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APPENDIX C
FIELD EVALUATION OF CANDIDATE MEASURES
Methodological Approach
The field study conducted during Task 5 of the NCHRP Project 20-34 efforts
evaluated candidate M.O.E. sensitivity to actual truck weight enforcement operations. This
study has defined weight enforcement M.O.E.s as..
"..determinable quantities of what is achieved as the result of truck weight
enforcement activity. Their application also quantifies the contribution that
activity makes toward achievement of one or more of the enforcement
goals. "
The following M.O.E.s (See Table I) were based on their suitability to demonstrate
truck weight enforcement effects. These measures addressed legal load-lim~t compliance
objectives of truck weight enforcement procedures as well as Me potential for overweight
trucks to produce pavement wear and tear.
Having developed a set of proposed truck weight enforcement M.O.E.s to be
evaluated, specific methodological considerations were Implemented web regard to the
Task-S evaluation study plan. In order to assess the M.O.E.s, it is essential to include Free
fundamental measures-evaluation related concepts: reliability, validity, and sensitivity. An
urlderstarlding and application ofthese concepts are necessary for an M.O.E. assessment.
Reliability This concept addresses measurement repeatability, i.e., confidence that repli-
cated applications wall yield consistent results. In order that a measurement technique be
1
Appendix C
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Table I. Designated Measures of Effectiveness (M.O.E.s)
and their Definitions
CO-ED :: i| ~ ~ ~ ii= ~ ~ ^; ~ ~; ;= At f:f f; my. its i:~: :~
The fraction (or percentage) of the total ob
Gross Weight Violation, Proportion served truck sample which exceeds the le
gal 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 ob
Single-axle Weight Violation, Proportion served truck sample with one or more axles
which exceeds the legal single-axle weight
limit.
| The extent to which average measured sin
Single-axleWeightViolation, Severity ale-axle weights for Me observed sub
sample of single-axle weight violators ex
ceeds the applicable legal limit.
The fraction (or percentage) of the total ob
Tandem-axIe Weight Violation, Proportion served truck sample with one or more tarl
dems which exceeds the legal tandem-axIe
weight limit.
| The extent to which average measured tan
Tar~dem-axIe Weight Violation, Severity dem-axIe weights for the observed sub
sample of tandem-axle weight violators
exceeds the applicable legal limit
The fraction (or percentage) of the total ob
Bridge Formula Violation, Proportion served truck sample which exceeds the le
gal Bridge Formula weight.
The extent to which average measured
Bridge Formula Violation, Severity Bridge Formula weights for the observed
sub-sample of Bridge Fonnula violators
exceeds the legal weight.
The fraction (or percentage) of the total ob
Excess ESALs, Proportion served truck sample exhibiting Excess
ESALs; i.e., ESALs attributable to Me 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.
Appendix C
2
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dependable, it must be reliable. Reliability refers to the degree of stability exhibited when a
measurement is repeated under identical conditions.
Concern for reliability comes from the necessity for dependability in measurement.
Synonyms for reliability are: dependability, stability, consistency, predictability, accuracy.
With regard to Suck weight enforcement M.O.E.s, reliability is necessary for the uniform
application of enforcement procedures across regions of the country or within a state.
In order to ensure the reliability of recommended M.O.E.s, the Task 5 evaluation
uniformly applied the M.O.E. sensitivity analysis to WIM truck weight data collected in
four states representing norm, south, east, and western regions of the United States.
Validity The validity of a measure refers to the degree to which it actually measures what
it is designed to measure. Validity is a complex subject, but it is particularly important in
behavioral research. It is possible to study reliability without inqu~nng into the meaning of
variables. It is not possible to study validity, however, without inquiring into the nature and
meaning of one's variables.
The validity of the tested measures in this study was established based on weir
relevance to truck weight enforcement objectives, i.e., examine compliance with legal
weight limits (e.g., axle, axle-grouping, and gross weights) and infrastructure
considerations. Therefore, no further consideration was necessary to ensure validity in We
field study.
Sensitivity A key element of the Task 5 Field Studies was to assess the sensitivity of
candidate M.O.E.s to actual truck weight enforcement operations.
