Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan Article Swipe
Xinning Zhu
,
Tianyue Sun
,
Yuan Hao
,
Zheng Hu
,
Jiansong Miao
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.20944/preprints201812.0056.v1
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.20944/preprints201812.0056.v1
Identifying group movement patterns of crowds and understanding group behaviors is valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile phone call detail records (CDRs). Unlike GPS data, CDRs are relatively poor in spatial resolution with low sampling rates, which makes it a big challenge to identify group members from thousands of tourists. Moreover, since touristic trips are not on a regular basis, no historical data of the specific group can be used to reduce the uncertainty of trajectories. To address such challenges, we propose a method called group movement pattern mining based on similarity (GMPMS) to discover tourist groups. To avoid large amounts of trajectory similarity measurements, snapshots of the trajectories are firstly generated to extract candidate groups containing co-occurring tourists. Then, considering that different groups may follow the same itineraries, additional traveling behavioral features are defined to identify the group members. Finally, with Hainan province as an example, we provide a number of interesting insights of travel behaviors of group tours as well as individual tours, which will be helpful for tourism planning and management.
Related Topics
Concepts
Tourism
Similarity (geometry)
Group (periodic table)
Geography
TRIPS architecture
Movement (music)
Computer science
Global Positioning System
Crowds
Phone
Big data
Data science
Data mining
Advertising
Artificial intelligence
Business
Computer security
Parallel computing
Linguistics
Chemistry
Archaeology
Organic chemistry
Philosophy
Aesthetics
Telecommunications
Image (mathematics)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints201812.0056.v1
- https://www.preprints.org/manuscript/201812.0056/v1/download
- OA Status
- green
- Cited By
- 1
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3125222150
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3125222150Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20944/preprints201812.0056.v1Digital Object Identifier
- Title
-
Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in HainanWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-12-04Full publication date if available
- Authors
-
Xinning Zhu, Tianyue Sun, Yuan Hao, Zheng Hu, Jiansong MiaoList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints201812.0056.v1Publisher landing page
- PDF URL
-
https://www.preprints.org/manuscript/201812.0056/v1/downloadDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.preprints.org/manuscript/201812.0056/v1/downloadDirect OA link when available
- Concepts
-
Tourism, Similarity (geometry), Group (periodic table), Geography, TRIPS architecture, Movement (music), Computer science, Global Positioning System, Crowds, Phone, Big data, Data science, Data mining, Advertising, Artificial intelligence, Business, Computer security, Parallel computing, Linguistics, Chemistry, Archaeology, Organic chemistry, Philosophy, Aesthetics, Telecommunications, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2018: 1Per-year citation counts (last 5 years)
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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