Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

12-2015

Abstract

Crowd simulation is a well-studied topic, yet it usually focuses on visualization. In this paper, we study a special class of crowd simulation, where individual agents have diverse backgrounds, ad hoc objectives, and non-repeating visits. Such crowd simulation is particularly useful when modeling human agents movement in leisure settings such as visiting museums or theme parks. In these settings, we are interested in accurately estimating aggregate crowd-related movement statistics. As comprehensive monitoring is usually not feasible for a large crowd, we propose to conduct mobility surveys on only a small group of sampled individuals. We demonstrate via simulation that we can effectively predict agents’ aggregate behaviors, even when the agent types are uncertain, and the sampling rate is as low as 1%. Our findings concur with prior studies in urban transportation, and show that sampled-based mobility survey would be a promising approach for improving the accuracy of crowd simulations.

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Decision Analytics

Publication

Proceedings of the 2015 Winter Simulation Conference: Huntington Beach, CA, December 6-9, 2015

First Page

139

Last Page

150

ISBN

9781467397438

Identifier

10.1109/WSC.2015.7408159

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

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://doi.org/10.1109/WSC.2015.7408159

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