Transportation mode identification based on smartphone

2014 ◽  
Author(s):  
Huichao Liu ◽  
Ying Feng ◽  
Liguo Zhang
Keyword(s):  
Transportation Mode ◽  
Mode Identification

Towards Crowdsourced Transportation Mode Identification: A Semi-Supervised Federated Learning Approach

2021 ◽  
pp. 1-1
Author(s):  
Chenhan Zhang ◽  
Yuanshao Zhu ◽  
Christos Markos ◽  
Shui Yu ◽  
James J.Q. Yu
Keyword(s):  
Learning Approach ◽  
Transportation Mode ◽  
Mode Identification

scholarly journals Transportation mode identification with GPS trajectory data and GIS information

2021 ◽  
Vol 26 (4) ◽  
pp. 403-416
Author(s):  
Ji Li ◽  
Xin Pei ◽  
Xuejiao Wang ◽  
Danya Yao ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Trajectory Data ◽  
Transportation Mode ◽  
Mode Identification ◽  
Gps Trajectory Data ◽  
Gps Trajectory

scholarly journals Leveraging the Self-Transition Probability of Ordinal Patterns Transition Network for Transportation Mode Identification Based on GPS Data

2021 ◽  
Author(s):  
Isadora Cardoso P. Silva ◽  
Joao B. Borges ◽  
Pedro H. Barros ◽  
Antonio F. Loureiro ◽  
Osvaldo A. Rosso ◽  
...  
Keyword(s):  
Transition Probability ◽  
The Self ◽  
Gps Data ◽  
Transportation Mode ◽  
Mode Identification ◽  
Traffic Jams ◽  
Ordinal Patterns ◽  
Transition Network ◽  
Initial Exploration

Abstract 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.

Statistical characteristics and transportation mode identification of individual trajectories

2020 ◽  
Vol 34 (10) ◽  
pp. 2050092
Author(s):  
Zhiren Huang ◽  
Pu Wang ◽  
Yang Liu
Keyword(s):  
Human Mobility ◽  
Location Based Services ◽  
Statistical Characteristics ◽  
Trajectory Data ◽  
Single Class ◽  
Transportation Mode ◽  
Mode Identification ◽  
Transportation Modes ◽  
Gps Trajectory Data ◽  
Gps Trajectories

Entering big data era, individual GPS trajectory data have created great opportunities for human mobility and collective behavior studies. Individual GPS trajectories can be collected by location-based services on mobile phones. However, GPS data often do not record transportation modes (e.g., walking, riding a bus, or driving a car). In this study, we analyzed the statistical characteristics of individual trajectories and present a collaborative isolation forest (Co-IF) model to identify the transportation modes of mobile phone GPS trajectories. Unlike previous models that identify multiple transportation modes simultaneously, the proposed Co-IF model builds a single-class classifier for each transportation mode and then combines their results. Compared to the existing models, the Co-IF model offers competitive performance and shows improved reliability with noisy trajectories.

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