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.
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).
Proposed artefact
The artefact proposed implement the analysis methods discussed with the machine learning and data mining capabilities.The artefact is able to apply the classification and predicting algorithms.The artefact can find the pattern and trends in the air traffic with the analyzed data.The artefact can also be used forecasting the future air traffic.
About the artefact
With these factors the machine learning the spatio-temporal pattern can be determined using the gravity model along with exponential smoothing (Awad et al., 2017).design of the artefact models The design of the model is based on the finding the best evaluation. The extended larger range of the variables through the data set are anticipated to the better comprehend the factors with decision tree (Phyoe, et al. 2015).Variable relation of the airline capacity, constraints, capabilities or the airport(Gardi et al., 2015).
The forecasting of the approaches that are used by the models are
Log-log model: This approach uses the linear regression with the logarithmic conversion of the independent and dependent variable (Chudy-Laskowska & Pisula, 2017). .3 stage model: The k-nearest with support vector machine and linear regression.
Implementation Approach
The following approach is taken in the artefact 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).
Forecasting with intelligence
Using advance software for the analysis of the data based on the learned knowledge to produce accuracy of the results. The optimal combination of the models are used to produce the accurate results along with identification of the disruption of the system due to holidays, weather or events.
Result
The result is calculated based on the cities. The variables that are considered as the depended variable that determine the forecast. The system analyses the variable to find the pattern and forecast all the available observations through the data available.
Trends
After the deregulation of domestic aviation in 1990 the private low-cost airlines have emerged into the Australian airline industry. This has the shown growth rate of 5% per year, in the air travel, over the past 30 years (Busquets, Evans & Alonso, 2015).
Limitation
The limitation of the artefact are as follows The model is only able to forecast and not able to solve any air holding problem The can estimate the demand but cannot do anything to manage the airport It requires correct data for the accurate results The initial run or the test can have errors as the machine requires to learning the trends for make accurate predictions
Comparison with the current system
The comparison with the current forecasting methodology with the proposed forecasting methodology is that the proposed system is more accurate that ay on the previous system as they required manual effort for making the analysis and estimation. In the proposed system the machine does the analysis and calculation part based on the forecasting algorithms that are implement with the model.
Conclusion
The accuracy of the machine learning can immensely benefit is the accurate and precise forecast of the air passenger traffic. Through the forecast the management can be prepare with the necessary strategies to meet the demand and maintain their economic growth.
Reference
Busquets, J. G., Evans, A., & Alonso, E. (2015). Application of Data Mining in Air Traffic Forecasting. In 15th AIAA Aviation Technology, Integration, and Operations Conference (p. 2732).
Phyoe, S. M., Guo, R., & Zhong, Z. W. (2015). An air traffic forecasting study and simulation. MATTER: International Journal of Science and Technology, 2(3).
Mao, L., Wu, X., Huang, Z., & Tatem, A. J. (2015). Modeling monthly flows of global air travel passengers: An open-access data resource. Journal of Transport Geography, 48, 52-60.
Srisaeng, P., Baxter, G., Richardson, S., & Wild, G. (2015). A forecasting tool for predicting Australia’s domestic airline passenger demand using a genetic algorithm. Journal of Aerospace Technology and Management, 7(4), 476-489.
Cruciol, L. L., Weigang, L., de Barros, A. G., & Koendjbiharie, M. W. (2015). Air holding problem solving with reinforcement learning to reduce airspace congestion. Journal of Advanced Transportation, 49(5), 616-633.
Chudy-Laskowska, K., & Pisula, T. (2017). SEASONAL FORECASTING FOR AIR PASSENGER TRAFIC.
Gardi, A., Sabatini, R., Kistan, T., Lim, Y., & Ramasamy, S. (2015, April). 4 Dimensional trajectory functionalities for air traffic management systems. In Integrated Communication, Navigation, and Surveillance Conference (ICNS), 2015 (pp. N3-1). IEEE.
Awad, Y. A., Koutrakis, P., Coull, B. A., & Schwartz, J. (2017). A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States. Environmental research, 159, 427-434.
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