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3.0 TRUCK-WEIGHT ENFORCEMENT MEASURES of EFFECTIVENESS
(M.O.E.s)
In response to the need for adequate truck-weight enforcement evaluation proce-
dures, NCHRP Project 20-34 developed and validated applicable measures of effective-
ness (M.O.E.s). The project effort also developed techniques to apply the M.O.E.s in a
systematic sampling plan and to analyze the collected data.
A discussion of the M.O.E. development process is contained in Appendix B.
The field validation procedure is detailed in Appendix C. Results of these efforts are
summarized in the following two report sections.
3.1 M.O.E. Development
The first step in the M.O.E. development process was to consider an operational
definition of truck weight enforcement M.O.E.s. Following this step, the project team
developed a set of objective criteria against which to evaluate candidate M.O.E.s. The
applied criteria were derived from M.O.E functional requirements. Candidate M.O.E.s
were ranked according to their suitability to meet the designated performance criteria.
It is necessary to understand the definition of truck-weight enforcement M.O.E.
A Measure of Effectiveness (M.O.E.) of weight enforcement activity is defined as a
Determinable quantity of what is achieved as the result of truck weight enforcement ac-
tivity". Its application should also be used to quantify Me contribution that a particular
activity makes toward achievement of one or more of the goals defined in Section 2.3. In
order to quantify effectiveness there must be measures which show benefits in terms of:
(~) compliance with operational weight and axIe-spacing regulations, (2) pave-
ment/bridge preservation, or (3) minimizing accidents, deaths, injuries, and property
damage.
Initial truck-weight enforcement M.O.E. concepts were developed on the basis of
art assessment of truck weight enforcement objectives (including results of a SO-state
s
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agency survey) and the anticipated sensitivity of resulting candidate measures to those
objectives. A set of candidate M.O.E.s for field validation was derived from a systematic
application of the following functional criteria:
I. Practicality of M.O.E. application, e.g., amenable to state agency data collection capa-
bility, cost requirements, and ease of measurement.
2. Reliability of candidate MO.E, i.e., correctly represents a Sue distribution of weights,
classification, percentage of overweight trucks, percent of bndge fonnula non-compli-
ance, etc.
3. Support random sampling, i.e., designed to achieve representative sampling over desig-
nated study region.
4. Absence of bias with regard to enforcement/monitoring procedure, i.e., generally sensi-
tive to prevailing truck characteristics regardless of enforcement activity.
5. MO.E. compatibility with agency data collection methods, i.e., achieved in terms of
measures that can be readily denved from existing or overwise readily obtainable data-
collection apparatus.
6. Sensitivity to infrastructure damage, e.g., considers that excessive axle-weight as op-
posed to excessive tandem-weight are more likely to result in pavement damage.
7. Applicability to future technology, i.e., data requirements are consistent with capabilities
of emerging technologies.
Candidate measures were evaluated and ranked by an expert panel. The final set
of designated candidate M.O.E.s for field validation, along troth their definitions, is pre-
sented in Table I.
3.2 M.O.E. Fielc! Validation
A field validation study was conducted to confirm the sensitivity of candidate
M.O.E.s to actual truck weight enforcement activity. Candidate M.O.E.s were tested in a
four-state evaluation effort that applied matched sets of weigh-in-motion (WIM) data,
collected under controlled baseline and enforcement conditions. Overall findings of the
field validation effort confirmed the suitability of M.O.E.s listed in Table I. State-specific
findings are summarized as follows.
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Table I. Designated Measures of Effectiveness (M.O.E.s)
and their Definitions
Hi - Eiifo~eiii,ent~"O¢~|.~., ~ ~ ~ ,, ~I) - i~.'=~...:~.~.~,.,:~,:~^,~.:..,:
The fraction (or percentage) of the total
Gross Weight Violation, Proportion observed truck sample which exceeds the
legal gross weight limit.
The extent to which average measured
Gross Weight Violation, Severity gross weights for the observed sub-sample
of gross weight violators exceeds the legal
gross weight limit.
The fraction (or percentage) of the total
Single-axIe Weight Violation, Proportion observed truck sample with one or more
axles which exceeds the legal single-axle
weight limit.
The extent to which average measured sin
Single-axle Weight Violation, Severity ale-axle weights for the observed sub
sample of single-axle weight violators ex
ceeds the applicable legal limit.
The fraction (or percentage) of the total
Tandem-axle Weight Violation, Proportion observed truck sample with one or more
tandems which exceeds the legal tandem
axle weight limit.
