TY - GEN
T1 - Optimal Control of Pedestrian Flows by Congestion Forecasts Satisfying User Equilibrium Conditions
AU - Yamada, Hiroaki
AU - Kamiyama, Naoyuki
N1 - Funding Information:
Acknowledgments. This work was supported by JST, PRESTO Grant Number JPMJPR1753, Japan, and Fujitsu Laboratories, Ltd. The authors would like to thank Hiroaki Iwashita, Takuya Ohawa, and Kotaro Ohori for the useful discussions.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Reducing congestion is one of the most important issues in theme park management. Optimization algorithms for reducing congestion in theme parks using simulation optimization methods have been proposed. In many existing methods, theme park managers directly regulate the movement of visitors. However, restricting the freedom to wander considerably reduces the visitor satisfaction. Thus, when controlling congestion in theme parks, we must consider the trade-off between reducing congestion and restricting freedom. In this paper, we propose an indirect control method for pedestrian flows using congestion forecasts and information distribution. Specifically, we propose a simulation-based heuristic algorithm for the problem of finding an optimal information distribution policy for congestion forecasts satisfying user equilibrium conditions.
AB - Reducing congestion is one of the most important issues in theme park management. Optimization algorithms for reducing congestion in theme parks using simulation optimization methods have been proposed. In many existing methods, theme park managers directly regulate the movement of visitors. However, restricting the freedom to wander considerably reduces the visitor satisfaction. Thus, when controlling congestion in theme parks, we must consider the trade-off between reducing congestion and restricting freedom. In this paper, we propose an indirect control method for pedestrian flows using congestion forecasts and information distribution. Specifically, we propose a simulation-based heuristic algorithm for the problem of finding an optimal information distribution policy for congestion forecasts satisfying user equilibrium conditions.
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U2 - 10.1007/978-3-030-69322-0_19
DO - 10.1007/978-3-030-69322-0_19
M3 - Conference contribution
AN - SCOPUS:85102745300
SN - 9783030693213
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 299
EP - 314
BT - PRIMA 2020
A2 - Uchiya, Takahiro
A2 - Bai, Quan
A2 - Marsá Maestre, Iván
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020
Y2 - 18 November 2020 through 20 November 2020
ER -