What is Data Expo 2009 - 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.

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How to use this dataset

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

Acknowledgments

When we use this dataset in our research, we credit the authors.

• License : CC BY 4.0.

• This data set is taken from Harvard Dataset- Data Expo 2009: Airline on time data

The main idea for uploading this dataset is to practice data analysis with my students, as I am working in college and want my student to train our studying ideas in a big dataset, It may be not up to date and I mention the collecting years, but it is a good resource of data to practice

Context

Airline data holds immense importance as it offers insights into the functioning and efficiency of the aviation industry. It provides valuable information about flight routes, schedules, passenger demographics, and preferences, which airlines can leverage to optimize their operations and enhance customer experiences. By analyzing data on delays, cancellations, and on-time performance, airlines can identify trends and implement strategies to improve punctuality and mitigate disruptions. Moreover, regulatory bodies and policymakers rely on this data to ensure safety standards, enforce regulations, and make informed decisions regarding aviation policies. Researchers and analysts use airline data to study market trends, assess environmental impacts, and develop strategies for sustainable growth within the industry. In essence, airline data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the aviation sector.

Content

This dataset comprises diverse parameters relating to airline operations on a global scale. The dataset prominently incorporates fields such as Passenger ID, First Name, Last Name, Gender, Age, Nationality, Airport Name, Airport Country Code, Country Name, Airport Continent, Continents, Departure Date, Arrival Airport, Pilot Name, and Flight Status. These columns collectively provide comprehensive insights into passenger demographics, travel details, flight routes, crew information, and flight statuses. Researchers and industry experts can leverage this dataset to analyze trends in passenger behavior, optimize travel experiences, evaluate pilot performance, and enhance overall flight operations.

Dataset Glossary (Column-wise)

• Passenger ID - Unique identifier for each passenger
• First Name - First name of the passenger
• Last Name - Last name of the passenger
• Gender - Gender of the passenger
• Age - Age of the passenger
• Nationality - Nationality of the passenger
• Airport Name - Name of the airport where the passenger boarded
• Airport Country Code - Country code of the airport's location
• Country Name - Name of the country the airport is located in
• Airport Continent - Continent where the airport is situated
• Continents - Continents involved in the flight route
• Departure Date - Date when the flight departed
• Arrival Airport - Destination airport of the flight
• Pilot Name - Name of the pilot operating the flight
• Flight Status - Current status of the flight (e.g., on-time, delayed, canceled)

Structure of the Dataset

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Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable Synthetic datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Kevin Woblick on Unsplash

Thumbnail by: Airplane icons created by Freepik - Flaticon

The U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT's monthly Air Travel Consumer Report, published about 30 days after the month's end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released.

This version of the dataset was compiled from the Statistical Computing Statistical Graphics 2009 Data Expo and is also available here.

This dataset provides detailed information on flight arrivals and delays for U.S. airports, categorized by carriers. The data includes metrics such as the number of arriving flights, delays over 15 minutes, cancellation and diversion counts, and the breakdown of delays attributed to carriers, weather, NAS (National Airspace System), security, and late aircraft arrivals. Explore and analyze the performance of different carriers at various airports during this period. Use this dataset to gain insights into the factors contributing to delays in the aviation industry.

Purpose: The purpose of this dataset is to offer insights into the performance of U.S. carriers at various airports during August 2013 - August 2023, focusing on flight arrivals and delays. By providing detailed information on key metrics such as the number of arriving flights, delays over 15 minutes, cancellations, and diversions, the dataset aims to facilitate analyses of factors contributing to delays, including those attributed to carriers, weather, the National Airspace System (NAS), security, and late aircraft arrivals. Researchers, data scientists, and aviation enthusiasts can leverage this dataset to explore patterns, identify trends, and draw conclusions that contribute to a better understanding of the aviation industry's operational challenges.

Structure: The dataset is structured as a tabular format with rows representing unique combinations of year, month, carrier, and airport. Each row contains information on various metrics, including flight counts, delay counts, cancellation and diversion counts, and delay breakdowns by different factors. The columns provide specific details such as carrier codes and names, airport codes and names, and counts of delays attributed to carrier, weather, NAS, security, and late aircraft arrivals. The structured format ensures that users can easily query, analyze, and visualize the data to derive meaningful insights.

