Chapter
Apr 26, 2012
Applying Locally Linear Embedding on Feature Extraction of Traffic Flow Data
Authors: Yenan Chen [email protected], Jianming Hu [email protected], Yi Zhang, and Di LiAuthor Affiliations
Publication: Traffic and Transportation Studies 2010
Abstract
We encounter high dimensional data when using time series vector to describe the traffic flow of a certain link during some time period, or using flow data from different links to describe the traffic status of a region at a certain time point. This paper applies a dimensionality reduction method, named Locally Linear Embedding (LLE) to extract temporal and spatial features out of these high dimensional traffic flow data. LLE can visualize our data in a low dimension space, thus giving a vivid perspective on the emerging features. According to these features, we can put links into different clusters and better interpret the evolution of traffic patterns. Furthermore, comparison between linear dimensionality reduction method, PCA and LLE is carried out. The result shows that LLE has better performance.
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© 2010 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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ASCE Technical Topics:
- Analysis (by type)
- Comparative studies
- Data collection
- Engineering fundamentals
- Infrastructure
- Linear functions
- Mathematical functions
- Mathematics
- Methodology (by type)
- Research methods (by type)
- Spatial analysis
- Spatial data
- Statistics
- Time series analysis
- Traffic engineering
- Traffic flow
- Traffic management
- Transportation engineering
- Vector analysis
Authors
Affiliations
Department of Automation, Tsinghua University, Haidian District, Beijing 100084, China. E-mail: [email protected]
Associate Professor, Tsinghua University, Haidian District, Beijing 100084, China. E-mail: [email protected]
Yi Zhang
No affiliation information available.
Di Li
No affiliation information available.
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