This report covers the creation of a system for monitoring vehicles in highway on-ramp queues. The initial phase of the project attempted to use a blob tracking algorithm to perform the ramp monitoring. The current system uses optical flow information to create virtual features based on trends in the optical flow. These features are clustered to form vehicle objects. These objects update themselves based on their statistics and those of other features in the image. The system has difficulties tracking vehicles when they stop at ramp queues and when they significantly occlude each other. However, the system succeeds by detecting vehicles entering and exiting ramps and can record their motion statistics as they do so. Several experimental results from ramps in the Twin Cities are presented.