Leveraging the self-transition probability of ordinal patterns transition network for transportation mode identification based on GPS data

Analyzing people’s mobility and identifying the transportation mode is essential for cities to create travel diaries. It can help develop essential technologies to reduce traffic jams and travel time between their points, thus helping to improve the quality of life of citizens. Previous studies in t...

Full description

Saved in:
Bibliographic Details
Published inNonlinear dynamics Vol. 107; no. 1; pp. 889 - 908
Main Authors Cardoso-Pereira, Isadora, Borges, João B., Barros, Pedro H., Loureiro, Antonio F., Rosso, Osvaldo A., Ramos, Heitor S.
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 2022
Springer Nature B.V
Subjects
Automotive Engineering
Cellular telephones
Classical Mechanics
Control
Deep learning
Diaries
Dynamical Systems
Engineering
Feature extraction
Identification
Information theory
Internet of Things
Machine learning
Mechanical Engineering
Mobility
Original Paper
Spatial data
Time series
Traffic congestion
Traffic jams
Transition probabilities
Transportation
Travel time
Vibration
Mobility
Ordinal patterns transition network
Ordinal patterns
Time series
Information theory
Online AccessGet full text

Cover

More Information
Summary:Analyzing people’s mobility and identifying the transportation mode is essential for cities to create travel diaries. It can help develop essential technologies to reduce traffic jams and travel time between their points, thus helping to improve the quality of life of citizens. Previous studies in this context extracted many specialized features, reaching hundreds of them. This approach requires domain knowledge. Other strategies focused on deep learning methods, which need intense computational power and more data than traditional methods to train their models. In this work, we propose using information theory quantifiers retained from the ordinal patterns (OPs) transformation for transportation mode identification. Our proposal presents the advantage of using fewer data. OP is also computationally inexpensive and has low dimensionality. It is beneficial for scenarios where it is hard to collect information, such as Internet-of-things contexts. Our results demonstrated that OP features enhance the classification results of standard features in such scenarios.
ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-021-07059-x