A Visual Analytics Framework for Exploring Theme Park Dynamics

摘要

In 2015,the top 10 largest amusement park corporations saw a combined annual attendance of over 400 mil-lion visitors Daily average attendance in some of the most popular theme parks in the world can average44,000 visitors per day. These visitors ride attractions, shop for souvenirs, and dine at local establishmenthowever, a critical component of their visit is the overall park experience. This experience depends on thewait time for rides the crowd flow in the park and various other factors linked to the crowd dynamicsand human behavior. As such, better insight into visitor behavior can help theme parks devise competitivestrategies for improved customer experience. Research into the use of attractions, facilities, and exhibits canbe studied, and as behavior profiles emerge, park operators can also identify anomalous behaviors of visitors which can improve safety and operations. In this article, we present a visual analytics framework forzing crowd dynamics in theme parks. Our proposed framework is designed to support behavioral analysis by summarizing patterns and detecting anomalies. We provide methodologies to link visitor movementdata, communication data, and park infrastructure data. This combination of data sources enables a semanticanalysis of who, what, when, and where, enabling analysts to explore visitor-visitor interactions and visitorastructure interactions. analysts can identify behaviors at the macro level through semantic trajectorclustering views for group behavior dynamics, as well as at the micro level using trajectory traces andovel visitor network analysis view. We demonstrate the efficacy of our framework through two case studiesof simulated theme park visitors.