Publication Type

Conference Proceeding Article

Publication Date

8-2013

Abstract

Queuing is a common phenomenon in theme parks which negatively affects visitor experience and revenue yields. There is thus a need for park operators to infer the real queuing delays without expensive investment in human effort or complex tracking infrastructure. In this paper, we depart from the classical queuing theory approach and provide a data-driven and online approach for estimating the time-varying queuing delays experienced at different attractions in a theme park. This work is novel in that it relies purely on empirical observations of the entry time of individual visitors at different attractions, and also accommodates the reality that visitors often perform other unobserved activities between moving from one attraction to the next. We solve the resulting inverse estimation problem via a modified Expectation Maximization (EM) algorithm. Experiments on data obtained from, and modeled after, a real theme park setting show that our approach converges to a fixedpoint solution quite rapidly, and is fairly accurate in identifying the per-attraction mean queuing delay, with estimation errors of 7-8% for congested attractions.

Discipline

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

Research Areas

Intelligent Systems and Decision Analytics

Publication

IEEE Conference on Automation Science and Engineering (CASE), 17-20 August 2013

First Page

776

Last Page

782

ISSN

2161-8070

Identifier

10.1109/CoASE.2013.6653930

Publisher

IEEE

City or Country

Madison, WI, USA

Copyright Owner and License

LARC

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://dx.doi.org/10.1109/TASE.2010.2040827