The modern theme park experience faces a critical challenge: balancing visitor satisfaction with operational efficiency. While parks like Ocean Park offer diverse attractions, visitors often struggle to navigate crowded spaces, leading to suboptimal experiences characterized by long wait times, inefficient routing, and mismatched preferences. This research explores the development of an intelligent tour guide application that dynamically ranks attractions through the integration of personal interests, real-time queue data, and spatial navigation – a system poised to redefine theme park engagement through advanced computational methods.
The primary technical hurdle involves real-time balancing of three
competing factors[3][6][8]:
1. Visitor Preferences: Individual interests
(thrill-seeking vs family-friendly) and accessibility requirements
2. Operational Dynamics: Queue times, ride maintenance
schedules, and crowd flow patterns
3. Spatial Constraints: Physical distances between
attractions and pathway congestion
Existing systems like TourVista ([1]) demonstrate basic personalization capabilities but lack integration with live operational data. Research reveals current preference learning algorithms struggle with ranking accuracy ([2]), while theme park crowd models ([3][6]) show predictive capabilities without personalization layers.