tongnie/tensorlib

Repository for paper 'Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns'.

37
/ 100
Emerging

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.

transportation-planning traffic-analysis urban-mobility data-imputation spatiotemporal-data
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 15 / 25

How are scores calculated?

Stars

16

Forks

4

Language

Python

License

MIT

Last pushed

Jun 09, 2023

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/tongnie/tensorlib"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.