Air Transportation Direct Share Time Series Analysis and Forecast

Abstract

Air transportation direct share is the ratio of direct passengers to total passengers on a directional origin and destination (O&D) pair during a period of time. The O&D direct share shows the air passengers' general preference for direct flight services under a certain market state. The O&D direct share time series shows the changing process of a direct flight market. A better understanding and more accurate forecasting of direct share can benefit air traffic planners, airlines, and airports in multiple ways. In previous studies and practice, the direct share is usually assumed as a fixed average, which is not true for air transportation practice. The characteristics of O&D direct share time series are investigated in this research. Time series models and machine learning models are explored for the direct share time series forecasting. 1295 busiest O&D pairs across the U.S. are considered in this research. A framework for direct share time series modeling and forecasting is proposed in this research, which is a promising replacement to the model used for the direct share forecasting by the Federal Aviation Administration Terminal Area Forecast.

Efficient Time Series Clustering by Minimizing Dynamic Time Warping Utilization

“…T IME series is one of the most important data in the modern data-driven society and can be generated from nearly every aspects in the daily life [1]. Time series analysis can benefit pervasive applications in different domains, e.g., financial marketing [2], smart home [3] and autonomous vehicles [4]. Time series clustering is a basic technique for analyzing time series.…”