Vehicle Classification, Rumble Strips Detection, and Mapping Using Artificial Intelligence

The collection of transportation data is crucial to data-driven decision-making. Using artificial intelligence for such data collection efforts could save substantial time and cost and help to improve transportation safety and efficiency. This research study used YOLOv4 object detection and DeepSORT tracking models to count and classify vehicles into 13 FHWA vehicle classes from video footages from existing roadside cameras. YOLOv4 was also used to detect the presence of rumble strips from roadway images and used to create a rumble strip inventory map. The trained vehicle counting/classification model achieves a counting accuracy of similar to 97% at daytime and similar to 91% at nighttime. The rumble strip detection model has similar to 95% accuracy. This study demonstrates that AI model can be trained to accurately count and classify vehicles under various weather, lighting, and traffic conditions, as well as to collect transportation asset data to improve roadway safety.