Exploring User Semantic Annotation from Trajectories in the Scenario of Shared Locations Article Swipe
Jens Golze
,
Monika Sester
,
Udo Feuerhake
,
Claus Brenner
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/agile-giss-5-25-2024
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/agile-giss-5-25-2024
Over the past decade(s), collecting spatiotemporal data has become easier due to technological advancements and more user-friendly collection processes. Additionally, government agencies, companies, and open data projects have made general environmental data, such as points of interest or land use coverage, more freely available. Scientific studies can combine this spatiotemporal and non-spatial data to analyze different types of human mobility data. The results of these studies are relevant to transportation and urban planning, as similar information is typically collected by means of surveys. However, deriving relevant information from Global Navigation Satellite System (GNSS) trajectories remains challenging due to inaccuracies in the positioning and the unavailability of groundtruth information regarding individual user location semantics (e.g. home place, work place, leisure place or others). This work presents a semantic location annotation approach based on a Hidden Markov Model and the Viterbi optimization algorithm. The model includes location emissions to account for the general usage of a particular location. The annotations are applied to the clustered stop points that identify regions of special interest to individual users in a trajectory data set. The proposed approach demonstrates that the adapted Viterbi optimization can assign the most probable and meaningful semantic labels to the user’s sequences and provides insights on the underlying regions of special interest.
Related Topics
Concepts
Unavailability
Computer science
Semantics (computer science)
Point of interest
Information retrieval
Annotation
Data science
Data mining
GNSS applications
Set (abstract data type)
Hidden Markov model
Semantic heterogeneity
Global Positioning System
Artificial intelligence
Semantic Web
Ontology-based data integration
Telecommunications
Engineering
Programming language
Reliability engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/agile-giss-5-25-2024
- https://agile-giss.copernicus.org/articles/5/25/2024/agile-giss-5-25-2024.pdf
- OA Status
- diamond
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399177346
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4399177346Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/agile-giss-5-25-2024Digital Object Identifier
- Title
-
Exploring User Semantic Annotation from Trajectories in the Scenario of Shared LocationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-30Full publication date if available
- Authors
-
Jens Golze, Monika Sester, Udo Feuerhake, Claus BrennerList of authors in order
- Landing page
-
https://doi.org/10.5194/agile-giss-5-25-2024Publisher landing page
- PDF URL
-
https://agile-giss.copernicus.org/articles/5/25/2024/agile-giss-5-25-2024.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://agile-giss.copernicus.org/articles/5/25/2024/agile-giss-5-25-2024.pdfDirect OA link when available
- Concepts
-
Unavailability, Computer science, Semantics (computer science), Point of interest, Information retrieval, Annotation, Data science, Data mining, GNSS applications, Set (abstract data type), Hidden Markov model, Semantic heterogeneity, Global Positioning System, Artificial intelligence, Semantic Web, Ontology-based data integration, Telecommunications, Engineering, Programming language, Reliability engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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