Examining the Dynamics of Local and Transfer Passenger Share Patterns in Air Transportation

TL;DR

Problem: understanding how local and transfer passenger shares evolve in U.S. air transport, including post-pandemic recovery. Approach: a data-driven framework applying multiple time-series clustering methods (DynTW, $k$-Shape, SOM, SBD, AP, DBA+GMM) to 3,936 O&D pairs using yearly aggregations to capture long-term patterns. Contributions: comprehensive O&D-level analysis of local-share dynamics, evidence of macro-level synchronization across markets, and a modeling approach that supports network planning and resilience. Significance: results inform airline network design, airport infrastructure investment, and policy decisions to enhance competitiveness and system resilience.

Abstract

The air transportation local share, defined as the proportion of local passengers relative to total passengers, serves as a critical metric reflecting how economic growth, carrier strategies, and market forces jointly influence demand composition. This metric is particularly useful for examining industry structure changes and large-scale disruptive events such as the COVID-19 pandemic. This research offers an in-depth analysis of local share patterns on more than 3900 Origin and Destination (O&D) pairs across the U.S. air transportation system, revealing how economic expansion, the emergence of low-cost carriers (LCCs), and strategic shifts by legacy carriers have collectively elevated local share. To efficiently identify the local share characteristics of thousands of O&Ds and to categorize the O&Ds that have the same behavior, a range of time series clustering methods were used. Evaluation using visualization, performance metrics, and case-based examination highlighted distinct patterns and trends, from magnitude-based stratification to trend-based groupings. The analysis also identified pattern commonalities within O&D pairs, suggesting that macro-level forces (e.g., economic cycles, changing demographics, or disruptions such as COVID-19) can synchronize changes between disparate markets. These insights set the stage for predictive modeling of local share, guiding airline network planning and infrastructure investments. This study combines quantitative analysis with flexible clustering to help stakeholders anticipate market shifts, optimize resource allocation strategies, and strengthen the air transportation system's resilience and competitiveness.

Figures (14)

• Figure 1: Simplified itineraries with last leg ATL $\rightarrow$ MEM.
• Figure 2: Local share and passenger share on MSP $\rightarrow$ ANC.
• Figure 3: Local share and passenger change BWI $\rightarrow$ ALB.
• Figure 4: Local share on O&D pairs PBI $\longleftrightarrow$ TPA.
• Figure 5: Local share and actual departure on MEM $\rightarrow$ DTW.