A Basic Study of the Forecast of Air Transportation Networks Using Different Forecasting Methods

Yuya Takahashi,Rie Osawa,Susumu Shirayama

doi:10.4236/jdaip.2017.52004

Abstract

This research applies network structuring theories to the aviation domain and predicts aviation network growth, considering a flight connection between airports as a link between nodes. Our link prediction approach is based on network structure information, and to improve prediction accuracy, it is necessary to estimate the mechanism of aviation network growth. This research critically evaluates the prediction accuracy of two methods: the receiver operating characteristic curve method (ROC) and the logistic regression method. We propose a four-step method to evaluate the relative predictive accuracy among different link prediction methods. A case study of US aviation networks indicated that the ROC method provided better prediction accuracy compared with the logistic regression method. This result suggests that tuning of the prediction distribution and the regression model coefficients can further improve the accuracy of the logistic regression method.

Highlights

In recent years, the number of air passengers has been increasing, with the worldwide annual number of passengers up by approximately 34% from 2010 to [1]
Based on the above discussion, in the current study, we aim to improve the prediction accuracy of the future aviation network by the method of link prediction coupled with predictive measures calculated from the network structure
We examine the prediction of the aviation network from the CN, PA, and AA values, which have high accuracy in the case of the receiver operating characteristic curve method (ROC) curve, and obtain the following insights: A node pair that will connect in the future possesses three main characteristics in accordance with the definition of the CN, PA, and AA

Summary

Introduction

The number of air passengers has been increasing, with the worldwide annual number of passengers up by approximately 34% from 2010 to [1]. According to this trend, it is assumed that the demand for the air routes will keep increasing and that the aviation network will change in response to the increased the demand. Traffic jams due to traffic concentration can be predicted by analyzing both demand and local characteristics, such as population distribution This perspective is important to aviation safety because the number of accidents caused by human errors is increasing owing to the traffic jams [4]

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Conclusion