research-article

A Mobility Model of Theme Park Visitors

Authors:

Mustafa Ilhan Akbas,

Damla TurgutAuthors Info & Claims

IEEE Transactions on Mobile Computing, Volume 14, Issue 12

Pages 2406 - 2418

https://doi.org/10.1109/TMC.2015.2400454

Published: 01 December 2015 Publication History

Abstract

Realistic human mobility modeling is critical for accurate performance evaluation of mobile wireless networks. Movements of visitors in theme parks affect the performance of systems which are designed for various purposes including urban sensing and crowd management. Previously proposed human mobility models are mostly generic while some of them focus on daily movements of people in urban areas. Theme parks, however, have unique characteristics in terms of very limited use of vehicles, crowd's social behavior, and attractions. Human mobility is strongly tied to the locations of attractions and is synchronized with major entertainment events. Hence, realistic human mobility models must be developed with the specific scenario in mind. In this paper, we present a novel model for human mobility in theme parks. In our model, the nondeterminism of movement decisions of visitors is combined with deterministic behavior of attractions in a theme park. The attractions are categorized as rides, restaurants, and live shows. The time spent at these attractions are computed using queueing-theoretic models. The realism of the model is evaluated through extensive simulations and compared with the mobility models SLAW, RWP and the GPS traces of theme park visitors. The results show that our proposed model provides a better match to the real-world data compared to the existing models.

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cover image IEEE Transactions on Mobile Computing

IEEE Transactions on Mobile Computing Volume 14, Issue 12

Dec. 2015

199 pages

ISSN:1536-1233

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Copyright © 2015.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 December 2015

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Cited By

Yan XTong DChen QZhou WXu Y(2019)Adaptive State Estimation of Stochastic Delayed Neural Networks with Fractional Brownian MotionNeural Processing Letters10.1007/s11063-018-9960-z50:2(2007-2020)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s11063-018-9960-z
Vasconcelos VAmazonas MCastro TRodrigues RFuks HVega KGadelha B(2019)Redefining Audience Role in Live PerformancesHuman-Computer Interaction. Perspectives on Design10.1007/978-3-030-22646-6_26(358-377)Online publication date: 26-Jul-2019
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https://dl.acm.org/doi/10.1145/3274192.3274233
Steptoe MKrüger RGarcia RLiang XMaciejewski R(2018)A Visual Analytics Framework for Exploring Theme Park DynamicsACM Transactions on Interactive Intelligent Systems10.1145/31620768:1(1-27)Online publication date: 20-Feb-2018
https://dl.acm.org/doi/10.1145/3162076
Al-Turjman F(2018)Modelling Green Femtocells in Smart-gridsMobile Networks and Applications10.1007/s11036-017-0963-123:4(940-955)Online publication date: 1-Aug-2018
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Pappalardo LSimini F(2018)Data-driven generation of spatio-temporal routines in human mobilityData Mining and Knowledge Discovery10.1007/s10618-017-0548-432:3(787-829)Online publication date: 1-May-2018
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Solmaz GTurgut D(2017)Modeling pedestrian mobility in disaster areasPervasive and Mobile Computing10.1016/j.pmcj.2017.05.00540:C(104-122)Online publication date: 1-Sep-2017
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Papadopoulos GKotsiou VGallais AChatzimisios PNoel T(2016)Low-power neighbor discovery for mobility-aware wireless sensor networksAd Hoc Networks10.1016/j.adhoc.2016.05.01148:C(66-79)Online publication date: 15-Sep-2016
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Medrano-Chávez APérez-Cortés ELopez-Guerrero MNotare MRumín ÁLopez-Guerrero M(2015)Studying the Effect of Human Mobility on MANET Topology and RoutingProceedings of the 13th ACM International Symposium on Mobility Management and Wireless Access10.1145/2810362.2810370(39-46)Online publication date: 2-Nov-2015
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