• year: The year of the data.
• month: The month of the data.
• carrier: Carrier code.
• carrier_name: Carrier name.
• airport: Airport code.
• airport_name: Airport name.
• arr_flights: Number of arriving flights.
• arr_del15: Number of flights delayed by 15 minutes or more.
• carrier_ct: Carrier count (delay due to the carrier).
• weather_ct: Weather count (delay due to weather).
• nas_ct: NAS (National Airspace System) count (delay due to the NAS).
• security_ct: Security count (delay due to security).
• late_aircraft_ct: Late aircraft count (delay due to late aircraft arrival).
• arr_cancelled: Number of flights canceled.
• arr_diverted: Number of flights diverted.
• arr_delay: Total arrival delay.
• carrier_delay: Delay attributed to the carrier.
• weather_delay: Delay attributed to weather.
• nas_delay: Delay attributed to the NAS.
• security_delay: Delay attributed to security.
• late_aircraft_delay: Delay attributed to late aircraft arrival.

Usage: Researchers, analysts, and data enthusiasts can utilize this dataset for a variety of purposes, including but not limited to:

Performance Analysis: Assess the on-time performance of different carriers at specific airports and identify potential areas for improvement.

Trend Identification: Analyze temporal trends in delays, cancellations, and diversions to understand whether certain months or periods exhibit higher operational challenges.

Root Cause Analysis: Investigate the primary contributors to delays, such as carrier-related issues, weather conditions, NAS inefficiencies, security concerns, or late aircraft arrivals.

Benchmarking: Compare the performance of various carriers across different airports to identify industry leaders and areas requiring attention.

Predictive Modeling: Use historical data to develop predictive models for flight delays, aiding in the development of strategies to mitigate disruptions.

Industry Insights: Contribute to a broader understanding of the factors influencing operational efficiency within the U.S. aviation sector.

As users explore and analyze the dataset, they can gain valuable insights that may inform decision-making processes, improve operational strategies, and contribute to a more efficient and reliable air travel experience.

Analysis of ‘MTA Subway Terminal On-Time Performance: Beginning 2015’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7d462bc3-c388-409f-aeac-02496b206c62 on 27 January 2022.

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

Terminal On-Time Performance measures the percentage of trains arriving at their destination terminals as scheduled.

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

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the first part of a two-part exercise focusing on time series analysis.

Introduction

Time series are a special class of dataset, where a response variable is tracked over time. The frequency of measurement and the timespan of the dataset can vary widely. At its most simple, a time series model includes an explanatory time component and a response variable. Mixed models can include additional explanatory variables (check out the nlme and lme4 R packages). We will be covering a few simple applications of time series analysis in these lessons.

Opportunities

Analysis of time series presents several opportunities. In aquatic sciences, some of the most common questions we can answer with time series modeling are:

• Has there been an increasing or decreasing trend in the response variable over time?

• Can we forecast conditions in the future?

  Challenges

Time series datasets come with several caveats, which need to be addressed in order to effectively model the system. A few common challenges that arise (and can occur together within a single dataset) are:

• Autocorrelation: Data points are not independent from one another (i.e., the measurement at a given time point is dependent on previous time point(s)).

• Data gaps: Data are not collected at regular intervals, necessitating interpolation between measurements. There are often gaps between monitoring periods. For many time series analyses, we need equally spaced points.

• Seasonality: Cyclic patterns in variables occur at regular intervals, impeding clear interpretation of a monotonic (unidirectional) trend. Ex. We can assume that summer temperatures are higher.

• Heteroscedasticity: The variance of the time series is not constant over time.

• Covariance: the covariance of the time series is not constant over time. Many of these models assume that the variance and covariance are similar over the time-->heteroschedasticity.

  Learning Objectives

After successfully completing this notebook, you will be able to:

1. Choose appropriate time series analyses for trend detection and forecasting

2. Discuss the influence of seasonality on time series analysis

3. Interpret and communicate results of time series analyses

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an era where e-commerce is booming, the ability to understand and optimize customer experience is paramount for businesses aiming to thrive. An international e-commerce company, specializing in electronic products, has embarked on an ambitious project to delve deep into their customer database to uncover vital insights that could revolutionize their operations. Leveraging advanced machine learning techniques, the company aims to dissect the complex dynamics of customer interactions and product shipments to enhance satisfaction and efficiency.