In behavioral studies, the concept of sensitivity is as follows. When an instrument,
e.g., measure, is used to classify individuals as having or not having a specific attribute, the
sensitivity of the measure is the proportion of correct results among people who actually
3
Appendix C
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have the attribute. That is, sensitivity is an indication that the applied measure produces a
true Indication of the sought attribute or condition. With regard to truck weight M.O.E.s, it
was necessary to seek assurance that application of the M.O.E. provided a true indication of
truck weight enforcement effects.
- The sensitivity of the candidate M.O.E.s to actual truck weight enforcement
activities was expenmentally determined in this field study through the controlled (matched
day-of-week, tune-of-day, and seasonal) comparison of measures between "baseline", i.e.,
no enforcement activity, and "enforcement" conditions, i.e., on-going truck-weighing
operations. Both permanent weigh scale operations and portable roadside truck weighing
procedures were observed as enforcement conditions.
Results
Candidate M.O.E.s were evaluated In this field study on the basis of matched WIM
data sets representing controlled enforcement and nor-enforcement the periods. Data
collection periods were controlled so as to avoid time-of-day, day-of-week, and seasonal
confounding effects. Applied WIM data were gathered in California, Georgia, Idaho, and
Minnesota. Enforcement procedures and results are discussed as follows for each of the
four study states.
California M.O.E. validation results, based on field observations in California, are dis-
cussed in this section. The analyses compare WIM data collected dunng baseline (non-
enforcement) and enforcement conditions.
The Califorrna Depardnent of Transportation provided output from a WIM scale
located approximately Free miles norm of He Santa NelIa weigh scale on I-5. The data
sample consisted of a 24-hour observation period containing 3,678 semi- and fi~-trailer
truck combinations. The permanent buck-weight enforcement scale was open during the
observation penod for seven consecutive hours (4 a.m. to ~ ~ a.m.~. This data set afforded
Appendix C
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an adequate sample size of uniform truck weights (e.g., no day-of-week or seasonal effect
contamination) to support a one-shot determination of enforcement ejects.
The analysis depicted in Table 2 below is based on sample of 2,370 Type 9
(Tractor with semi-trailer) trucks. Given observed samples of 416 and 1,954 trucks, re
spectively, during the enforcement and baseline conditions, lower gross weights (i.e.,
55,948 versus 59,547 pounds) were observed during times when the weigh station was
open. A furler examination of axIe-specific weight differences between conditions re
vealed that rear-tandem weights were lighter during enforcement conditions. While
lower average ESALs were observed during the enforcement period, the difference was
not statistically significant. However, it is worth noting that while no trucks exhibiting
Excess ESALs were observed during the period when the scale was open, a small (.36)
Excess ESAL average was observed when the scale was closed.
Table 2. California Measures Sensitivity Experiment
Gross Weight Violation, Proportion
Gross Weight Violation, Severity
Single-axI~n Lion
Single-axle Weight Violation, Severity
Tandem-axle Weight Violation, Proportion
Tandem-axle Weight Violation, Seventy
Bndge Formula Violation, Proportion
Bridge Formula Violation, Seventy
Excess ESALs, Proportion
Excess ESALs, Severity
Note: Weight units are pounds
5.9%
2,567
2.9%
438
6.8%
2,016
44.3%
7,400
. 1 % -
.36
10.1%
2,266
31 %
879
6.5%
1,607
40.2%
9,780
o
o
No
No
No
No
No*
Yes
Yes
No
No*
Yes
* = Non-signiJ cant tendengy
An examination of Type 9 truck weights exceeding 80,000 pounds revealed only
slightly lower average gross weights (i.e., 82,266 versus 82,567 pounds) during periods
~ The designation, "Non-significant tendency", indicates a numerical difference which
suggests a possible observed enforcement effect; however, the difference is not suffi-
ciently strong to be statistically significant.
5
Appendix C
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when the scale was open. The proportion of trucks exceeding 80,000 pounds was higher
(i.e., 1 0.1 percent versus 5.9 percent) when the scale was open.
