, Professor, Mechanical Engineering
Currently, most of the costs associated with operating and maintaining the roadway infrastructure are paid for by revenue collected from the motor fuel use tax. As fuel efficiency and the use of alternative-fuel vehicles increases, alternatives to this funding method must be considered. One such alternative is to assess mileage-based user fees (MBUF) based on vehicle miles traveled (VMT) aggregated within the predetermined geographic
areas, or travel zones, in which the VMT is generated. Most of the systems capable of this use Global Positioning Systems (GPS). However, GPS has issues with public perception, as it is commonly associated with unwanted monitoring or tracking and is thus considered an invasion of privacy. This research explored a method that utilizes cellular assignment, which is capable of determining a vehicle's current travel zone but incapable of determining a vehicle's precise location, thus better preserving user privacy. This is accomplished with a k-nearest neighbor (KNN) machine-learning algorithm focused on the boundary of such
travel zones. The work focused on the design and evaluation of algorithms and methods that, when combined, would enable such a system. The primary experiment evaluated the accuracy of the algorithm at sample boundaries in and around the commercial business district of Minneapolis, Minnesota. The results show that with the training data available, the algorithm can correctly detect when a vehicle crosses a boundary to within two city blocks, or roughly 200 meters, and is thus capable of assigning the VMT to the appropriate zone. The findings imply that a cellular-based VMT system may successfully aggregate VMT by predetermined geographic travel zones without infringing on drivers' privacy.