A practical approach to determining critical macroeconomic factors in air-traffic volume based on K-means clustering and decision-tree classification
| Autor: | Jieh Haur Chen, Hsi Hsien Wei, Hsin Yi Wei, Zhongnan Ye, Yi Ping Chen, Chih Lin Chen |
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| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
education.field_of_study Computer science 020209 energy Strategy and Management Industrial production index 05 social sciences Measures of national income and output Population k-means clustering Decision tree Transportation 02 engineering and technology Management Monitoring Policy and Law Air traffic control 0502 economics and business 0202 electrical engineering electronic engineering information engineering Per capita Econometrics education Cluster analysis Law |
| Zdroj: | Journal of Air Transport Management. 82:101743 |
| ISSN: | 0969-6997 |
| DOI: | 10.1016/j.jairtraman.2019.101743 |
| Popis: | A given region's volume of air passengers and cargo is frequently taken to represent its economic development. This research proposes a practical methodology for investigating the inherent patterns of the relationships between air-traffic volume and macroeconomic development, utilizing data-mining techniques, including K-means clustering and Decision Tree C5.0 classification. Using the case of Taiwan from 2001 to 2014, 32 potential macroeconomic factors ascertained from a literature review were combined with air-traffic volume data to establish a 168-month dataset. After this dataset was grouped into five clusters, decision trees were implemented to determine its critical macroeconomic characteristics. The resulting four critical factors and their thresholds were the Information and Electronics Industrial Production Index (IE Index), at 83.22; National Income Per Capita, at US$3,222; Employed Population, at 10.134 million; and the Japanese Nikkei 225 Stock Average, at 10564.44. Among these, the IE Index was found to be the first critical factor relating to air-traffic volume as well as the only characteristic to distinguish Cluster V – 58 consecutive months from March 2010 to December 2014 inclusive – among others, and the reasonableness of this finding was confirmed via examination of detailed air-traffic statistics. Besides, the effectiveness of the four identified critical factors as predictive variables were validated by comparing forecasted results with actual air traffic volume from 2015 to 2016. Understanding these four critical factors and their relative importance is of great value to policymakers seeking to allocate limited resources optimally and objectively. Therefore, as an effective and efficient means of capturing significant and explainable macroeconomic factors influencing air-traffic volume, the proposed methodology can be applied to strategy formulation, operations management, and investment planning by governments, airports, airlines, and related entities. |
| Databáze: | OpenAIRE |
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