Revealing representative day-types in transport networks using traffic data clustering

Revealing representative day-types in transport networks using traffic data clustering

Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to...

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Bibliographic Details
Main Authors: Burghout, W. (Author), Cebecauer, M. (Author), Gundlegård, D. (Author), Jenelius, E. (Author)
Format: Article
Language:English
Published: Taylor and Francis Ltd. 2023
Subjects:
Cluster analysis
Cluster validity
clustering
Clusterings
day clustering
Day clustering
dimensionality reduction
Dimensionality reduction
External index
external indices
Forecasting
Information management
Intelligent systems
Internal index
internal indices
K-means clustering
Learning systems
network-wide
Network-wide
Pattern recognition
prediction
Reduction
Representative days
Scalability
Short term prediction
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