The foundation of this analytical venture is a robust dataset comprising 10,999 observations across 12 meticulously curated variables. These variables provide a comprehensive overview of the customer journey, from the initial purchase to the final delivery. Key data points include:

ID: A unique identifier for each customer, ensuring precise tracking and personalized insights. Warehouse Block: With the company's expansive warehouse segmented into blocks A through E, this variable helps in logistics optimization and inventory management. Mode of Shipment: Understanding the impact of different shipment methods (Ship, Flight, Road) on customer satisfaction and delivery efficiency. Customer Care Calls: The frequency of customer inquiries serves as an indicator of service quality and customer engagement. Customer Rating: A direct measure of customer satisfaction, with ratings ranging from 1 (lowest) to 5 (highest). Cost of the Product: This financial metric is crucial for pricing strategies and profitability analysis. Prior Purchases: Tracking customers' purchase history aids in predicting future buying behavior and personalizing marketing efforts. Product Importance: Categorizing products based on their importance (low, medium, high) enables tailored handling and prioritization. Gender: Analyzing shopping patterns and preferences across genders. Discount Offered: Examining the impact of discounts on sales volume and customer acquisition. Weight in Grams: The logistical aspect of shipping, influencing costs and delivery methods. Reached on Time: The critical outcome variable indicating whether a product was delivered within the expected timeframe, serving as a benchmark for operational efficiency. The company acknowledges the contribution of the broader data science community by making this dataset publicly available on GitHub, fostering collaborative research and innovation in customer analytics. This initiative is not just about understanding past performances but is aimed at inspiring data-driven strategies that can address pressing questions such as the correlation between customer ratings and on-time deliveries, the effectiveness of customer support, and the influence of product importance on customer satisfaction and delivery success.

This exploratory journey through data is poised to offer actionable insights that could lead to enhanced product shipment tracking, improved customer satisfaction, and ultimately, a competitive edge in the fast-paced world of e-commerce.

Context

The U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics tracks the on-time performance of domestic flights operated by large air carriers. I came across this useful data from DOT's database at working and figured this would be a really helpful dataset: Summary information on the number of on-time, delayed, canceled, and diverted flight.

Content

The datasets contain daily airline information covering from flight information, carrier company, to taxing-in, taxing-out time, and generalized delay reason of exactly 10 years, from 2009 to 2019. The DOT's database is renewed from 2018, so there might be a minor change in the column names.

Acknowledgements

The flight delay and cancellation data were collected and managed by the DOT's Bureau of Transportation Statistics, only included data related to time-analysis on each flight. For any inspiration, please see tasks.

We'll tailor a bespoke airline dataset to meet your unique needs, encompassing flight details, destinations, pricing, passenger reviews, on-time performance, and other pertinent metrics.

Leverage our airline datasets for diverse applications to bolster strategic planning and market analysis. Scrutinizing these datasets enables organizations to grasp traveler preferences and industry trends, facilitating nuanced operational adaptations and marketing initiatives. Customize your access to the entire dataset or specific subsets as per your business requisites.

Popular use cases involve optimizing route profitability, improving passenger satisfaction, and conducting competitor analysis.

Analysis of ‘Strategic Measures_Transit Travel Time Reliability: Percent change in MetroBus on-time performance by Type’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/1e24fd66-c213-4ebe-ab2b-35ba26e2da37 on 26 January 2022.

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

This dataset supports measure M.A.2.a of SD 2023. The source of the data is Capital Metro. Each row displays the statistics related to performance by time.This dataset can be used to know more about on-time performance trends for transit in Austin. View more details and insights related to this measure on the story page : https://data.austintexas.gov/stories/s/M-A-2-a-Transit-Travel-Time-Reliability-percent-ch/ktzy-fxx3/

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

This data release includes estimates of annual and monthly mean concentrations and fluxes for nitrate plus nitrite, orthophosphate and suspended sediment for nine sites in the Mississippi River Basin (MRB) produced using the Weighted Regressions on Time, Discharge, and Season (WRTDS) model (Hirsch and De Cicco, 2015). It also includes a model archive (R scripts and readMe file) used to retrieve and format the model input data and run the model. Input data, including discrete concentrations and daily mean streamflow, were retrieved from the National Water Quality Network (https://doi.org/10.5066/P9AEWTB9). Annual and monthly estimates range from water year 1975 through water year 2019 (i.e. October 1, 1974 through September 30, 2019). Annual trends were estimated for three trend periods per parameter. The length of record at some sites required variations in the trend start year. For nitrate plus nitrite, the following trend periods were used at all sites: 1980-2019, 1980-2010 and ...

