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.

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.

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.

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|

Passengers enplaned and deplaned at Canadian airports, annual.

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.

Description

This dataset reflects the Air Traffic in Europe from 2016 to 2024.

Acknowledgements

https://ansperformance.eu/

Photo by Hanson Lu on Unsplash

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).

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

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.

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.

Conducted by the Federal Highway Administration, the NHTS is the authoritative source on the travel behavior of the American public. It is the only source of national data that allows one to analyze trends in personal and household travel. It includes daily non-commercial travel by all modes, including characteristics of the people traveling, their household, and their vehicles. The NHTS dataset contains data for all 150,147 completed households in the sample includinghousehold, person, vehicle and daily (travel day) trip level data.This is in part of the same series of surveys that were previously called the Nationwide Personal Transportation Study, but has been renamed into the National Household Travel Survey.

In this dataset, we collected the average prices of all flight departures in Spain projected in December by Ryanair. Although we are uploading only raw data, this dataset can be useful to study the connectivity of Spain, and its accessibility by the people (comparing frequencies and prices in different airports).

We searched for the December period to focus on the relative fluctuations of prices in holidays versus the rest of the month. Also, the prices may change between holidays lengths in days, and the day of departure. Specifically, we searched for 2 to 4 days of vacation, and departures from Thursday, Friday and Saturday. We only searched for the average prices of two adults because Ryanair prices are averages per person.

All in all, this dataset has 11 attributes and 9794 rows.

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.

It is also important to measure PTIs along the roads buses travel, to measure how reliable the roads are that commuters travel on. To calculate the agency and division type combination PTIs, for each route, all their segments’ planning times from 7-8 AM, 8-9 AM, 4-5 PM, and 5-6 PM are first summed. Then, those are divided by the sums of those segments' free-flow travel times for those same time periods, to get one PTI per time period for each route. Then, the highest of those four PTIs is taken to get one maximum peak hour PTI per route. Then, for each agency and division type combination, all of their routes’ maximum peak hour PTIs are averaged for each year to get the PTIs. Since all NJ Transit routes in the DVRPC region are part of their Southern Division, NJ Transit only has one agency and division mode combination. SEPTA has two: “City” and “Suburban”. SEPTA splits their bus routes into their urban routes, all within their City Transit Division, and their suburban routes, which are in their Victory and Frontier divisions. The Victory and Frontier divisions have been grouped into their own “Suburban” division type.

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.

This dataset is the 2017 Puget Sound Regional Travel Survey (PSRC household survey), which collected detailed trip information and sociodemographic characteristics from 6,254 individuals in 3,285 households living in the central Puget Sound (Seattle, WA) region (Puget Sound Regional Council, 2018). In 2017, the central Puget Sound region had a population of 4,063,700 with a density of 591 people per square mile, which is comparable to other metropolitan areas, such as Boston or Houston (American Community Survey 2017). Covering 0.20 percent of households and 0.16 percent of the population in the region, the survey used a geographically proportional sampling plan based on the household distribution in each census block group, and oversampled population segments that are traditionally difficult to reach, such as transit users and pedestrians (Puget Sound Regional Council, 2018). Data on 2,580 households and 5,019 individuals with 17,468 trips were collected via a one-to-four-day travel diary filled in by the participant via telephone or a web-based interface, conducted from Tuesday through Thursday; and data on 705 households and 1,235 individuals with 35,024 trips were collected via a new app-based seven-day travel diary, conducted from Monday through Sunday. The travel diary includes participant identification (person ID), identification of trip origin and trip destination at the scale of the census block group (trip ID), trip start/end time, and trip duration of each trip.

Each year, the Greater Manchester Travel Diary Survey (TRADS) collects detailed transport and travel information from every member (aged five or older) of 2,000 Greater Manchester households. Respondents provide details about all the trips they make in a 24-hour period. TRADS focuses on the specifics of the trips and the characteristics of the people making them, rather than attitudes to, and satisfaction with, travel. The survey sample is designed to be representative of each Greater Manchester (GM) district based on resident demographics. The survey runs throughout the year, from the beginning of February to the end of the following January. The only days when surveys aren’t conducted are Christmas Day and any days following a bank holiday. The data collected from 2,000 GM households equates to more than 4,500 residents and around 10,000 trips. The key information captured by the survey includes trip origins and destinations, travel times, travel methods, and journey purposes. Surveying is carried out face-to-face by experienced interviewers. The response rate was 58% for both 2017-19 and 2024. The survey’s annual sample - a random probability sample stratified by district - provides confidence intervals of +/- 1% to 2% at the GM household level, and +/- 7% to 8% at the district household level. Before the pandemic, trip estimates were based on data collected over three years, providing confidence intervals of +/- 1% at the GM household level, and +/- 3% to 4% at the district household level. However, since 2020, travel habits have been too unstable for this approach, so estimates from 2021 onwards are based on single-year data. The survey data is weighted/expanded to the GM population based on each district’s population by age, gender, and Acorn Category. The weights are small, with high weighting efficiency. Between 2019 and 2022, the weighting methodology was updated to better account for population growth. In 2019, data was expanded to the Census 2011 population levels, while 2024 data is expanded to the 2023 mid-year population estimates. This change has most notably impacted districts with significant population growth, such as Manchester and Salford, where the estimated number of trips has increased despite a decrease in the average trip rate per person. In 2023, the questionnaire was updated to include new travel modes (eg distinguishing between electric and combustion engine car drivers) and new demographic questions (eg sexual orientation, gender identity). These updates remain in place for 2024. Changes were also made in 2023 to better capture commute and business trips, reflecting the working habits of GM residents, resulting in more commute trips and fewer business trips being recorded. For the 2024 survey, business trips and commute trips were combined into one 'Business and Commuting' category, due to very few business trips being recorded overall. The report includes data estimates for 2019 and 2024. While overall estimates at the district household level have confidence intervals of +/- 7% to 8%, caution is advised when interpreting sub-group estimates (eg commute trips, short trips, age, hour, and purpose) due to larger confidence intervals. Before the pandemic, TRADS estimates closely aligned with key variables and other data sources (eg census data, ticket sales, Google Environment Insight Explorer). And generally, TRADS trip estimates show remarkable year-on-year stability, even for smaller modes and journey purposes. For example, the number of taxi trips has consistently been around 100,000 daily since 2017. Note: totals in tables may not sum precisely due to rounding to the nearest 1,000. If you would like more details of the surveying methodology, our technical notes can be made available on request. For more information about TRADS or for further analysis, please contact insight@tfgm.com.