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Figure B.1. Example South Carolina IIMS screen.
(South Carolina, through participating in the I-95 Corridor Requirements System--State Version (HERS-ST)--to predict
Coalition travel demand model development effort) travel mobility and safety-related measures. HERS-ST is available at
demand models that could be used to support risk analysis. no cost and requires HPMS data as input. All of the pilot par-
However, none of the participants had identified risk sce- ticipants had this data available, and it was thus feasible to run
narios that could be used in conjunction with their travel de- HERS-ST for each of the pilot corridors.
mand models for quantifying potential consequences of HERS-ST predicts a limited number of measures of pave-
different risks. ment condition, including Present Serviceability Rating (PSR),
and International Roughness Index (IRI). However, HERS-ST's
modeling of pavements is rudimentary compared to that fea-
B.3 Analyses Performed
sible in most agency pavement management systems (PMS).
Using the data described in the previous section, a set of Thus, where feasible, supplemental analysis results were used
analyses was performed for each of the pilot corridors. The as an alternative to HERS-ST for predicting pavement condi-
goal of the analysis was to predict future performance of tion. In the case of South Carolina, agency PMS results were
the pilot corridors for different budget scenarios in terms of available for the pilot corridor. For Wisconsin, research team
the core measures recommended for a typical Interstate Asset members working with Wisconsin DOT on a separate effort
Management Plan. Also, to the extent feasible, analyses were used the remaining service life approach described in Interim
performed to demonstrate application of the risk assessment Report 1 to analyze pavement conditions for the pilot corri-
approach recommended previously. The paragraphs below dor. For California, supplemental analyses were available at a
describe the analyses performed and specific analytical tools system level, but not for the pilot corridor. Thus, the research
used for the pilot. team relied upon HERS-ST for predicted pavement conditions.
Pavement/Highway Inventory. This category of data can Bridges. For South Carolina, a set of Pontis simulations
be used to predict pavement conditions, as well as a range of was run for the pilot corridor using South Carolina Pontis
other measures, including mobility and safety-related mea- models. For California DOT (Caltrans), the bridge needs analy-
sures. The research team used the FHWA Highway Economic sis approach defined for Caltrans' SHOPP update process was
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used. This entailed using Pontis to generate functional im- calculation requires remaining service life for each asset, re-
provement needs, predict deterioration over time, and predict newal cost, and service life for a newly renewed asset. The figure
the benefits of certain types of bridge needs. For Wisconsin, shows the percent of a group of assets functioning as intended.
state-specific models were not available and element data, In this example a $2M annual budget results in 63 percent of
though available, were not stored in the form of a Pontis data- the assets functioning as intended at the end of 20 years (com-
base. Thus, NBIAS was used to predict bridge conditions for pared to a starting value of 75 percent), a $1M annual budget
the Wisconsin corridor. results in having 31 percent functioning as intended at the end
of the period, and a $0.5M annual budget results in approxi-
Other Structures and Roadside Assets. As noted previ- mately 16 percent functioning as intended.
ously, limited data were available for other assets besides pave- Where other agency systems and data were available, such
ment and bridges. Where such data existed, they were either as the SCDOT IIMS and data associated with the Caltrans
in terms of remaining service lives of assets (e.g., of facilities) SHOPP update process, the research team reviewed this in-
or average condition/level of service of some population of formation, but did not integrate these data sources into the
assets. The research team developed and tested spreadsheet analysis process except as otherwise noted.
models for applying a remaining service life approach to assets
for which remaining life and rehabilitation/replacement costs Risk. The research team demonstrated the risk assessment
were available, and for applying a maintenance level-of-service approach for examining structure-related risk using data pro-
model to assets for which levels of service were defined. vided by Caltrans. Structure risks considered systematically
Figure B.2 illustrates the results of applying a simple remain- by Caltrans include risks of seismic events, bridge scour, and
ing service life model. In this example, an asset (e.g., a facility) guardrail failure. Previously Cambridge Systematics worked
is deemed to be "functioning as intended" if its age is less than with Caltrans to develop a utility measure that included
the service life for that asset, and is deemed to be not function- measures of vulnerability and consequence for each of these
ing as intended if it has no remaining service life. Given an risks, as well as for other bridge investment needs. For exam-
annual budget, it is a straightforward calculation to predict the ple, for seismic risk, Caltrans has developed a list of proposed
age distribution over time for a population of assets, aging the seismic retrofit projects, and calculated seismic vulnerability
assets each year of the analysis period and simulating renewal scores for each bridge. These scores provide an indication of
of an asset that has reached the end of its service life, to the ex- the degree of risk, and data on the functionality of the bridge
tent this is feasible given the available budget. Performing this (e.g., traffic, detour distance around the bridge) provides an
Figure B.2. Example calculation of percent functioning as intended versus annual
budget using service life data.
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indication of the consequences in the event of a seismic event. measure, and each series graphed shows results for a different
These measures were combined to predict the utility of address- budget allocation.
ing each identified seismic need, and to develop a relationship
between utility achieved and predicted spending. METIS. The major limitation of AssetManager is that the
system shows results for different user-specified budget alloca-
AssetManager NT. AssetManager is a set of analytical tions, but does not provide support for optimizing a budget be-
tools initially developed through NCHRP Project 20-57, tween different objectives. Performing a cross-asset allocation
and now licensed through AASHTO. AssetManager NT is a is a difficult proposition. Typically, one would solve such a
network-level tool that combines analysis results generated multi-objective optimization problem by first defining a utility
through other systems, displaying predicted conditions for a measure that encompasses all of the objectives one attempts to
range of different performance measures and budget assump- achieve by investing in IHS assets (as was performed for help-
tions. The system is well-suited for applications such as sup- ing characterizing benefits of mitigating structure-related risks
porting development of an Interstate Asset Management Plan, described above). Previously Cambridge Systematics devel-
where results from different systems are combined with the oped a tool for solving multi-objective optimization problems
goal of providing an integrated view of predicted conditions. using an evolutionary process, in which a user views different
The research team imported into AssetManager NT data candidate solutions (which correspond to different potential
on pavement conditions, bridge conditions, mobility, safety, objective functions), and gradually converges upon an optimal
and risk. In the system one can view predicted conditions solution through manual evaluation of potential solutions.
over time for a pilot corridor for a specified distribution of The research team used this tool, the Multi-Objective Evolu-
funds. Figure B.3 shows an example screen from the system. tionary Tool for Interactive Solutions (METIS), to pilot the
In this example results are shown for six different perfor- process of optimizing resources allocated to IHS assets.
mance measures (pavement condition, bridge health index, Figure B.4 shows an example screen from METIS. In this
number of Structurally Deficient bridges, delay, user costs, screen, alternative resource allocation results are shown for four
crash rate) for four different budget allocations. Each pane different candidate solutions with different weights on four dif-
in the figure represents results for a different performance ferent objectives: maximizing pavement condition (measured
Figure B.3. Example AssetManager screen.