The presence of Bridge Formula violations was examined for both the baseline
and enforcement conditions. A smaller proportion of the truck sample (i.e., 40.2 versus
44.3 percent) was seen to exhibit Bridge Formula violations during periods when the
weigh scale was open implying a favorable enforcement effect. Nevertheless, the degree
of violation was more severe (i.e., 9,780 versus 7,400 pounds) during this enforcement
period.
An examination of single-axle violations first examined steering axle weights and
compared these to the California legal limit of 12,500 pounds. During the baseline con-
dition, 2.9 percent of the weighed steering axles exceeded the legal limit by an average of
438 pounds. During the enforcement period, 3.1 percent exceeded the limit by an in-
creased average weight of 879 pounds. Second, an examination was conducted for indi-
vidual axles throughout the tandems. During the baseline period, 1.2 percent exceeded
the California legal limit by art average of 1, 279 pounds, and during the enforcement pe-
riod, 4.8 percent exceeded the limit by an average of 1,664 pounds. Thus, no enforce-
ment was observed with respect to single axle weights.
Tandem weight distributions were compared between baseline and enforcement
conditions. A favorable enforcement effect was noted with regard to rear tandem viola-
tions. The severity of violation was significantly decreased (i.e., from 2,016 pounds to
1,607 pounds, and the proportion of violations fell slight from 6.8 percent to 6.5 percent
during the enforcement period.
In addition, we applied one candidate measure that had been considered as an
M.O.E., i.e., the 95th-percentile gross weight, to the data set. A slightly lower 95th-
percentile, i.e., 80.1 Lips versus 81.2 hips, was found for the "scale closed" Buck sample.
This difference was not statistically significant.
Appendix C
6
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California M.O.E. Validation Summary The California Department of
Transportation provided output from a WTM scale located on I-5. An analysis of 3,678
truck combinations exhibited lower gross weights with a smaller proportion of overweight
axles dunog the time when the weigh station was open. Data on a sub-sample of 2,370
tractor-sem~-trailer combinations was further analyzed to examine M.O.E. sensitivity to the
enforcement activity. The results confirmed the following M.O.E.s: Tandem-axIe Weight
Violation Severity, Bridge Formula Violation Proportion, and Excess ESAL Severity.
Georgia Mobile truck-weight enforcement operations, utilizing an obtrusive portable
roadside weigh scale, were conducted at two locations In Georgia: a rural arterial, State
Route 300 in Crisp County; and a rural interstate, Interstate 20 in Taliferio County. WIM
equipment problems, i.e., failure to generate data for a representative truck sample,
precluded use of data gathered at Me rural arterial location. Data gathered at the interstate
location did produce a suitable vehicle sample; however, the WIM equipment proved to be
"over-calibrated", i.e., generating higher than expected truck weights, thus requiring a
"Qu~ity-Control" analytic procedure. A brief explanation of the applied Quality Control
procedure follows.
Chaparral Systems of Santa Fe, New Mexico has developed a software package to
analyze 'raw WIM data and apply a series of quality control corrections, e.g., factors to
compensate for WIM-calibration error. The software examines truck weight distributions
and notes distributional 'peaks' due to Me presence of emptr and loaded trucks in the traffic
stream. The QC software then applies correction factors based on expected peaks for
loaded and empty trucks. This public-doma~n software is ready available2, and can be
operated using Windows and SAS software.
Truck weight data collected on I-20 in Talifero County demonstrated problems due
to apparent WIM equipment over-calibration. Therefore, two follow-on steps were taken
2 Interested users should contact Statistician, Cindy Cornell, at Chaparral Systems, 649
Harkle Road, Santa Fe, New Mexico 87501
7
Appendix C
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with regard to the Georgia data. First, data files were supplied to Chaparral Systems so they
could conduct a Quality Check analysis. Second, the M.O.E. sensitivity analysis was
conducted with regard to the existing data to examine for enforcement effects. This
analysis tested the sensitivity of 'uncorrected' data to enforcement effects. This step is
Important in the M.O.E. assessment process in order to address the requirement to prelimi-
nanly conduct quality control steps prior to applying any M.O.E. evaluation procedure.
The applied Quality Check (QC) analysis generated the plot, Figure Ion the next
page, comparing baseline (06JUN95, broken line) with enforcement (13JUN95, solid line)
conditions. It is important to note that plotted data have been corrected for the calibration
error through the application of correction factors.
