A Model for Selection of Attractions in Theme Park and Estimation of Model Parameters Article Swipe
Hitoshi Shimizu
,
Tatsushi Matsubayashi
,
Futoshi Naya
,
Hiroshi Sawada
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1527/tjsai.wd-b
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1527/tjsai.wd-b
The theme park problem is a platform where methods of guiding visitors to relieve congestion are developed and evaluated by reproducing a crowded theme park on a computer. In the theme park problem, the attraction selection model is an important element in the simulator. In previous studies, multinomial logit model was mainly used for attraction selection. However, when we observed real amusement parks, we found that we can not reproduce the characteristics of waiting time of real attraction by this model. In this research, we propose a multinomial linear model as a model of attraction selection. This model can express the rational behavior of visitors that waits for a while when waiting times of all attractions are too long for them. We showed that this model can reproduce characteristics of waiting time using multiagent simulator (MAS). We also developed a method to estimate the parameters of the proposed model from the aggregated data of the output of MAS. As a result of numerical experiments, it was confirmed that the performance of the parameter estimation was good. The proposed model and method for parameter estimation can be applied not only to the theme park problem but also to various problems related to human behavior of selection.
Related Topics
Concepts
Theme park
Computer science
Theme (computing)
Selection (genetic algorithm)
Attraction
Multinomial logistic regression
Estimation
Amusement
Model selection
Artificial intelligence
Operations research
Mathematical optimization
Simulation
Machine learning
Mathematics
Tourism
Geography
Operating system
Archaeology
Linguistics
Philosophy
Psychotherapist
Economics
Psychology
Management
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1527/tjsai.wd-b
- https://www.jstage.jst.go.jp/article/tjsai/34/5/34_wd-B/_pdf
- OA Status
- diamond
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2970287762
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2970287762Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1527/tjsai.wd-bDigital Object Identifier
- Title
-
A Model for Selection of Attractions in Theme Park and Estimation of Model ParametersWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-08-31Full publication date if available
- Authors
-
Hitoshi Shimizu, Tatsushi Matsubayashi, Futoshi Naya, Hiroshi SawadaList of authors in order
- Landing page
-
https://doi.org/10.1527/tjsai.wd-bPublisher landing page
- PDF URL
-
https://www.jstage.jst.go.jp/article/tjsai/34/5/34_wd-B/_pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.jstage.jst.go.jp/article/tjsai/34/5/34_wd-B/_pdfDirect OA link when available
- Concepts
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Theme park, Computer science, Theme (computing), Selection (genetic algorithm), Attraction, Multinomial logistic regression, Estimation, Amusement, Model selection, Artificial intelligence, Operations research, Mathematical optimization, Simulation, Machine learning, Mathematics, Tourism, Geography, Operating system, Archaeology, Linguistics, Philosophy, Psychotherapist, Economics, Psychology, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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11Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.it | 164 |
| abstract_inverted_index.of | 9, 72, 75, 93, 103, 113, 129, 145, 153, 156, 161, 170, 203 |
| abstract_inverted_index.on | 25 |
| abstract_inverted_index.to | 12, 141, 189, 196, 200 |
| abstract_inverted_index.we | 58, 63, 66, 84 |
| abstract_inverted_index.The | 0, 176 |
| abstract_inverted_index.all | 114 |
| abstract_inverted_index.and | 17, 179 |
| abstract_inverted_index.are | 15, 116 |
| abstract_inverted_index.but | 194 |
| abstract_inverted_index.can | 67, 98, 126, 184 |
| abstract_inverted_index.for | 53, 107, 119, 181 |
