Performance Overview
We compared the performance of different subsets of LTPP data,
crossclassifying on dimensions expected to correlate with different longterm
pavement performance trends. These dimensions include: region, KESAL (traffic
loading in thousands of equivalent singleaxle loads), and pavement subgrade
type (treated or untreated). For each dimension, we examine the performance of
Asphalt Concrete (AC), Jointed Plain Concrete Pavement (JPCP), Jointed
Reinforced Concrete Pavement (JRCP) and Continuously Reinforced Concrete
Pavement (CRCP) separately.
The LTPP program is organized geographically into four administrative regions:
North Atlantic (NA), North Central (NC), Western (WR), and Southern (SR). This
crossclassification was performed as a firstorder approach to examining
interactions between geographical region, pavement type, and roughness.
Traffic load is a major factor in pavement degradation. Hence, we
crossclassify sections by traffic load in thousands of singleaxle equivalent
loads per year to see if there is any discernible correlation between loading,
pavement type, and roughness over time.
The third crossclassification involves categories based on whether the
sections were constructed on a treated or untreated subgrade. Treated subgrade
types in the database include bituminous treated bases, nonbituminous treated
bases, and a catchall treated subgrade type. Untreated subgrade types include
granular bases, unbound bases, and a catchall untreated subgrade type.
Results by Region
To evaluate the effects of traffic loading we grouped the sections
into quartiles based on their mean yearly ESAL loading, with the
first quartile representing the lowest loading scenarios and the
fourth quartile representing the highest loading scenarios.
Test Subset

AC sections in Western (WR) and North Atlantic (NA) states saw maintenance
occur sooner than in the Southern (SR) regions of the country, with p = 0.03
and p=0.02 respectively.

The initial roughness of AC sections is lower (p < 0.001) but increases
faster (p < 0.001) than for CRCP sections (Figures 1 & 2).

AC sections in the NA and WR saw the first intervention, at 10.8
and 11.8 years respectively, compared to the SR and NC regions
where the maintenance occurred at the 15.2 and 15.5 year marks
respectively. (NA versus NC, p = 0.01; NA versus SR, p = 0.002;
NC versus WR, p = 0.02; SR versus WR, p = 0.005).
Figure 1: Regional performance profile for
AC in the test dataset.
Figure 2: Regional performance profile for CRCP.
LTPP Dataset

AC sections in the NA and WR saw the first intervention, at
10.8 and 11.8 years respectively, compared to the SR and NC regions where the
maintenance occurred at the 15.2 and 15.5 year marks respectively. (NA versus
NC, p = 0.01; NA versus SR, p = 0.002; NC versus WR, p = 0.02; SR versus WR, p
= 0.005).

Figure 3, illustrates the initial IRI and IRI at first intervention for AC,
JPCP, JRCP and CRCP sections. AC sections had a statistically significant lower
initial IRI akkcross all regions (with each comparison meeting the criteria of
p < 0.05 for a Students’s ttest), while having a statistically significant
higher rate of change over time compared to all the other concrete pavement
types across all regions (each comparison meeting the criteria of p < 0.05 for
a Students’s ttest). CRCP pavements tended to have the slowest rate of IRI
change.

Fit statistics for each of the sections for each region were developed.
Figure 4, illustrates the metrics developed for AC sections in the SR.
Similar plots were developed for each pavement type in each region. However,
they are not being presented on this page, in order to preserve
focus on broad trends.
Figure 3: Initial IRI and IRI at first
intervention by region (IRI in m/km)
Figure 4: IRI metric distributions for AC
sections in the southern region
Figure 5: Regional performance profile for
AC using full LTPP dataset.
Traffic Load
To evaluate the effects of traffic loading we grouped the sections
into quartiles based on their mean yearly ESAL loading, with the
first quartile representing the lowest loading scenarios and the
fourth quartile representing the highest loading scenarios.
Test Subset

AC is used more in lowtraffic scenarios, with only four AC
sections found in the fourth (e.g. highest loading) ESAL quartile.
CRCP and JPCP are used more in hightraffic scenarios. (No hypothesis
test performed.)

There is no evidence in the test dataset that IRI performance over
time is sensitive to loading within pavement groups. However, there
is evidence to suggest that JPCP may undergo earlier maintenance in
the second (p = 0.035) and third (p = 0.049) loading quartiles
respectively.