Results follow for M.O.E. computations based on data generated directly from the
SHRP WIM equipment and not subjected to the QC analysis. We fee! that it is important to
exarn~ne calculations based on these data, as these are the form of data Initially generated as
the result of WIM data-collection procedures. Any M.O.E. analysis too! that can be
validated with "raw" data will be more easily applied by states than if a QC analysis is
necessary.
Analysis of uncorrected WIN data An analysis of "uncorrected" Georgia data,
i.e., Quality Control correction factors were not applied, yielded a number of results which
supported M.O.E. development. Although no promising difference was observed for
average gross weight difference (e.g., gross weights exhibited a larger variance In the
enforcement condition), lower rear tandem weights were evident during the enforcement
period. The most significant M.O.E.-developmental effects were noted for the proportion
of overweight trucks and associated axles. The proportion of overweight Ducks was
significantly lower (at the .05 significance level) during the enforcement period. This
finding also held for Me examination of compliance proportion associated with each axle
companson.
Appendix ~
8
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-
- to
a'
~ -
a)
Q
IMP
~ ~1m
Hi ,
~ ~ UJ
a 39
o
in
Cal
o
_
a)
Q
X
8
to
i`
in
a,
1
A
Cal
~ .
U)
~ ~ .!
,, ~
o ,
CO
to
-
Figure 1. Plot of QC-corrected data versus uncorrected data.
·9
u,
a)
z
lo
1
a,
z
_ ~
Appendix C
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Representative terms from entire chapter:
gross weight
Two critical M.O.E.s, the proportion of Single Trailer trucks exhibiting Excess
ESALs (i.e., .72 and .67, respectfully, for the baseline arid enforcement conditions) and
the proportion of trucks exhibiting Bridge Formula violations (i.e., .69 arid .63) did not
differ between baseline arid enforcement conditions.
Analysis of QC
tendency for lower tandem-axIe violations during the enforcement period, the observed
difference was not statistically significant.
Two additional M.O.E.s were validated on the basis ofthe field observations. First,
less severe Bndge Formula violations were observed during the enforcement penod.
Second, while a non-sign~ficantly smaller percentage (~.8 versus 9.4) of Excess ESAL
violations was observed during the enforcement period, the level of severity was reduced by
a small (.65 versus I.0 ESALs) but statistically significant level.
M.O.E. differences between QC-corrected and non-corrected data sets
The comparison between Georgia enforcement and baseline conditions involved two
examining data sets, one on which WIM-calibration corrections (i.e., Quality Control or QC
analyses) had been applied and another on which corrections had not been applied. This
comparison was conducted to determine the suitability of non-corrected data sets for
M.O.E. evaluation.
The QC-corrected set indicated significantly lower steenng-axIe weights during the
enforcement condition, i.e., indicating a valid enforcement effect. This effect was not
evident In the non-corrected data set. Over less consequential difference were as follows:
the non-corrected data set indicated greater variability on steering-axIe ESALs during the
baseline condition, less vanability on second-axIe ESALs during Me baseline condition, and
greater variability in third tandem weights during Me enforcement condition.
In general, the QC-corrected data set was more sensitive to ESAL variability
differences between the baseline and enforcement conditions, and it detected lower third
tandem weights during the enforcement condition. Moreover, the QC-corrected data set
discerned Excess ESAL differences between baseline and enforcement conditions.
Agreement between the two data sets was noted wad regard to certain M.O.E.
differences, e.g., lower rear-axIe weights dunng the enforcement condition. The most
~ .!
Appendix C
No significant differences were observed with regard to Excess ESAL or Bridge
Formula violations.
Thursday enforcement effects This data comparison is based on WIM data collected
in the southbound direction on consecutive Tuesdays, i.e., March 7 and March 14.
A baseline condition, i.e., no enforcement, was established for data collected on
Tuesday, March 14. Data were compared for an enforcement condition in effect on March
7. On that date, the Port of Entry was operated during the Day shift, thus allowing data
comparison for the corresponding time period one week later. The applied database for this
enforcement effects comparison consisted of 474 trucks for the baseline condition and 512
trucks for the enforcement condition. The analyzed baseline and enforcement condition
samples consisted of 439 alla 473 Type 9 trucks, respectively.
