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TremBR: Exploring road networks for trajectory representation learning
Tao Yang Fu,
Wang Chien Lee
Computer Science and Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
65
Scopus citations
Overview
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Dive into the research topics of 'TremBR: Exploring road networks for trajectory representation learning'. Together they form a unique fingerprint.
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Computer Science
Representation Learning
100%
Recurrent Neural Network
50%
Prediction Time
50%
Mean Absolute Error
50%
Network Topology
25%
Neural Network
25%
Feature Vector
25%
Dimensional Feature
25%
Learning Framework
25%
Spatial Property
25%
Technical Issue
25%
Learning Process
25%
temporal property
25%
Data Preparation
25%
Matching Network
25%
Keyphrases
Road Network
100%
Road Segment
57%
Mean Absolute Error
28%
Network Topology
14%
Order of Magnitude
14%
Neural Network Model
14%
Learning Process
14%
Spatial Properties
14%
Encoder-decoder
14%
Model Design
14%
Matching Techniques
14%
Temporal Properties
14%
Learning Framework
14%
Representation Learning
14%
Learning Model
14%
Real-world Trajectory
14%
Data Preparation
14%
Trajectory Dataset
14%
Novel Representation
14%
Mean Rank
14%
Low-dimensional Feature Vector
14%
Social Sciences
Road Network
100%
Neural Network
25%
Travel Time
25%
Learning Process
12%