Freeway speed forecasting with sparse sensors using graph networks
In brief
- GCRN+ predicts speeds on 4,127 highway links using only 15.6% of sensors.
- The model merges interpolation and graph networks to cover unsensored areas.
- Accuracy improved: RMSE of 2.21 mph vs. 3.23 mph for historical average.
Researchers developed a method to estimate real-time and predict speeds across an entire highway network using limited sensors. Their model, GCRN+, combines data interpolation and deep learning to cover areas without sensors.
What it changes for you
- Transport & LogisticsRoad network managers can optimize signage and traffic management even on segments without sensors.
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