Predicting a number of passengers per trip is an important topic in airline business and travel economy which has spurred the interest of airline companies to develop better predictive models to accommodate in their business model. This presents extensive process of predicting the occupancy of the airline seats using the ARIMA model. Published airline passenger data is obtained from RPubs is used with predictive model developed. Results achieved convey that the autoregressive integrated moving average model has a strong potential for prediction and can compete with existing models for this business projects.
Air transport is presently one of the fastest growing service sectors in world. People continuously travel for various reasons (tourism, business) to different locations within country and worldwide. As a result, analyses and forecasts of data regarding air passengers is of great importance in designing development strategies for air service providers. Air transport improves the growth of local economy, arriving to the development of companies, and thus developing the competitiveness of business. For this it is important to analyze the future air traffic and its changes with time properly which helps the air service providers to design their services adequately.
Forecasting traffic intensity using air traffic models involves finding the future demand for air transport in the service. Another important thing is the seasonality of air traffic. Seasonal variations make it essential to observe traffic intensity in every month of the year, and on every day of the month. The main output of a traffic forecast includes number of air passengers.
The main purpose of this study is to prepare a tool to analyze and forecast the traffic flow month wise which helps Airlines to revise their services.
An airline company has the data of the number of passengers that have travelled with them on a particular route for the past few years. Using this data, they want to see if they can forecast the number of passengers for the next twelve months.
Making this forecast could be quite beneficial to the company as it would help them take some crucial decisions like -
- What capacity aircraft should they use?
- When should they fly?
- How many air hostesses and pilots do they need?
- How much food should they stock in their inventory?
