# Probabilistic Traffic Forecasting

Probabilistic traffic forecasting has relied on multi-step diffusion sampling, which is slow and often trails deterministic baselines on point accuracy. CoReFlow learns the flow map, the solution operator of the probability-flow ODE, directly and relaxes trajectory consistency through the conditional mean, so a single forward pass produces the full forecast. On four real road-network datasets, PEMS03, PEMS04, PEMS07 and PEMS08, it ranks first in 11 of 12 deterministic metric cases and takes all 14 reported comparisons against generative baselines on PEMS07 and PEMS08.

Probabilistic traffic forecasting has relied on multi-step diffusion sampling, which is slow and often trails deterministic baselines on point accuracy. CoReFlow learns the flow map, the solution operator of the probability-flow ODE, directly and relaxes trajectory consistency through the conditional mean, so a single forward pass produces the full forecast. On four real road-network datasets, PEMS03, PEMS04, PEMS07 and PEMS08, it ranks first in 11 of 12 deterministic metric cases and takes all 14 reported comparisons against generative baselines on PEMS07 and PEMS08.