Abstract:
Weight enforcement is essential for highway infrastructure conservation. Overweight
vehicles represent an exponentially higher degradation for the pavement then those inside
the legal limits. They also represent increased danger to their own safety and of the other
road users, due to the possibility that the excessive load compromises the truck’s ability to
maneuver and break efficiently. However, performing high-precision weight measurements
nowadays demand that the vehicle reduce their speed in order to enter weight
enforcement stations. In this aspect, high-speed weight-in-motion (HS-WIM) technology is
a viable alternative, where the vehicles’ weights are measured in operational speeds.
However, current HS-WIM systems face a challenge of increasing their accuracy in order
to compete with low-speed weighing systems. In this context, this paper presents a
statistical model for error correction in HS-WIM systems as a function of the pavement
temperature and the measured speed, which are parameters that are repeatedly shown to
be related to error in these systems. The proposed model is based on a set of fitted linear
equations that are created considering temperature and speed intervals, which are
determined according to data collected in the field with known-weight trucks. A practical
application of the proposed method is presented that shows that it is capable of increasing
the system’s performance both by displacing the average closer to zero and also by
reducing the deviation of the resulting errors. Therefore, the proposed method is presented
as a tool to increase HS-WIM systems’ performance, in hopes that it contributes to the
growth of HS-WIM technology and its viability in practical applications.
