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A Method for Extracting Travel Patterns Using Data Polishing
Mio Hosoe
Tottori University: Tottori Daigaku
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7728-1010
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Journal of Big Data.
With the development of ICT (Information and Communication Technology), interest in using the large amount of accumulated data for traffic policy planning has been increasing. In recent years, data polishing has been proposed as a new methodology for big data analysis. Data polishing is one of the graphical clustering methods. This method can be used to extract patterns that are similar or related to each other by clarifying the cluster structures in the data. The purpose of this study is to reveal travel patterns of railway passengers by applying data polishing to smart card data collected in Kagawa Prefecture, Japan. This study uses 9,008,709 data points collected during the 15 months from December 1st, 2013 to February 28th, 2015. This data set includes such information as trip histories and types of passengers. The study uses the data polishing method to cluster 4,667,520 combinations of information about individual rides: day of the week, time of day, passenger type, origin station, and destination station. As a result, 127 characteristic travel patterns were specified from those combinations.
Keywords
public transport, smart card, high order data
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CURRENT STATUS: Published
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Posted 28 Sep, 2020
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On 07 Jan, 2021
No community comments so far
Editorial decision: Major Revision
On 09 Nov, 2020
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Received 08 Nov, 2020
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Received 03 Nov, 2020
Reviewer #9 agreed
On 29 Oct, 2020
Reviewer #8 agreed
On 26 Oct, 2020
Reviewer #6 agreed
On 26 Oct, 2020
Reviewer #7 agreed
On 26 Oct, 2020
Review #7 received
Received 26 Oct, 2020
Review #6 received
Received 26 Oct, 2020
Review #8 received
Received 25 Oct, 2020
Review #1 received
Received 25 Oct, 2020
Review #2 received
Received 24 Oct, 2020
Reviewer #5 agreed
On 24 Oct, 2020
Reviewer #1 agreed
On 23 Oct, 2020
Reviewer #2 agreed
On 23 Oct, 2020
Reviewer #3 agreed
On 23 Oct, 2020
Reviewer #4 agreed
On 23 Oct, 2020
Reviewers invited
Invitations sent on 22 Oct, 2020
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On 23 Sep, 2020
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On 22 Sep, 2020
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This preprint is under consideration at Journal of Big Data. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
A Method for Extracting Travel Patterns Using Data Polishing
Mio Hosoe
Tottori University: Tottori Daigaku
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7728-1010
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 1
Posted 28 Sep, 2020
Published
On 07 Jan, 2021
No community comments so far
Editorial decision: Major Revision
On 09 Nov, 2020
Review #4 received
Received 08 Nov, 2020
Review #5 received
Received 05 Nov, 2020
Review #3 received
Received 03 Nov, 2020
Reviewer #9 agreed
On 29 Oct, 2020
Reviewer #8 agreed
On 26 Oct, 2020
Reviewer #6 agreed
On 26 Oct, 2020
Reviewer #7 agreed
On 26 Oct, 2020
Review #7 received
Received 26 Oct, 2020
Review #6 received
Received 26 Oct, 2020
Review #8 received
Received 25 Oct, 2020
Review #1 received
Received 25 Oct, 2020
Review #2 received
Received 24 Oct, 2020
Reviewer #5 agreed
On 24 Oct, 2020
Reviewer #1 agreed
On 23 Oct, 2020
Reviewer #2 agreed
On 23 Oct, 2020
Reviewer #3 agreed
On 23 Oct, 2020
Reviewer #4 agreed
On 23 Oct, 2020
Reviewers invited
Invitations sent on 22 Oct, 2020
Editor assigned
On 23 Sep, 2020
First submitted
On 22 Sep, 2020
Submission checks complete
On 22 Sep, 2020
Editor invited
On 22 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Technical Communication
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Journal of Big Data.
With the development of ICT (Information and Communication Technology), interest in using the large amount of accumulated data for traffic policy planning has been increasing. In recent years, data polishing has been proposed as a new methodology for big data analysis. Data polishing is one of the graphical clustering methods. This method can be used to extract patterns that are similar or related to each other by clarifying the cluster structures in the data. The purpose of this study is to reveal travel patterns of railway passengers by applying data polishing to smart card data collected in Kagawa Prefecture, Japan. This study uses 9,008,709 data points collected during the 15 months from December 1st, 2013 to February 28th, 2015. This data set includes such information as trip histories and types of passengers. The study uses the data polishing method to cluster 4,667,520 combinations of information about individual rides: day of the week, time of day, passenger type, origin station, and destination station. As a result, 127 characteristic travel patterns were specified from those combinations.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.