The Macao Government provides web-based streaming videos for the public to monitor live traffic and road conditions across the city. This allows individuals to review the latest road traffic conditions online before planning their travels. To let road user makes better and faster decisions, it is desirable to design an automated subsystem in an Intelligent Transportation System (ITS). And the subsystem should analyze available live video streams and recommend multiple travel routes, if possible, to each query instantly. In the paper, we propose a real-time road traffic condition evaluation system. Its design is based on a combination of deep learning models (YOLO and BoTSORT), and a modified Non-Maximum Suppression (mNMS) algorithm. The mNMS strategy removes the needs to manually tune the NMS parameters. By deploying YOLO with our mNMS, the object detection efficiency on live videos improves significantly. Together with the BoTSORT method, we can track the moving vehicles, create the corresponding motion trajectories, and identify traffic lanes with high correctness. The generated trajectory then operates as a filtering mechanism in assessing real-time road traffic conditions. By separating the lanes based on observation angles and using a per-lane status score independently, we further enhance the overall system performance. Through thorough experiments on the live videos, our design correctly estimates traffic status with high accuracy and without needing any manual parametric adjustments.

Though large-scale pretrained models have been revolutionary, the time has come for models to start learning like humans. They should be curious about new things and use past knowledge to make educated conjectures. A model should learn untrained tasks on the fly while simultaneously pushing the upper performance bounds of existing knowledge.