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The importance of data as an ingredient for sound economic decision-making requires BEA to deliver data to decision-makers and other data users not only quickly but also reliably—that is, on schedule. Each fall, BEA publishes a schedule for the release of its economic data the following year; this measure is evaluated as the number of scheduled releases issued on time. BEA has an outstanding record of releasing its economic data on schedule.

The Airport Departure Control System (DCS) market is experiencing robust growth, driven by the increasing passenger traffic globally and the imperative for airlines and airports to enhance operational efficiency and passenger experience. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $3.5 billion by 2033. This growth is fueled by several key trends, including the adoption of cloud-based solutions for improved scalability and cost-effectiveness, the integration of advanced technologies like Artificial Intelligence (AI) and machine learning for predictive analytics and optimized resource allocation, and the increasing focus on real-time data analysis to mitigate delays and improve on-time performance. The segmentation reveals a strong preference for cloud-based systems, offering flexibility and accessibility compared to on-premises solutions. Airlines, airports, and ground handlers represent the largest application segments, driven by the need for centralized control and efficient management of departure processes. Competition in the market is intense, with a diverse range of established players and emerging technology providers vying for market share. However, high initial investment costs and the complexity of integrating new systems into existing infrastructure present challenges to market expansion. Geographic expansion is another significant factor. North America and Europe currently hold the largest market share, owing to advanced infrastructure and early adoption of DCS technologies. However, rapid growth is anticipated in the Asia-Pacific region, fueled by significant investments in airport infrastructure and burgeoning air travel demand. The Middle East and Africa are also poised for substantial growth due to ongoing infrastructural development and increasing air travel numbers. The market’s future trajectory will depend on continuous technological advancements, the adoption of innovative solutions, and effective collaboration between stakeholders across the aviation ecosystem. Further expansion will be influenced by regulatory compliance and the ongoing demand for improved passenger experience, safety, and operational efficiency within the global airport system.

Trend Detection and Forecasting

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the second part of a two-part exercise focusing on time series analysis.

Introduction

Time series are a special class of dataset, where a response variable is tracked over time. Time series analysis is a powerful technique that can be used to understand the various temporal patterns in our data by decomposing data into different cyclic trends. Time series analysis can also be used to predict how levels of a variable will change in the future, taking into account what has happened in the past.

Learning Objectives

1. Choose appropriate time series analyses for trend detection and forecasting
2. Discuss the influence of seasonality on time series analysis
3. Interpret and communicate results of time series analyses

Analysis of ‘Mark on Time ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/5ae9d0c1c8d8c9146a44cca5 on 17 January 2022.

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

Total number of brands sold, isolated MNH, NHL associated with ENH, MNH associated with EOL and MNH associated with ANH

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

Analysis of ‘MDOT Maryland Transit Administration Modal On-Time Performance Monthly (FY)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2c777931-116c-4330-9df4-1bbec96d090c on 26 January 2022.

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

MDOT MTA Modal On-time performance measures the percent of service provided on-time for each MTA mode of transit.

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

1) Data Introduction • The On Time Graduation Dataset is a GPA dataset collected from a private university in Indonesia. This dataset includes GPA scores from the first four semesters (ip1, ip2, ip3, ip4) and indicates whether the student graduated on time (tepat). This dataset can be used to analyze the factors influencing timely graduation and to develop predictive models for educational outcomes.

2) Data Utilization (1) On Time Graduation Data has characteristics that: • It includes four GPA scores representing the academic performance of students over their first four semesters. This information is essential for understanding academic progress and identifying patterns that contribute to on-time graduation. (2) On Time Graduation Data can be used to: • Educational Analytics: Helps in identifying students at risk of not graduating on time by analyzing their GPA trends, allowing for timely interventions. • Policy Making: Assists educational institutions in developing policies to support students in achieving academic success and graduating on time. • Predictive Modeling: Supports the development of models to predict students' likelihood of graduating on time based on their GPA scores.

Analysis of ‘Company on Time ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/5ae9d0bdc8d8c9146d44cc83 on 16 January 2022.

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

No. of Constitutions in the month, Total No of Constitutions, Percentage of Constitutions per NHS, Average Time of Constitution (accumulated), Total number of companies that have joined the arbitration centers and N° total of ENH with associated Brand

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

DATA on Time-motion analysis in men’s breaking a longitudinal study