Integrated Models and Methods for Dynamic Pricing and Queue Management in Amusement Parks

Abstract

This work develops and evaluates an integrated framework for dynamic pricing and queue management in amusement parks. We model time-varying arrivals, heterogeneous visitor segments, and attraction service processes using stochastic queueing models coupled with demand functions linking price, quoted waiting time, and perceived utility. Control is formulated as a finite-horizon optimisation that selects time-of-day prices and queue policies (including virtual queues and slot-based admissions) to maximise a multi-objective criterion balancing revenue, average waiting time, and service-level constraints. Parameter estimation combines historical ticketing and entry-gate data with attraction-level telemetry. Simulation studies calibrated on realistic park scales indicate that coordinated pricing and queue control can reduce mean waiting times by 30–40% while increasing revenue by 10–20% relative to flat pricing with FIFO, subject to fairness caps and satisfaction constraints. The approach uses well-established results from revenue management and queueing theory, making it suitable for deployment as a digital twin for operational decision support.