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 are being considered that don’t use fuel consumption as a surrogate for road use.
Many systems have been proposed which are capable of assessing mileage based user
fees (MBUF) based on the vehicle miles traveled (VMT) aggregated within
predetermined geographic areas, or travel zones, in which the VMT is generated. Most of
the systems capable of this use GPS. However, GPS has issues with public perception, commonly associated with unwanted monitoring or tracking and thus an invasion of privacy. One method to mitigate these issues is to use a system that utilizes a cellular network
based approach that can determine a vehicle’s current travel zone, but does not determine
a vehicle’s position through the use of GPS. The approach proposed here is based on a knearest
neighbors (KNN) machine learning algorithm focused on the boundary of such
travel zones. This method has two main phases. In phase one, the training phase, data is
collected near zone boundaries using a cellular modem and a GPS receiver. This
hardware creates a database that pairs readings consisting of observable cell towers and
the strengths with which they were received, with the travel zone in which the reading
took place, as determined by the GPS receiver. Then in phase two, the operational phase,
GPS is no longer needed as the system detects changes in the vehicle’s travel zone by comparing currently available cellular information with the database. This method, while
capable of determining the travel zone, is incapable of determining a vehicle’s precise
location, which better preserves both the user’s actual privacy and perceived privacy.
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 KNN algorithm at sample boundaries in and around the
commercial business district (CBD) 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. A means for handling this
relatively small ambiguous region between travel zones is also presented. The findings
imply that a cellular-based VMT system may indeed be a feasible method to aggregate
VMT by predetermined geographic travel zones.
University of Minnesota M.S. thesis. April 2012. Major: Mechanical engineering. Advisor: Max Donath. 1 computer file (PDF); vii, 79 pages, appendices A-C.
Davis, Brian James.
Aggregating VMT within predefined geographic zones by cellular assignment:a non GPS-based approach to mileage-based road use charging..
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