Temporal Transfer Learning selects source tasks for zero-shot transfer of RL policies to solve a range of coarse-grained advisory autonomy hold durations in traffic optimization more reliably than baselines.
Dynamical model of traffic congestion and numerical simulation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
NOVA applies symbolic regression to 4.7 million NGSIM observations to identify a two-term car-following model (RMSE 1.376 m/s²) and a lane-change model (67.4% balanced accuracy) that outperform recent baselines and transfer zero-shot between sites.
citing papers explorer
-
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy
Temporal Transfer Learning selects source tasks for zero-shot transfer of RL policies to solve a range of coarse-grained advisory autonomy hold durations in traffic optimization more reliably than baselines.
-
NOVA: Symbolic Regression Discovery of Interpretable Car-Following and Lane-Change Models with Driver Heterogeneity
NOVA applies symbolic regression to 4.7 million NGSIM observations to identify a two-term car-following model (RMSE 1.376 m/s²) and a lane-change model (67.4% balanced accuracy) that outperform recent baselines and transfer zero-shot between sites.