Table 7 summarizes results of the M.O.E. finding for this expenment. A large num
her of the M.O.E.s were validated in this data set. During the enforcement penod, a smaller
percentage of trucks violated the 80,000-pound gross weigh limit; and given Me sub-sample
of violators, Me sevens of Me violations was decreased.
Table 7. Iciaho Measures Sensitivity Experiment (Thursday Enforcement
Effects)
Gross Weight Violation, Proportion
v _ A_
~_< ~ _ ~ v_! 7~m
Single-axle Weight Violation' Severity
Tandem-axle Weight Violation, Proportion
n - . ,. ~ I _ ..
Bridge Formula Violation, Proportion
Bridge Formula Violation, Severity
Excess ESALs, Proportion
E~E~ ~ _
Note: Weight units are pounds
Appendix C
1 2.5 °/0
2,493
10.3 SO
6874
. ,
14.8 °/0
1,621
3.4 onto
1 ,854
16.2 °/0
. .42
8
5 5 onto
1 ,765
5.o onto
5,188
8.5 onto
834
3 8 onto
3,361
8.2 onto
.
.33
Yes
Yes
Yes
Yes
Yes
Yes
.
No
No
. Yes
l No*
* = Non-signif cant tendency
An examination of axle-specific violations revealed reduced violation proportions
for all axles during the enforcement condition. The largest violation reduction, from ~ 0.3
to 5.0 percent, was observed for the forth axle (lead axle on rear tandem). Moreover, the
average severity of axle violations decreased during the enforcement period, i.e., from
5,188 pounds per axle, as opposed to 6,874 pounds per axle in the baseline condition.
This experiment produced M.O.E. validation with respect to the Excess ESAL
measure. Lower average ESALs were observed for the enforcement truck sample and a
smaller proportion of this sample exhibited Excess ESALs.
IcIaho M.O.E. Validation Summary A large volume of WIM data, i.e., gathered on ap-
proximately 29,000 commercial vehicles, was provided by the Idaho Transportation De-
partment. A comparison of baseline versus enforcement conditions during three different
weekdays produced a number of significant 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 measures were shown to be sensitive to enforcement activity.
M.O.E.s most consistently demonstrating sensitivity to enforcement activity were: (~) Gross
Weight Violation Proportion, (2) Single-axle Weight Proportion, (3) Tandem-axte Weight.
Proportion, and (4) Excess ESAL Proportion. While less frequently associated with en-
forcement activity, the following measures were also validated in the Idaho data: (~) Gross
Weight Violation Seventy, (2) Single-axle Weight Violation Severity, (3) Tandem-axle
Weight Violation Seventy, and (4) Excess ESAL Seventy.
Minnesota Data sets provided by the Minnesota Deparunent of Transportation were
applied in this measures sensitivity experiment. Bending plate WIM data were gathered
approximately five miles from a permanent truck-weight enforcement scale during times
when the scale was open and closed. Data collection periods were designated to conform to
the weigh station-operating schedule. The weigh station is routinely closed one weekend
Appendix C
per month, thus providing opportunity to obtain data sets for open versus closed periods
while controlling for day-of-week and season of year effects.
During the research team's November 1995 meeting with Minnesota D.O.T.
Officials, we requested data sets representing designated study days during November,
December, and January in order to support the Measures Sensitivity experimental design.
However, November data were not provided due to problems with the WIM scale. Data
collected over twelve days were provided for designated days in December 1995 and
January 1996.
Due to truck shipping trends affected by the holidays, we were limited regarding the
applicability of certain of the data sets. However, a number of non-confounded data
comparisons were possible to support the Task 5 Measures Sensitivity experiments.
Two comparisons are discussed herein which compare candidate M.O.E.s between
periods of enforcement versus non-enforcement. The first comparison, carefully controlling
day-of-week effects, is based on data sets collected on consecutive Tuesdays. In general, no
enforcement eject was found wad regard to candidate M.O.E. differences. The second
comparison based on consecutive business days of operation, contrasting on the last two
days in 1995 wad the first business day in 1996, thereby emanating New Year's Day
traffic, did reveal some differences. Each of these two comparisons is now separately
discussed.
