ITECH7405 Project: Air Passenger Traffic Forecasting with ML Model

This project focuses on forecasting air passenger traffic using machine learning techniques. It addresses the problem of increasing flight delays and the need for accurate traffic estimation to meet demand capacity. The project aims to develop a model that analyzes historical data, identifies patterns using data mining and machine learning, and forecasts future air passenger traffic levels. The execution plan involves data collection, statistical analysis, and the implementation of data mining techniques with classification and linear regression algorithms. The proposed artifact utilizes factors such as the number of airports, population, income, and fuel prices to determine spatiotemporal patterns. The model employs techniques like K-nearest neighbors, support vector machines, and linear regression to enhance forecasting accuracy. The project compares the proposed methodology with current systems, highlighting the advantages of machine learning in providing more accurate and precise forecasts for better management and economic growth in the airline industry. Desklib provides access to this and similar assignments for students.

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

Project Aim
 The main aim of the project is to develop a model
based on machine learning that is able to
 Analyze the past data
 Find the pattern and trend data mining and machine
learning
 Using the machine learning model to forecast the
future levels of the air passenger traffic.
 Distribute the demand based on the traffic in cities.

Project Execution Plan
 The achievement of the objective requires the
execution of the developed model based on three
elements
 Collection of the data from the records and reports
 Statistical analysis of data with machine learning.
 Consideration of larger data set for the precise
estimation through Three stage models.
 Designing model for the analysis and forecasting.
 Implementation of the data mining techniques with
classification and linear regression algorithms.

Approach
 Division of the traffic data into classifiable object
 Classification of the variables with decision tree
and support vector machine algorithm.
 Combine the values of the resultant variable with
linear regression for the analysis and forecast the
future traffic with required annotations.
 Using Two models for cross verification of the
predicted result for increased accuracy.

Data collection/
analysis plan
 The data can be collected from the following areas
 Recorded dataset
 Reports
 Survey
 The collected data can be analyzed by data mining
to find pattern for determining the trends (Mao et
al. 2015).
 Analysis of the data begin by classification of the
data with pre-processing.
 Extraction of the featured data
 Applying regression for the final analysis

Historical Data
 The data recorded from 1985 to 2017 for the
domestic air traffic in Australia, reveals the
increase in the air traffic in past trends over the
past 32 years.
 With the oncoming years the demand of the airline
transport is likely to increase.
 The domestic flight service has increased far more
than the international flight service.
 In the last 10 years the average annual growth in the
passenger has been 1.9%.

Analysis
 The analysis of the data is done through following two
methods used of the forecasting the future trends and
demand
 Grouping the data with the k nearest algorithm
 Classification of the data with decision trees to sperate
the data based on labels and requirement.
 Support vector machine algorithm for finding the
estimated range for the traffic
 Linear regression model with logarithm transformation to
 Exponential smoothing of the forecast data to find the
average value of the demands
 A spatio-temporal prediction model for the forecasting
and trend prediction (Awad et al., 2017).