Metropolis-Scale Resilient and Trustworthy Traffic Flow Inference Using Multi-Source Data

ArXi:2605.25004v1 Announce Type: cross Inferring network-wide traffic states from sparse observations with high accuracy and trustworthy uncertainty quantification is essential for intelligent transportation systems, yet it remains challenging due to the underdetermined nature of the problem, multifaceted disturbances in sensing networks, and the inherent conflicts among multiple inference sub-tasks when modeled jointly.