Show-Up Profiles for Scheduled Services: Estimation and Applications

Abstract

Problem definition: Motivated by passenger arrivals at the security checkpoint of the Raleigh-Durham International Airport, we develop methods to study arrivals to a system in which arrivals are tied to scheduled events, such as flights. A key concept for modeling arrivals in such systems is the "show-up profile," a parametric probability distribution describing how far in advance passengers arrive for their flights. These profiles can be combined based on a known flight schedule to yield an effective and interpretable aggregate passenger arrival forecast. Existing industry practice and academic work estimate show-up profiles using customer surveys or other data sources matching arriving passengers with flights, which are typically not available to U.S. airports. This motivates our study of an easy to implement and dynamic method for estimating show-up profiles. Methodology/results: We introduce an innovative solution for estimating show-up profiles using infrared-beam people-counting sensors and a structural estimation approach that does not require a mapping of passengers to flights. A direct maximum likelihood approach is intractable, but we propose a tractable approximation and prove that it yields consistent estimates of the underlying show-up profile parameters. Our approach produces forecasting results comparable to pure machine learning methods in our airport context, yields significantly improved adaptive forecasts when combined with machine learning methods, and reveals empirical insights about passenger behavior variations across different times of day and flight destinations. Managerial implications: Our work presents a novel application of Internet of Things (IoT) technology to service operations with incomplete data and demonstrates the value of integrating known operational structure with black-box forecasting approaches. The methods we develop and test can be readily applied at U.S. airports and other transportation hubs, and they can be adapted to other event-driven service environments such as theaters, healthcare facilities, and museums.

Keywords: service operations, stochastic methods, transportation