Infrastructure-based LiDAR sensors are emerging as a key enabler for intelligent traffic management and digital road-operation platforms. Unlike camera-based systems, roadside LiDAR can detect, classify, and track vulnerable road users and vehicles while maintaining a privacy-conform operational profile, making it particularly suitable for urban environments with high pedestrian activity. This paper argues that LiDAR-derived object trajectories can support two complementary functions: first, predictive analytics for proactive and automated traffic operations, including crossing-intention estimation, adaptive control, and safety warnings; and second, digital platforms for data fusion, remote monitoring, and automated maintenance workflows. Evidence from recent research at the Mobility Innovation Campus in Munich, Germany and related studies shows that roadside LiDAR can monitor intersections, predict pedestrian crossing intention, and feed digital-twin environments used for testing connected and automated driving functions.