Air Passenger Traffic Forecasting with Machine Learning

Air Passenger Traffic
Forecasting with
Machine Learning
Student’s Name
Instructor’s Name
Course Code
Date

Table of Contents
• Introduction
• Problem Statement
• Traditional air traffic forecasting approaches
• Project Aim
• Project Execution Plan
• Approach
• Design
• Data collection/ analysis plan
• Historical data
• Analysis
• Proposed artefact
• About the artefact
• Design of the artefact models
• Implementation Approach
• Development models

Table of Contents (Contd.)
• Forecasting with intelligence
• Results
• Trends
• Responsibilities
• Roles
• Limitation
• Comparison with the current system
• Further Research
• Conclusion
• References

Introduction
 Analysis of the air traffic movements help in
forecasting the future need and demands of
service in the airport.
 Forecasting help is determining the improvements
required to meet these demands
 Analysis of the air traffic data for 20 major cities
have been analysis to propose the forecasting
model.

Problem Statement
 There are certain negative consequences along
with the substantial economic benefits, these are.
 Increasing delays in flight.
 Failing to accurately estimate and find the pattern in
the air traffic movement
 Failing to meet the demand capacity.
 The restriction in flow control measure create air
holding problem (Cruciol et al. 2015).
 Inadequate air travel service during the high
demand hours.

Traditional air traffic
forecasting approaches
 Many regulatory Agencies use the traditional
modelling techniques developed in 1985 by
International Civil Aviation organization for traffic
forecasting (Srisaeng et al., 2015). Through
review of literature it is identified that the
common forecasting methods are
 Market Research
 Gravity model
 Simulation model
 Time Series Model
 Trend projections
 Econometric relationship forecasting