| abstract_inverted_index.not | 68, 187 |
| abstract_inverted_index.the | 29, 33, 42, 70, 100, 143, 146, 150, 154, 168, 171, 190 |
| abstract_inverted_index.too | 117 |
| abstract_inverted_index.was | 50, 165, 174 |
| abstract_inverted_index.MAS. | 157 |
| abstract_inverted_index.This | 96 |
| abstract_inverted_index.also | 137, 195 |
| abstract_inverted_index.data | 152 |
| abstract_inverted_index.from | 149 |
| abstract_inverted_index.long | 118 |
| abstract_inverted_index.only | 188 |
| abstract_inverted_index.park | 2, 24, 31, 192 |
| abstract_inverted_index.real | 60, 76 |
| abstract_inverted_index.that | 65, 105, 123, 167 |
| abstract_inverted_index.this | 79, 82, 124 |
| abstract_inverted_index.time | 74, 131 |
| abstract_inverted_index.used | 52 |
| abstract_inverted_index.when | 57, 110 |
| abstract_inverted_index.found | 64 |
| abstract_inverted_index.good. | 175 |
| abstract_inverted_index.human | 201 |
| abstract_inverted_index.logit | 48 |
| abstract_inverted_index.model | 36, 49, 89, 92, 97, 125, 148, 178 |
| abstract_inverted_index.them. | 120 |
| abstract_inverted_index.theme | 1, 23, 30, 191 |
| abstract_inverted_index.times | 112 |
| abstract_inverted_index.using | 132 |
| abstract_inverted_index.waits | 106 |
| abstract_inverted_index.where | 7 |
| abstract_inverted_index.while | 109 |
| abstract_inverted_index.(MAS). | 135 |
| abstract_inverted_index.linear | 88 |
| abstract_inverted_index.mainly | 51 |
| abstract_inverted_index.method | 140, 180 |
| abstract_inverted_index.model. | 80 |
| abstract_inverted_index.output | 155 |
| abstract_inverted_index.parks, | 62 |
| abstract_inverted_index.result | 160 |
| abstract_inverted_index.showed | 122 |
| abstract_inverted_index.applied | 186 |
| abstract_inverted_index.crowded | 22 |
| abstract_inverted_index.element | 40 |
| abstract_inverted_index.express | 99 |
| abstract_inverted_index.guiding | 10 |
| abstract_inverted_index.methods | 8 |
| abstract_inverted_index.problem | 3, 193 |
| abstract_inverted_index.propose | 85 |
| abstract_inverted_index.related | 199 |
| abstract_inverted_index.relieve | 13 |
| abstract_inverted_index.various | 197 |
| abstract_inverted_index.waiting | 73, 111, 130 |
| abstract_inverted_index.However, | 56 |
| abstract_inverted_index.behavior | 102, 202 |
| abstract_inverted_index.estimate | 142 |
| abstract_inverted_index.observed | 59 |
| abstract_inverted_index.platform | 6 |
| abstract_inverted_index.previous | 45 |
| abstract_inverted_index.problem, | 32 |
| abstract_inverted_index.problems | 198 |
| abstract_inverted_index.proposed | 147, 177 |
| abstract_inverted_index.rational | 101 |
| abstract_inverted_index.studies, | 46 |
| abstract_inverted_index.visitors | 11, 104 |
| abstract_inverted_index.amusement | 61 |
| abstract_inverted_index.computer. | 27 |
| abstract_inverted_index.confirmed | 166 |
| abstract_inverted_index.developed | 16, 138 |
| abstract_inverted_index.evaluated | 18 |
| abstract_inverted_index.important | 39 |
| abstract_inverted_index.numerical | 162 |
| abstract_inverted_index.parameter | 172, 182 |
| abstract_inverted_index.reproduce | 69, 127 |
| abstract_inverted_index.research, | 83 |
| abstract_inverted_index.selection | 35 |
| abstract_inverted_index.simulator | 134 |
| abstract_inverted_index.aggregated | 151 |
| abstract_inverted_index.attraction | 34, 54, 77, 94 |
| abstract_inverted_index.congestion | 14 |
| abstract_inverted_index.estimation | 173, 183 |
| abstract_inverted_index.multiagent | 133 |
| abstract_inverted_index.parameters | 144 |
| abstract_inverted_index.selection. | 55, 95, 204 |
| abstract_inverted_index.simulator. | 43 |
| abstract_inverted_index.attractions | 115 |
| abstract_inverted_index.multinomial | 47, 87 |
| abstract_inverted_index.performance | 169 |
| abstract_inverted_index.reproducing | 20 |
| abstract_inverted_index.experiments, | 163 |
| abstract_inverted_index.characteristics | 71, 128 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.16270264 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |