juyongjiang/TrendGCN
[CIKM 2023] This is the official source code of "TrendGCN: Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting" based on Pytorch.
This tool helps traffic management professionals predict future traffic conditions more accurately. By analyzing historical traffic sensor data, it generates forecasts for traffic flow and speed. The output can be used by urban planners and transportation authorities to anticipate congestion, optimize signal timing, and make informed decisions about infrastructure.
No commits in the last 6 months.
Use this if you need robust and accurate short-term to medium-term traffic forecasts (up to 12 hours) to manage urban traffic networks.
Not ideal if you require real-time, instantaneous traffic anomaly detection or very long-term strategic planning beyond daily operations.
Stars
50
Forks
2
Language
Python
License
MIT
Category
Last pushed
Aug 11, 2023
Commits (30d)
0
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