  Traffic volume data collection is a crucial aspect of transportation
engineering and urban planning, as it provides vital insights into traffic
patterns, congestion, and infrastructure efficiency. Traditional manual methods
of traffic data collection are both time-consuming and costly. However, the
emergence of modern technologies, particularly Light Detection and Ranging
(LiDAR), has revolutionized the process by enabling efficient and accurate data
collection. Despite the benefits of using LiDAR for traffic data collection,
previous studies have identified two major limitations that have impeded its
widespread adoption. These are the need for multiple LiDAR systems to obtain
complete point cloud information of objects of interest, as well as the
labor-intensive process of annotating 3D bounding boxes for object detection
tasks. In response to these challenges, the current study proposes an
innovative framework that alleviates the need for multiple LiDAR systems and
simplifies the laborious 3D annotation process. To achieve this goal, the study
employed a single LiDAR system, that aims at reducing the data acquisition cost
and addressed its accompanying limitation of missing point cloud information by
developing a Point Cloud Completion (PCC) framework to fill in missing point
cloud information using point density. Furthermore, we also used zero-shot
learning techniques to detect vehicles and pedestrians, as well as proposed a
unique framework for extracting low to high features from the object of
interest, such as height, acceleration, and speed. Using the 2D bounding box
detection and extracted height information, this study is able to generate 3D
bounding boxes automatically without human intervention.