Consecutive-Tuesdays Comparison The first comparison was based on two
samples of Type 9 (Five-axIe, Semi-tra~ler combination) trucks. Data were controlled for
bow seasonal arid day-of-week ejects. Each data set was collected on a Tuesday during
late December 1995 and early January 1996. The "Enforcement" condition, i.e., scale-open,
sample contained 1,915 trucks, arid the "Non-enforcement" condition, i.e. scale-closed,
sample contained 1,357 trucks. Observed differences between the data sets did not reveal
Appendix C
20
differences that support the operational validation of truck weight enforcement M.O.E.s.
Table ~ summarizes the observed M.O.E. comparison between conditions.
Table B. Minnesota Measures Sensitivity Experiment One
-if ^ --DIM. BE.--- ~-~ - -- ~-
Gross Weight Violation, Proportion
Gross Weight Violation, Severity
Single-axle Weight Violation, Proportion
Single-axle Weight Violation, Severity
Tandem-axle Weight Violation, Proportion
Tandem-axie Weight Violation, Severity
Bridge Formula Violation, Proportion
Bridge Formula Violation, Severity
Excess ESALs, Proportion
Excess ESALs, Severity
1.55 °/0
2,O43
6.0 onto
1 ,338
7 2 onto
3,566
1 5o/o
2,200
9.1 3%
.52
1.90 °/0
2,000
4.4 onto
1 ,231
8.3 onto
5,900
36 onto
1 ,700
10.70 °/0
.55
No
No*
No
No*
No
No
No
No*
No
No
Note: Weight units are pounds
* = Non-signif cant tendency
Truck weights were heavier on average, 48,228 pounds versus 46,166 pounds, with
an insignificant increase, 1.90 versus 1.55 percent, in gross-weight overload violations
during the enforcement period. The distribution of axle-specific overload violations was
consistent with the noted gross-weight violation rate. A slight increase in average ESALs
per truck, .99 versus .85, was noted during the enforcement period. The only observed
difference, supportive of M.O.E. development was that larger Bridge Formula violations
did occur during the nonenforcement period; however the sample sizes were too small to
be statistically significant. Of the 1,915 Bucks observed during the enforcement period, a
single Bridge Formula violation, i.e., 1,700 pounds, was detected. Of the 1,357 trucks
observed during the non-enforcement period, only two Bridge Formula violations' i.e.,
averaging 2~200 pounds, were detected.
An examination of axle-specific violations revealed non-significant differences
with the exception of an increased proportion of Axle 3 (rear axle in drive tandem) of 6.0
versus 4.4 percent in the enforcement condition. The average severity of axle violations
was slightly reduced' i.e.' 12~306 versus 13~379 pounds during the enforcement period;
however' this reduction was not statistically significant.
21;
Appendix C
Slight but statistically non-significant increases in the proportion of overweight
tandems were associated with enforcement activity. The most pronounced difference
was seen for driver tandems, whereby the proportion of violators increased from 7.2 to
8.3 percent. The severity of the associated tandem violations increased from an average
of 3,566 pounds during the baseline condition to 5,900 pounds during the enforcement
period.
In addition to the 3,272 Type 9 (five-axle semi-trailer) trucks noted above, a similar
analysis for 260 Type 10, 11, and 12 (multi-trailer) trucks revealed similar results. No
differences were observed to support the development of M.O.E.s. Specifically' Bridge
Formula violations noted for the Type 9's were not replicated. The likely explanation is that
there were significantly fewer of the latter truck types observed.
In summary, this data set revealed no statistically significant truck weight effects to
support M.O.E. validation.
Consecutive Business~ays Comparison The second comparison revealed
slightly more promising results in terms of establishing the applicability of candidate truck
weight enforcement M.O.E.s. Based on two samples of Type 9 (Five-axIe, Semitrailer
combination) trucks, data were controlled for seasonal effects due to the close time
proximity between enforcement and non-enforcement conditions, i.e., this sample pair
contrasted the last two days in ~ 995 with the first business day in ~ 996, again omitting New
Year's Day. The enforcement sample contained 1,915 trucks, and Me non-enforcement
sample contained 1,671 trucks. Table 9 on the next page summarizes the observed M.O.E
comparison between conditions.
