✈️ Airline Flight Path Demand Analysis

A data-driven project exploring how the COVID-19 pandemic impacted airline flight path demand. Using R, this project examines historical flight data, visualizes trends, and builds predictive models to better understand the changing landscape of air travel.

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📝 Table of Contents

• Project Overview
• Features
• Data Sources
• Installation
• Usage
• Results
• License

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📖 Project Overview

The COVID-19 pandemic drastically altered the demand for air travel, causing significant shifts in passenger behaviors and airline operations. This project focuses on:

• Analyzing flight path demand during the pandemic.
• Identifying trends in passenger travel across major routes.
• Predicting recovery patterns for the airline industry.

This analysis provides insights for airlines, policymakers, and travelers to adapt to the "new normal" in aviation.

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🌟 Features

• Exploratory Data Analysis (EDA): Visualize trends in flight frequency and demand.
• COVID-19 Impact Analysis: Evaluate changes in demand by geographic region and airline.
• Predictive Modeling: Use regression and time series models to forecast future flight demand.
• Interactive Visualizations: Build plots and dashboards to summarize findings.

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📊 Data Sources

This project utilizes the following datasets:

1. Bureau of Transportation Statistics (BTS): Contains flight data and passenger counts.
2. COVID-19 Data: Case trends and restrictions by region.
3. OpenSky Network: Real-time flight tracking data.

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⚙️ Installation

To run this project locally, follow these steps:

1. Clone this repository:

   git clone https://github.com/Jchow2/R-airline-covid19.git
   cd R-airline-covid19

Data

Transtats - United States Bureau of Transportation Statistics: https://www.transtats.bts.gov/

Install Required R Packages

install.packages(c("tidyverse", "lubridate", "forecast", "ggplot2", "shiny"))

Open the R project file and Load Script

source("airline-covid.R")

Usage

1. Exploratory Data Analysis

Perform an exploratory analysis of the dataset to generate summary statistics and visualizations.

Run exploratory analysis script

source("eda_airline_flight_demand.R")

This script outputs key metrics, trends, and visual summaries of the dataset, helping you understand passenger demand patterns.

2. Predict Future Demand

Estimate future trends in passenger demand using predictive models.

Run predictive modeling script

source("airline_predict_passenger_demand.R")

This script uses time-series forecasting models to predict passenger trends for upcoming months based on historical data.

3. Interactive Dashboard

Explore the results interactively through a Shiny application.

Launch the Shiny dashboard

shiny::runApp("shiny_app.R")

The dashboard provides a user-friendly interface to view predictions, analyze trends, and customize inputs for demand forecasting.

Results

Key insights from the analysis include:

International Demand Impact:

Significant declines in passenger demand on international routes during 2020, attributed to the pandemic's travel restrictions.

Regional Domestic Recovery:

Domestic travel showed faster recovery rates in certain regions, likely influenced by localized policy changes and consumer behavior.

Forecasting Insights:

Predictive models indicate a gradual recovery in passenger demand, with projections suggesting near-normal levels by 2023. Regional variations highlight the importance of localized strategies for recovery.

Interactive Visualization:

Explore detailed visualizations and insights through the Shiny app, providing a user-friendly dashboard for demand analysis. Detailed results, visualizations, and outputs are available in the results directory.

📜 License

This project is licensed under the MIT License.

👩‍💻 Author

Developed and maintained by Justin Chow. Feel free to connect on LinkedIn or reach out via email at jsjchow23@gmail.com.