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A Visual Analytics Framework for Exploring Theme Park Dynamics

Authors:

Michael Steptoe,

Robert Krüger,

Rolando Garcia,

Ross MaciejewskiAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 8, Issue 1

Article No.: 4, Pages 1 - 27

https://doi.org/10.1145/3162076

Published: 20 February 2018 Publication History

Abstract

In 2015, the top 10 largest amusement park corporations saw a combined annual attendance of over 400 million visitors. Daily average attendance in some of the most popular theme parks in the world can average 44,000 visitors per day. These visitors ride attractions, shop for souvenirs, and dine at local establishments; however, a critical component of their visit is the overall park experience. This experience depends on the wait time for rides, the crowd flow in the park, and various other factors linked to the crowd dynamics and human behavior. As such, better insight into visitor behavior can help theme parks devise competitive strategies for improved customer experience. Research into the use of attractions, facilities, and exhibits can be 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 for analyzing 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 movement data, communication data, and park infrastructure data. This combination of data sources enables a semantic analysis of who, what, when, and where, enabling analysts to explore visitor-visitor interactions and visitor-infrastructure interactions. Analysts can identify behaviors at the macro level through semantic trajectory clustering views for group behavior dynamics, as well as at the micro level using trajectory traces and a novel visitor network analysis view. We demonstrate the efficacy of our framework through two case studies of simulated theme park visitors.

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Index Terms

A Visual Analytics Framework for Exploring Theme Park Dynamics
1. Information systems
  1. Information systems applications
    1. Decision support systems
    2. Spatial-temporal systems
      1. Geographic information systems

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Published In

cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 8, Issue 1

Special Issue on Interactive Visual Analysis of Human Crowd Behaviors and Regular Paper

March 2018

132 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/3185338

Editor:
Michelle Zhou
Juji, Inc., USA

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Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 20 February 2018

Accepted: 01 November 2017

Revised: 01 October 2017

Received: 01 January 2017

Published in TIIS Volume 8, Issue 1

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Chen LLiu JZhao Y(2023)Innovation and Development: An Analysis of Landscape Construction Factors in Quanzhou Maritime Silkroad Art ParkSustainability10.3390/su1504315715:4(3157)Online publication date: 9-Feb-2023
https://doi.org/10.3390/su15043157
Bai SHe HHan CYang MYu DBi XGupta BFan WPanigrahi P(2023)Exploring thematic influences on theme park visitors' satisfaction: An empirical study on Disneyland ChinaJournal of Consumer Behaviour10.1002/cb.215723:1(90-106)Online publication date: 16-Mar-2023
https://doi.org/10.1002/cb.2157
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