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Research Article
Isadora Cardoso P. Silva
Federal University of Minas Gerais: Universidade Federal de Minas Gerais
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7681-7653
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This work is licensed under a CC BY 4.0 License. Read Full License
Analyzing people mobility and identifying the transportation mode used by them is essential for cities that want to reduce traffic jams and travel time between their points, thus helping to improve the quality of life of citizens. Mining this type of data, however, faces several complexities due to its unique properties. In this work, we propose the use of Information Theory quantifiers retained from the Ordinal Patterns (OP) transformation, for transportation mode identification. As an initial exploration, our results show that OP satisfactorily characterizes the trajectories. Moreover, in this scenario, the characteristics of OP transformation can be advantageous, such as its simplicity, robustness, and speed.
Keywords
Ordinal Patterns, Ordinal Patterns Transition Network, Time Series, Information Theory, Mobility
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CURRENT STATUS: Under Review
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Posted 06 Apr, 2021
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Received 03 Apr, 2021
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A new preprint design is coming!
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This preprint is under consideration at Nonlinear Dynamics. 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 Article
Isadora Cardoso P. Silva
Federal University of Minas Gerais: Universidade Federal de Minas Gerais
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7681-7653
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: Under Review
Version 1
Posted 06 Apr, 2021
No community comments so far
Reviews received
Received 03 Apr, 2021
Reviewers invited
Invitations sent on 03 Apr, 2021
Editor assigned
On 19 Mar, 2021
First submitted
On 19 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Electronic Materials and Devices
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Analyzing people mobility and identifying the transportation mode used by them is essential for cities that want to reduce traffic jams and travel time between their points, thus helping to improve the quality of life of citizens. Mining this type of data, however, faces several complexities due to its unique properties. In this work, we propose the use of Information Theory quantifiers retained from the Ordinal Patterns (OP) transformation, for transportation mode identification. As an initial exploration, our results show that OP satisfactorily characterizes the trajectories. Moreover, in this scenario, the characteristics of OP transformation can be advantageous, such as its simplicity, robustness, and speed.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
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