≤ trip distance < 3 miles).

12.Number of Trips 3-5 : Number of trips by residents greater than 3 miles and shorter than 5 miles (3 ≤ trip distance < 5 miles).

13.Number of Trips 5-10 : Number of trips by residents greater than 5 miles and shorter than 10 miles (5 ≤ trip distance < 10 miles).

14.Number of Trips 10-25 : Number of trips by residents greater than 10 miles and shorter than 25 miles (10 ≤ trip distance < 25 miles).

15.Number of Trips 25-50 : Number of trips by residents greater than 25 miles and shorter than 50 miles (25 ≤ trip distance < 50 miles).

16.Number of Trips 50-100 : Number of trips by residents greater than 50 miles and shorter than 100 miles (50 ≤ trip distance < 100 miles).

17.Number of Trips 100-250 : Number of trips by residents greater than 100 miles and shorter than 250 miles (100 ≤ trip distance < 250 miles).

18.Number of Trips 250-500 : Number of trips by residents greater than 250 miles and shorter than 500 miles (250 ≤ trip distance < 500 miles).

19.Number of Trips >=500 : Number of trips by residents greater than 500 miles (trip distance ≥ 500 miles).

20.Row ID : Unique row identifier.

21.Week : The week number corresponding to the recorded date.

22.Month : The month number corresponding to the recorded date.

If this was helpful, a vote is appreciated 😄!

Total monthly number of passengers arriving to and departing from Heathrow Airport, including both international and domestic flights.

Have you ever been stuck in an airport because your flight was delayed or cancelled and wondered if you could have predicted it if you'd had more data? This is your chance to find out.

The 2009 ASA Statistical Computing and Graphics Data Expo consisted of flight arrival and departure details for all commercial flights on major carriers within the USA, from October 1987 to April 2008. This is a large dataset containing nearly 120 million records in total.

The aim of the data expo is to provide a graphical summary of important features of the data set. This is intentionally vague in order to allow different entries to focus on different aspects of the data, but here are a few ideas to get you started: •When is the best time of day, day of the week, and time of year to fly to minimise delays? •Do older planes suffer more delays? •How well does weather predict plane delays? •How does the number of people flying between different locations change over time? •Can you detect cascading failures as delays in one airport create delays in others? Are there critical links in the system? •Use the available variables to construct a model that predicts delays.