Truck weights were marginally lower on average, 48,228 versus 50,646 during the
enforcement period. During the enforcement period, the average overload violation was
Appendix C
22
Table 9. Minnesota Measures Sensitivity Experiment Two
Gross Weight Violation, Proportion I
Gross Weight Violation, Severity
Single-axle Weight Violation, Proportion
Single-axIe Weight Violation, Severity
Tandem-axle Weight Violation, Proportion
Tandem-axie Weight Violation, Severity
Bndge Formula Violation, Proportion
Bridge Formula Violation, Severity
Excess ESALs, Proportion
Excess ESALs, Severity
Note: Weight units are pounds
4.19 °/0
2,323
6.9 onto
1 ,054
1 1.6 onto
1,31 1
0.21 °/0
1 ,650
1i.4 /
0.57
....
.. :.:.: .. . :i.t,.i ~,, n, :.,:
1 .9oo/o
2,000
3.9 onto
1,230
8.3 TO
~ ,314
0.43 TO
1 ,700
10.7 TO
0.55
....... i
~1~..-...
Yes
No*
Yeses
No
Yes
No
No
No
No*
No*
* = Non-signif cant tendency
lower, i.e., 2,000 pounds compared with 2,323 pounds during the non-enforcement period;
however this difference was not statistically significant. There was a significant decrease in
Me proportion (~.90 versus 4.19 percent) of gross-weight overload violations during the
enforcement period. Also, a comparison of axIe-specific violations revealed a greater
proportion of overloads on the steering and last axles during the period of non-enforcement.
Decreased average ESALs were observed dunng the enforcement period; however,
axIe-specific analyses demonstrated Mat the decrease could not be associated with specific
axles. The proportion of trucks exhibiting Excess ESALs, and their associated severity,
while exhibiting tendencies to demonstrate valid enforcement effects, did not significantly
differ between the enforcement and non-enforcement conditions.
Generally smaller proportions of single-axle violations were observed during the
enforcement condition, with the most pronounced difference being a reduction from 6.9
to 3.9 percent for the form axle. Very small differences (1,054 versus 1,230 pounds) in
the severity of average axle violations were observed between the baseline and enforce-
ment conditions.
23
Appendix C
Smaller proportions of tandem violations were observed during the enforcement
condition, with the most pronounced difference being a reduction from ~ I.6 to 8.3 per-
cent for the drive tandem. No statistical effect was associated with severity of the tandem
violations, as nearly identical average tandem violations (1,31 ~ and 1,314 pounds) were
observed between baseline and enforcement conditions.
A very small number of Type 9 trucks were observed to exhibit Bridge Formula
violations. Four (of 1,915 trucks) during the enforcement condition and seven (of 1,617
trucks) dunng the non-enforcement condition were in violation. The level of observed
violation, i.e. 1,700 and 1,650 pounds was also quite small. These violations did not
statistically differ between the enforcement and non-enforcement conditions.
In summary, this data set revealed a few truck weight effects (i.e., a lower
proportion of overweight trucks and lower average ESALs) which support the M.O.E.
validation effort.
Minnesota M.O.E. Validation Summary Data sets representing two weeks of
continuous traffic monitoring were provided by Me Minnesota Depardnent of Transporta-
tion. Ben(ling-plate WIM data were gathered approximately five miles from a permanent
truck-weight enforcement scale dunng 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, and the other set produced a tendency to lower
Bridge Formula violations. The results confirmed the following M.O.E.s: (~) Gross Weight
Violation Proportion, and (2) Tandem-axIe Violation Proportion.
Summary of Measures-Sensitivi~cy FieIc! Vaticiation Study Candidate M.O.E.s
were developed during We course of NCHRP Project 20-34 based on their suitability to
demonstrate truck weight enforcement effects: Proportion and Severity of Gross Weight
Violations, Proportion and Severity of Single-axie Weight Violations, Proportion and
Appendix C
24
Severity of Tandem-axle Weight Violations, Proportion and Severity of Bridge Formula
Violations, and Proportion and Severity of Excess ESAL Violations. These measures
addressed legal load-limit compliance objectives of truck weight enforcement procedures as
well as the potential for overweight trucks to produce pavement wear and tear. However, a
field study was necessary to examine the sensitivity of these measures to actual field truck
weight enforcement operations.
