<div class="csl-bib-body">
<div class="csl-entry">Lemmel, J., Babaiee, Z., Kleinlehner, M., Majic, I., Neubauer, P., Scholz, J., Grosu, R., & Neubauer, S. (2022). Deep-Learning vs Regression: Prediction of Tourism Flow with Limited Data. In <i>Schedule - IJCAI’22 Workshop. AI4TS: AI for Time Series Analysis</i>. IJCAI’22 Workshop - AI4TS: AI for Time Series Analysis, Vienna, Austria. IJCAI. https://doi.org/10.34726/4262</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/177468
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dc.identifier.uri
https://doi.org/10.34726/4262
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dc.description.abstract
Modern tourism in the 21st century is facing numerous challenges. One of these challenges is the rapidly growing number of tourists in space limited regions such as historical city centers, museums or geographical bottlenecks like narrow valleys. In this context, a proper and accurate prediction of tourism volume and tourism flow within a certain area is important and critical for visitor management tasks such as visitor flow control and prevention of overcrowding. Static flow control methods like limiting access to hotspots or using conventional low level controllers could not solve the problem yet. In this paper, we empirically evaluate the performance of several state-of-the-art deep-learning methods in the field of visitor flow prediction with limited data by using available granular data supplied by a tourism region and comparing the results to ARIMA, a classical statistical method. Our results show that deep-learning models yield better predictions compared to the ARIMA method, while both featuring faster inference times and being able to incorporate additional input features.
en
dc.language.iso
en
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dc.relation.isversionof
https://doi.org/10.48550/arXiv.2206.13274
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
Computer Science
en
dc.subject
Machine Learning
en
dc.subject
Statistics
en
dc.subject
Applications
en
dc.title
Deep-Learning vs Regression: Prediction of Tourism Flow with Limited Data
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.identifier.doi
10.34726/4262
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dc.contributor.affiliation
Datenvorsprung GmbH, Austria
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dc.contributor.affiliation
Graz University of Technology, Austria
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dc.contributor.affiliation
Datenvorsprung GmbH, Austria
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dc.contributor.affiliation
Graz University of Technology, Austria
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dcterms.dateSubmitted
2022
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dc.rights.holder
DatenVorsprung GmbH
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Schedule - IJCAI'22 Workshop. AI4TS: AI for Time Series Analysis
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tuw.peerreviewed
true
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tuw.relation.publisher
IJCAI
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tuw.researchTopic.id
A2a
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tuw.researchTopic.id
C6
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tuw.researchTopic.name
Urban and Regional Transformation
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tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.value
50
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tuw.researchTopic.value
50
-
tuw.linking
https://ijcai-22.org/
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tuw.publication.orgunit
E191-01 - Forschungsbereich Cyber-Physical Systems
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dc.identifier.libraryid
AC17204521
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dc.description.numberOfPages
7
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tuw.author.orcid
0000-0002-3517-2860
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tuw.author.orcid
0000-0002-8219-005X
-
tuw.author.orcid
0000-0002-0834-3791
-
tuw.author.orcid
0000-0002-8745-8190
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tuw.author.orcid
0000-0001-5715-2142
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dc.rights.identifier
CC BY 4.0
de
dc.rights.identifier
CC BY 4.0
en
tuw.event.name
IJCAI'22 Workshop - AI4TS: AI for Time Series Analysis
en
dc.description.sponsorshipexternal
Austrian Research Promotion Agency (FFG)
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dc.description.sponsorshipexternal
Austrian Science Fund (FWF)
-
dc.relation.grantnoexternal
FO999887513
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dc.relation.grantnoexternal
W1255-N23
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tuw.event.startdate
24-07-2022
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tuw.event.enddate
24-07-2022
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tuw.event.online
Hybrid
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tuw.event.type
Event for scientific audience
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tuw.event.place
Vienna
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tuw.event.country
AT
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tuw.event.presenter
Lemmel, Julian
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tuw.event.track
Multi Track
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wb.sciencebranch
Sonstige und interdisziplinäre Geowissenschaften
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wb.sciencebranch
Informatik
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1059
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.oefos
1010
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wb.sciencebranch.value
10
-
wb.sciencebranch.value
80
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wb.sciencebranch.value
10
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item.openaccessfulltext
Open Access
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item.cerifentitytype
Publications
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item.languageiso639-1
en
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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item.grantfulltext
open
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item.fulltext
with Fulltext
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item.mimetype
application/pdf
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item.openairetype
conference paper
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crisitem.author.dept
E191-01 - Forschungsbereich Cyber-Physical Systems
-
crisitem.author.dept
E191-01 - Forschungsbereich Cyber-Physical Systems
-
crisitem.author.dept
Datenvorsprung GmbH, Austria
-
crisitem.author.dept
Graz University of Technology, Austria
-
crisitem.author.dept
Datenvorsprung GmbH, Austria
-
crisitem.author.dept
E127 - Institut für Geoinformation und Kartographie
-
crisitem.author.dept
E191-01 - Forschungsbereich Cyber-Physical Systems
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crisitem.author.dept
E192-03 - Forschungsbereich Knowledge Based Systems