The extent to which average measured tan
Tandem-axleWeightViolation, Severity dem-axle weights for the observed sub
sample of tandem-axle weight violators
exceeds the applicable legal limit.
The Faction (or percentage) of the total
Bndge Formula Violation, Proportion observed truck sample which exceeds the
legal Bridge Formula weight.
The extent to which average measured
Bridge Formula Violation, Severity Bridge Formula weights for the observed
sub-sample of Bridge Formula violators
exceeds Be legal weight.
The fraction (or percentage) of the total
Excess ESALis, Proportion | observed truck sample exhibiting Excess
ESALs; i.e., ESALs attributable to the ille
gal portion the individual single or tandem
axle group.
The average value of Excess ESALs ob
Excess ESALs, Severity served for the truck sub-sample exhibiting
Excess ESALs.
' Equivalent Single Axle Load is defined in the Glossary (Appendix H) of this report.
7
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California The California Department of Transportation provided output from a WIM
scale located on I-5. An analysis of 3,678 truck combinations exhibited lower gross weights
path a smaller proportion of overweight axles during the tune when the weigh station was
open. Data on a sub-sample of 2,370 tractor-semi-trailer combinations was further analyzed
to examine M.O.E. sensitivity to the enforcement activity. The results confirmed the
validity of the following M.O.E.s: Tandem-axIe Weight Violation Severity, Bridge For~nula
Violation Proportion, and Excess ESAL Seventy.
Georgia Mobile truck-weight enforcement operations, utilizing an obtrusive portable
roadside weigh scale, were conducted at a rum interstate location. An analysis of WIM
data gathered on 483 combination trucks revealed a number of M.O.E. validation effects
associated with observed axle and tandem weights. Under conditions of visible (and
unexpected) mobile enforcement operations, Me observed truck sample exhibited tower
steering-axIe weights, lower rear-axIe
weights, and lower rear-tandem weights. Dunng the surprise enforcement operation, a
number of overweight trucks were observed to either park alongside the roadway or divert
to alternate routes. The results validated the following M.O.E.s: Single-axIe Weight
Violation Proportion, Tandem-axie Weight Violation, and Excess ESAI~ Seventy.
Idaho A large volume of WIM data, i.e., gathered on approximately 29,000 commercial
vehicles, was provided by Me Idaho Trarsportation Department. A comparison of baseline
versus enforcement conditions during Tree different weekdays produced a number of sig-
nificar~t findings. While no day-of-week effects were readily evident to indicate on which
days enforcement effort would more likely be effective, all of Me tested operational meas-
ures were shown to be sensitive to enforcement activity. M.O.E.s most consistently demon-
strating sensitivity to enforcement activity were: (1) Gross Weight Violation Proportion, (2)
Single-axIe Weight Proportion, (3) Tandem-axIe Weight Proportion, and (4) Excess ESAL
Proportion. While less frequently associated with enforcement activity, the electiveness of
following measures were also validated in the Idaho data: (1) Gross Weight Violation
Severity, (2) Single-axle Weight Violation Severity, (3) Tandem-axle Weight Violation
Seventy, and (4) Excess ESAL Severity.
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Minnesota Data sets representing two weeks of continuous traffic monitoring were
provided by the Minnesota Department of Transportation. Bending-plate WIM data were
gathered approximately five miles from a permanent truck-weight enforcement scale during
times when the scale was both open and closed. While generally weak M.O.E. validation
findings were seen in Minnesota results, one data set did exhibit a smaller proportion of
gross weight and tandem axle violations along with a tendency for less severe excess
ESALs. The other set produced a tendency to lower Bndge Formula violations. The results
validated the following M.O.E.s: (~) Gross Weight Violation Proportion, and (2) Tandem-
axIe Violation Proportion.
Summary All of the tested M.O.E.s. were shown to be sensitive to actual truck weight
enforcement activities. A number of factors were seen to affect M.O.E. sensitivity to
enforcement procedures, including actual truck weight/configuration charactenstics,
shipping commodity demands, observed truck sample size, and WIM equipment vanables.
Those measures most strongly supported by the field data (in descending order) are as
follows: (~) Excess ESALs, Severity, (2) Tie - Gross Weight Violation, Severity, and
Excess ESALs, Proportion; and (3) Tie - Gross Weight Violation, Proportion, Single-axIe
Weight Violation, Proportion; and Tandem-axle Weight, Severity.
9
Representative terms from entire chapter:
gross weight