This four-state effort examined WIM data gathered in the presence of enforcement
activities and compared it with data collected trader non-enforcement affected flow
conditions. Data collection periods controlled for day-of-week, time-of-day, and seasonal
effects. Findings for each state are summarized as follows.
California The Californua Department of Transportation provided output from a
WIM scale located on I-5. An analysis of 3,678 truck combinations exhibited lower gross
weights with a smaller proportion of overweight axles dunng the time when the weigh
station was open. Data on a su~sample of 2,370 tractor-semi-tra~ler combinations was
further analyzed to examine M.O.E. sensitivity to the enforcement activity. The results
confirmed Me following M.O.E.s: Tandem-axte Weight Violation Severity, Bndge Formula
Violation Proportion, and Excess ESAL Severity.
Georgia Mobile truck-weight enforcement operations, utilizing an obtrusive
portable roadside weigh scale, were conducted at a rural interstate location. An analysis of
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, the observed truck sample exhibited lower steenng-axie weights,
lower rear-axle weights, and lower rear-tandem weights. During the surprise enforcement
operation, a number of overweight trucks were observed to either park alongside the
roadway or divert to alternate routes. The results confirmed the following M.O.E.s: Single-
axIe Weight Violation Proportion, Tandem-axle Weight Violation, and Excess ESAL
Severity.
2s
Appendix C
Idaho A large volume of WIM data, i.e., gathered on approximately 29,000 com-
mercial vehicles, was provided by the Idaho Transportation Department. A comparison of
baseline versus enforcement conditions during three different weekdays produced a number
of significant findings. While no day-of-week effects were readily evident to indicate on
which days enforcement effort would more likely be effective, all of the tested operational
measures were shown to be sensitive to enforcement activity. M.O.E.s most consistently
demonstrating sensitivity to enforcement activity were: (1) Gross Weight Violation Propor-
tion, (2) Single-axle Weight Proportion, (3) Tandem-axle Weight Proportion' and (4) Ex-
cess ESAL Proportion. While less frequently associated with enforcement activity, the fol-
lowing measures were also validated in the Idaho data: (1) Gross Weight Violation Sever-
ity, (2) Single-axle Weight Violation Severity, (3) Tandem-axle Weight Violation Severity,
and (4) Excess ESAL Severity.
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, and the other set produced a tendency to lower Bridge Formula violations.
The results confirmed the following M.O.E.s: (1) Gross Weight Violation Proportion, and
(2) Tandem-axle Violation Proportion.
Overview A large number of factors were seen to affect M.O.E. sensitivity to
enforcement procedures, including actt3;l1 truck weight/configuration characteristics,
shipping commodity demands, observed truck sample size, and WIM equipment variables.
Table 10 on the next page summarizes which M.O.E.s were shown to be sensitive to actual
truck weight enforcement actives in each of the states. It is highly evident that all M.O.E.s
will not discriminate between enforcement conditions at every site.
Appendix C
26
Table 11. Measure Sensitivity Summary by State
t~i-~-~:y~ ~E~MiO.=E.~::~ i: ~ ~1 :~::~ CAN :~ - ~ | Grid :| ~ MA
I Gross Weight Violation, Proportion | ~| | ~l
I Gross Weight Violation, Severity | ~| | ~|
l ingle-axle Weight violation, Proportion I I ~I ~l
l ingle-axle WeightViolation, Severity | I ~I
l andem-axle Weight violation, Proportion I | ~| ~l
.
l andem-axle Weight violation, Severity | ~| I ~l
ridge Formula Violation, Proportion | ~| :' I
Bridge Formula Violation, Severity | | ~I I
.
Excess ESALs, Proportion | ~| ~I ~
I Excess ESALs, Severity | ~| ~| ~l l
Legend: 0= Significant elect; ~ = Non-significant tendency
27
Appendix C
