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.

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) |

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.

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.

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|

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.

Passengers enplaned and deplaned at Canadian airports, annual.

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

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.

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.

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

This dataset was used in my dissertation project to find the best time to buy the airline ticket based on number of days before departure date which was inspired by the work of Domínguez-Menchero et al (2014) '*Optimal purchase timing in the airline market*'.

The dataset was scraping using Selenium and BeatifulSoup python package. It contains direct flight data of flights from London to Bangkok, Hong Kong, Tokyo, Seoul, and Singapore. The data were gathered 30 days and 66 days before the departure date consisting of route, airline name, direct flight type, departure date, departure date (format), search date, days before departure, ticket price, price on departure date, saving rate, and day of week.

To provide a detailed description of your dataset, let's go over each feature based on your dataset structure and the columns in the file. I'll also explain potential meanings for each column and what could be inferred from them.

Columns Description (Assuming from your Dataset)

Based on the columns mentioned in your dataset (Tamil_nadu_taxi_trips_cleaned.csv), here's a detailed description of each:

1. Date_Time:

   • Description: The timestamp representing the exact date and time of the taxi trip's start. It can be broken down into hour, day, month, and year to perform time-based analysis like peak travel hours or trends across months.
   • Type: DateTime
   • Potential Analysis: You could analyze trends based on time, such as determining peak traffic hours, fare variation by time of day, or the busiest travel days of the week.

2. Pickup_Location:

   • Description: This represents the geographical or categorical location where the taxi trip begins. The value could be a specific location name, zone, or area code.
   • Type: Categorical (encoded later into numerical form)
   • Potential Analysis: You can analyze the distribution of trips across different locations, identify popular pickup spots, or perform clustering on locations to find patterns.

3. Drop_Location:

   • Description: The destination location where the taxi trip ends. Like Pickup_Location, this could be represented as a location name or area code.
   • Type: Categorical (encoded later into numerical form)
   • Potential Analysis: This can be used for analyzing the most common destinations, calculating distances between pickup and drop locations, and evaluating demand for rides to certain areas.

4. Distance_km:

   • Description: The distance traveled during the trip in kilometers.
   • Type: Numeric
   • Potential Analysis: This feature is directly related to the fare prediction, as longer distances tend to result in higher fares. You can also analyze average trip distances, or correlate distances with time spent in traffic.

5. Fare_INR:

   • Description: The fare charged for the trip, represented in Indian Rupees (INR).
   • Type: Numeric
   • Potential Analysis: This is a key feature for fare prediction models. You could also analyze average fares, identify outliers (like unusually high or low fares), or see how fare correlates with other features such as distance, time of day, and number of passengers.

6. No_of_Passengers:

   • Description: The number of passengers on the trip.
   • Type: Numeric (integer)
   • Potential Analysis: You can analyze the frequency of trips with different numbers of passengers, check if the number of passengers impacts the fare, or evaluate how many shared rides or group trips occur.

7. Travel_Time_hrs:

   • Description: The duration of the taxi trip in hours.
   • Type: Numeric
   • Potential Analysis: This is an important feature for analyzing traffic conditions and travel efficiency. You can evaluate if longer travel times correlate with higher fares and whether travel time increases during rush hours.

8. Tips_INR:

   • Description: The amount of tip given by the passenger in INR.
   • Type: Numeric
   • Potential Analysis: You can analyze tipping patterns, see if there's a relationship between distance, fare, and tips, or identify passengers' tipping behavior based on time of day or specific locations.

9. Tourist_Place_Nearby:

   • Description: Indicates whether the pickup or drop location is near a tourist attraction.
   • Type: Categorical (likely a binary indicator, i.e., yes/no)
   • Potential Analysis: This feature could be used to analyze the impact of tourist locations on fare prices, distance, and passenger frequency. You can also identify if tourists are more likely to tip.

10. Weather_Condition:

    • Description: Represents the weather conditions during the trip (e.g., sunny, rainy, cloudy, etc.).
    • Type: Categorical (encoded later into numerical form)
    • Potential Analysis: Weather conditions may impact both travel times and fare amounts. For example, rainy weather could lead to longer travel times, affecting fare amounts.

11. Vehicle_Type:

    • Description: Specifies the type of vehicle used for the taxi trip (e.g., sedan, SUV, auto-rickshaw, etc.).
    • Type: Categorical (encoded later into numerical form)
    • Potential Analysis: Different vehicle types may result in varying fare structures. You can analyze how different vehicle types affect fare, travel time, and tipping behavior.

Steps for Dataset Analysis

1. Handling Missing Data:
   • As seen earlier, several columns had missing values (Date_Time, Pickup_Location, Drop_Location, Distance_km, etc.). Filling these appropriate...

China Air: Passenger Traffic: Domestic data was reported at 664.657 Person mn in 2024. This records an increase from the previous number of 590.516 Person mn for 2023. China Air: Passenger Traffic: Domestic data is updated yearly, averaging 95.618 Person mn from Dec 1970 (Median) to 2024, with 42 observations. The data reached an all-time high of 664.657 Person mn in 2024 and a record low of 0.210 Person mn in 1970. China Air: Passenger Traffic: Domestic data remains active status in CEIC and is reported by Civil Aviation Administration of China. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TI: Air: Passenger Traffic.

EUROCONTROL is a pan-European, civil-military organisation dedicated to supporting European aviation.

Its Aviation Intelligence & Performance Review Unit provides independent collection and validation of air navigation services performance-related data and intelligence gathering. These flights data include international arrivals and departures, and domestic flights, but exclude overflights.

The Victorian Integrated Survey of Travel and Activity is a major data collection exercise conducted by DTP to understand all aspects of day-to-day travel by Victorians. VISTA is a rich dataset that investigates travel and activities people undertake as they go about their lives and informs policy, project and planning decisions made across the Victorian transport portfolio. Since 2012, more than 32,000 households have contributed to the ongoing survey. Data collection is spread evenly across each year, allowing average daily travel behaviours to be described. See the VISTA website for more details about this survey. Detailed findings are available via an interactive data visualisation tool that allows users to explore the data further.

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).