  The integration of Light Detection and Ranging (LiDAR) and Internet of Things
(IoT) technologies offers transformative opportunities for public health
informatics in urban safety and pedestrian well-being. This paper proposes a
novel framework utilizing these technologies for enhanced 3D object detection
and activity classification in urban traffic scenarios. By employing elevated
LiDAR, we obtain detailed 3D point cloud data, enabling precise pedestrian
activity monitoring. To overcome urban data scarcity, we create a specialized
dataset through simulated traffic environments in Blender, facilitating
targeted model training. Our approach employs a modified Point
Voxel-Region-based Convolutional Neural Network (PV-RCNN) for robust 3D
detection and PointNet for classifying pedestrian activities, significantly
benefiting urban traffic management and public health by offering insights into
pedestrian behavior and promoting safer urban environments. Our dual-model
approach not only enhances urban traffic management but also contributes
significantly to public health by providing insights into pedestrian behavior
and promoting safer urban environment.