AC undergoes repair earlier than JPCP in the first loading quartile
(p = 0.005) and earlier than both CRCP (p = 0.006) and JPCP (p =
0.034) in the second loading quartile.

The roughness of CRCP sections increases at a slower rate in the
third and fourth loading quartiles than both AC (p = 0.009, p <
0.001) and JPCP (p = 0.012, p = 0.009).
LTPP Dataset

There is no evidence that IRI performance over time is sensitive to
different levels of loading. Of course, it is worth considering
that this is a conditional independence given that the designs of
pavements catering to different loads may vary significantly.

Consistent with trends from the test data set, AC undergoes repair
earlier (difference of approximately 7 years on average) than JPCP
in the first, third and forth loading quartiles (p = 0.001, p =
0.03 and p = 0.002) and earlier than both CRCP (p = 0.02) in the
forth quartile.

Figure 6, illustrates the initial IRI and IRI at first intervention
for AC, JPCP, JRCP and CRCP sections. AC sections had a
statistically significant lower initial IRI than the concrete
pavement types in the third loading quartile (with each comparison
meeting the criteria of p < 0.05 for a Students’s ttest), while
having a generally higher rate of change over time compared to all
the other concrete pavements (statistically significant in loading
quartiles 3 and4). CRCP pavements tended to have the slowest rate
of IRI change.
Figure 6: Comparison of Initial IRI and IRI at
First Intervention by Traffic Quartiles (IRI in m/km)
Subgrade Treatment
Test Set

AC has lower initial IRI with an untreated base (p = 0.018).

CRCP is not sensitive to base type – both treated and untreated
bases perform equally.
LTPP Dataset

AC has lower initial IRI for treated bases than for untreated
bases (p = 0.03).

None of the concrete pavements showed any significant sensitivity
to the type of base.

For sections with both treated and untreated bases AC had
significantly lower initial IRI compared to all the concrete
pavements.

For sections with treated bases AC had significantly higher time
rate change of IRI compared to all the concrete pavements. Figure
7, illustrates a comparison of each pavement type’s initial IRI and
IRI at first intervention for treated and untreated bases.
Figure 7:Comparison of Initial IRI and IRI at
First Intervention by base treatement(IRI in m/km)
Summary
 Generalizability

Caution must be exercised when generalizing the results of LTPP
data analysis. As the above results illustrate, the choice of the
data subset can lead to different answers to the same question. For
example, an analysis of performance of AC sections in the NA
region, for the test subset and the entire LTPP dataset, produce
different trends. Similarly, analysis of the initial IRI
performance for AC on treated or untreated bases is different
depending on whether the test dataset is used, or the entire LTPP
dataset is used. Crossclassification of the data also reveals
differences in trends dependent on the choice of the data subset.
These inconsistencies illustrate that the results of performance
analysis have very narrow applicability to the context of the
dataset used. On the same note, when a particular trend is
repeatedly and consistently observed across multiple contexts, its
scope of application becomes broader and therefore more general.
For example, across all datasets analyzed, it was observed that the
initial IRI for AC sections tend to be statistically significantly
lower than concrete sections, while the time rate change of IRI for
concrete sections also tend to be statistically significantly lower
than those for AC sections, resulting in longer times to first
intervention for concrete sections when compared to AC sections.
 No Free Lunch

When broad trends can be observed across multiple datasets they
often highlight tradeoffs in performance. For example, there is
evidence of tradeoffs between initial roughness (favoring AC) and
the rate of change of roughness over time (favoring CRCP).
Identifying such tradeoffs should be the objective of analyzing
historical datasets as they help decisionmakers develop deeper
insights regarding their networks and the maximize the level of
service they can provide. It also veers the conversation away from
biasing the pavement selection process by presenting a partial view
of the data analysis.
 Context is King

The above two points segue into the third and final takeaway that
emphasizes the need for context sensitive decisionmaking. Analysis
of LTPP data to support decisionmaking should be encouraged,
however, data subsets for analysis should be constructed to
accurately reflect the context (location, use, functional class) of
the decision question. As a corollary, the results of the analysis
should narrowly apply to the scope of the decision question at
hand, and not be generalized to apply to other derivative
decisions.