ReviewSensors (Basel, Switzerland)2018
A Review of GPS Trajectories Classification Based on Transportation Mode.
Review in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Estimation of trip purposes in public transport during the COVID-19 pandemic: The case of Santiago, Chile.Journal of transport geography · 2023Article
- A Study on the Geometric and Kinematic Descriptors of Trajectories in the Classification of Ship Types.Sensors (Basel, Switzerland) · 2022Article
- Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl.Movement ecology · 2022Article
- On the Challenges and Potential of Using Barometric Sensors to Track Human Activity.Sensors (Basel, Switzerland) · 2020Review
- Magnetic Field Gradient-Based EKF for Velocity Estimation in Indoor Navigation.Sensors (Basel, Switzerland) · 2020Article
- GPS Trajectory Completion Using End-to-End Bidirectional Convolutional Recurrent Encoder-Decoder Architecture with Attention Mechanism.Sensors (Basel, Switzerland) · 2020Article
- Feature-First Add-On for Trajectory Simplification in Lifelog Applications.Sensors (Basel, Switzerland) · 2020Article
- Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
GPS trajectories generated by moving objects provide researchers with an excellent resource for revealing patterns of human activities. Relevant research based on GPS trajectories includes the fields of location-based services, transportation science, and urban studies among others. Research relating to how to obtain GPS data (e.g., GPS data acquisition, GPS data processing) is receiving significant attention because of the availability of GPS data collecting platforms. One such problem is the GPS data classification based on transportation mode. The challenge of classifying trajectories by transportation mode has approached detecting different modes of movement through the application of several strategies. From a GPS data acquisition point of view, this paper macroscopically classifies the transportation mode of GPS data into single-mode and mixed-mode. That means GPS trajectories collected based on one type of transportation mode are regarded as single-mode data; otherwise it is considered as mixed-mode data. The one big difference of classification strategy between single-mode and mixed-mode GPS data is whether we need to recognize the transition points or activity episodes first. Based on this, we systematically review existing classification methods for single-mode and mixed-mode GPS data and introduce the contributions of these methods as well as discuss their unresolved issues to provide directions for future studies in this field. Based on this review and the transportation application at hand, researchers can select the most appropriate method and endeavor to improve them.
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Registered trials
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