Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2013
Abstract
In this paper, we illustrate how massive agent-based simulation can be used to investigate an exciting new application domain of experience management in theme parks, which covers topics like congestion control, incentive design, and revenue management. Since all visitors are heterogeneous and self-interested, we argue that a high-quality agent-based simulation is necessary for studying various problems related to experience management. As in most agent-base simulations, a sound understanding of micro-level behaviors is essential to construct high-quality models. To achieve this, we designed and conducted a first-of-its-kind real-world experiment that helps us understand how typical visitors behave in a theme-park environment. From the data collected, visitor behaviors are quantified, modeled, and eventually incorporated into a massive agent-based simulation where up to 15,000 visitor agents are modeled. Finally, we demonstrate how our agent-based simulator can be used to understand the crowd build-up and the impacts of various control policies on visitor experience.
Keywords
Consumer behaviour, Digital simulation, Multi-agent systems, Travel industry
Discipline
Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering
Publication
WSC '13: Proceedings of the 2013 Winter Simulation Conference: December 8-11, 2013, Washington DC
First Page
1527
Last Page
1538
ISBN
9781479920778
Identifier
10.1109/WSC.2013.6721536
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
CHENG, Shih-Fen; LIN, Larry Junjie; DU, Jiali; LAU, Hoong Chuin; and VARAKANTHAM, Pradeep Reddy.
An Agent-based Simulation Approach to Experience Management in Theme Parks. (2013). WSC '13: Proceedings of the 2013 Winter Simulation Conference: December 8-11, 2013, Washington DC. 1527-1538.
Available at: https://ink.library.smu.edu.sg/sis_research/1828
Copyright Owner and License
LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1109/WSC.2013.6721536
Included in
Artificial Intelligence and Robotics Commons, Business Commons, Operations Research, Systems Engineering and Industrial Engineering Commons