tongnie/tensorlib
Repository for paper 'Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns'.
This helps transportation researchers and urban planners fill in missing information within their spatiotemporal traffic datasets. You feed it incomplete historical traffic data, organized as a tensor (time intervals x locations x days), and it outputs a complete, estimated dataset. It's designed for professionals who need to analyze full traffic patterns despite gaps caused by sensor malfunctions or network issues.
No commits in the last 6 months.
Use this if you need to accurately recover missing values in your historical traffic flow or speed data, especially when facing complex missing patterns like entire sensor outages or daily downtime.
Not ideal if you are looking for real-time traffic data prediction or imputation, as this tool is specifically for off-line analysis of historical data.
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16
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Language
Python
License
MIT
Category
Last pushed
Jun 09, 2023
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