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 the 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, commonly associated with unwanted monitoring or tracking and is thus considered an invasion of privacy.
The method proposed here utilizes cellular assignment, which is capable of determining a vehicle's current travel zone, but is incapable of determining a vehicle's precise location, thus better preserving user privacy. This is accomplished with a k-nearest neighbors (KNN) machine learning algorithm focused on the boundary of such travel zones.
The work described here focuses on the design and evaluation of algorithms and methods that when combined, would enable such a system. The primary experiment performed evaluates 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 2 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 the drivers' privacy.