Publication Type

Conference Proceeding Article

Publication Date

5-2014

Abstract

In this work, we focus on the novel application of learning the diffusion dynamics of visitors among attractions at a large theme park using only aggregate information about waiting times at attractions. Main contributions include formulating optimisation models to compute diffusion dynamics. We also developed algorithm capable of dealing with noise in the data to populate parameters in the optimization model. We validated our approach using cross validation on a real theme park data set. Our approach provides an accuracy of about 80$% for popular attractions, providing solid empirical support for our diffusion models.

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics

Publication

AAMAS '14: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems: May 5-9, 2014, Paris

First Page

1501

Last Page

1502

ISBN

9781450327381

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

City or Country

Richland, SC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dl.acm.org/citation.cfm?id=2615731.2616032

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