Spatial-Temporal Analysis of Recent Air Passenger Flows

Spatial-Temporal Analysis of Recent Air Passenger Flows

Changjoo Kim (University of Cincinnati, Cincinnati, OH, USA) and Hyun Kim (University of Tennessee, Knoxville, TN, USA)
Copyright: © 2013 |Pages: 21
DOI: 10.4018/jagr.2013100103
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Airline mergers and acquisitions (MAs) are on the rise across the globe and have been a growing trend in the U.S. aviation industry in the last few years. MAs are taking several factors into consideration, such as cost efficiency, competition, and geographic coverage. For airlines, these transactions can eliminate overlapping routes and help reduce competition, leading airlines to achieve higher operating margins. For travelers, MAs often lead to lesser flight frequency, higher airfares or longer travel miles. To explore spatial-temporal variations from airline and passenger perspectives, this paper focuses on 55 major airports in the 50 largest cities between 2000 and 2010. The detailed results of passenger flow patterns suggest that some airports have more spatial imbalance than others in terms of passenger travel distances. Further, the findings indicate that the MAs have different effects on passenger flows and traveled distances, and the effect is complexly related to the airport’s spatial status.
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It is well-known that the distance (cost) associated with air travel depends on a multitude of factors, such as connections, travel fares, network structure, airlines, etc. All of these factors vary across space. In this study, we examined spatio-temporal changes in the landscape of inter-city air passenger flows. In order to analyze how MAs change the differentials in hub service structure and affect travel distances associated with inter-city passenger travel, a spatial-temporal framework is proposed to derive a ratio of direct distance to flow distance, representing the efficiency of inter-city air travel among the top 55 large U.S. airports in 50 cities, and the 7 major Delta hubs. The results highlight the tremendous impact that changes in the hub structure service can have on the distance of inter-city air passenger travel. Moreover, the results provide important insights on how the level of service availability between cities in the U.S. varies across space (market segments).

In literature, traditional air passenger flows studies are based on airports or cities (Corsi, 1997; Jin, 2004; Wang, 2007). This approach provided a general understanding of a node-based perspective of air passengers but did not successfully address a network aspect among airports to various factors. This gap can be overcome by incorporating market segments into the analysis. To illustrate the importance of a market-based approach, where each market (city pair) is a measurement unit for air passenger flows, there are several noticeable characteristics on air passenger flows. First, air passenger flows often occur in a direct flow and support the importance of a network relationship among airports. Second, air passenger flow by nature is not merely a city-based event but also largely related to the interaction among cities. Last, the market segment, rather than the airport itself, can efficiently identify a repeated pattern of air passengers over multiple periods. Due to these characteristics, a market-based air passenger analysis is an appropriate method to study air passenger flows collected over time. In this study, we analyze market-based, inter-city air passengers based on an 11-year database. The purpose of this research is to systematically study the relationship between the MAs and the changes in flow and distance in major airports. To do so, it statistically tests the hypothesis that the MAs increase the likelihood that a distance between hubs will decrease, and a distance between hub and spoke will increase.

This article is organized as follows. The following section explores issues relevant to the air passenger flow analysis and models shown in previous research. It reflects recent studies in air transportation and focuses on papers that address passenger flows and distances within the context of the airline network structure. The methodology section provides a brief explanation of a statistical test and time series analysis. The application of the methodology is demonstrated with the use of empirical analyses. Concluding remarks are then given in the final section.

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