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Please use this identifier to cite or link to this item: http://hdl.handle.net/11299/135168

Title: Aggregating VMT within Predefined Geographic Zones by Cellular Assignment: A Non-GPS-Based Approach to Mileage- Based Road Use Charging
Authors: Davis, Brian
Donath, Max
Keywords: Cellular assignment
Mileage based user fees
Mileage based road use fees
Mileage based road user charging
Vehicle miles of travel KNN
K nearest neighbors
Machine learning
Learning (Artificial intelligence)
Signal processing
Issue Date: Aug-2012
Publisher: Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota
Series/Report no.: CTS 12-28
Abstract: 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.
URI: http://purl.umn.edu/135168
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