Estimating Spatial Mean Speeds from Local Sensors: A Machine-Learning Approach

Accurate network macroscopic fundamental diagram (NMFD) estimation requires accurate spatial mean speed estimates that are hard to get from loop detector devices (LDD) as they capture local speeds only. This study introduces a correction method able to reconstruct the mean speed from loop data leveraging floating car devices (FCD) during the training phase. This significantly improves LDD-based NMFD estimation. Unlike previous studies focusing on local speed corrections, our approach integrates