Beyond Queueing Theory: Integrating Poisson Processes and Prospect Theory to Model Passenger Satisfaction in Baggage Reclaim

As global aviation traffic recovers beyond prepandemic levels, airport terminals must address the challenge of balancing landside operations to sustainably avoid service level reductions. The baggage reclaim is a unique operational bottleneck that combines elements of logistics performance with passenger psychological profile. Given that the digitalization of airport operations and passenger experience personalization remains a significant concern for the authors (Palașcă & Stăncel, 2025), the general objective of this paper is to develop an interdisciplinary theoretical model that combines stochastic engineering processes with behavioral economic theories to quantify the utility of waiting from a passenger perspective. This objective will be achieved through the following specific objectives: to simulate the technical performance of baggage flow using Poisson processes to determine probability distributions of waiting times; and, to quantify the psychological disutility of these waiting times by applying Prospect Theory to different passenger profiles. Rather than solely minimizing average wait times, the proposed model demonstrates that the variance of waiting time, combined with the psychological “loss” acquired by exceeding wait times, is the far greater enemy of passenger loyalty. The methodology used in the paper relies on a hybrid mathematical approach, that, combines a robust mathematical model based on Poisson processes to simulate baggage flow patterns at airports, as well as a utility score (U) based on Prospect Theory in behavioral economics. Specifically, it uses five formulas that separately calculates the probability of waiting times exceeding the passengers’ expectation time of arrival (ETA) and the loss aversion coefficient that weighs this score. Insights were extrapolated from studies spanning topics such as the theory of queuing, Internet of Things (IoT) technologies, agent-based simulation methods, and loyalty programs psychology. Key insights yielded from this research state that optimizing the “tail” of the waiting time distribution (such as the risk of extreme delays) provides a superior marginal return on passenger experience satisfaction than increasing the average speed of baggage delivery. As well, it is demonstrated that the correlation between passengers’ arrival and baggage load induction into the carousel system, influenced by seat-order delivery management strategies, can reduce passengers perceived wait times with minimal infrastructural changes. Furthermore, the research demonstrates that correlating passenger arrival with baggage induction reduces perceived wait times. Hence, the proposed model provides a basis for a dashboard-ready algorithm to optimize resource allocation. This development, therefore, makes up a significant part of developing dynamic operational strategies. The proposed model can provide, in future implementations, with a dashboard-ready algorithm to dynamically allocate resources and avoid operational bottlenecks while managing expectations in real time.