Resource Allocation Logic Framework to Meet Highway Asset Preservation (2012)

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1 S u m m a r y Introduction and Approach The overarching goal of the National Cooperative Highway Research Program (NCHRP) Project 14-21, “Resource Allocation Framework to Meet Highway Asset Preservation Needs,” is to develop an analysis framework that state DOTs can use as a guide and tool to allocate highway system preservation resources across various principal asset groupings, preser- vation activities, and regions within their jurisdiction. There is no widely accepted logic framework to address preservation resource allocation decisions that account for a broader (multi-asset) view of preservation program needs, nor does a sound logic framework exist for adjusting and optimizing allocations and results accountability across all asset groups— after the inevitable funding constraints and priority changes are introduced. It is not uncommon in resource allocation processes that preservation needs for some highway asset groups are initially estimated based on priorities and expected performance or condition results. Tools exist to support this estimation—particularly for bridge struc- tures and pavement, but these tools do not effectively support optimization decisions on the division of available resources between asset groupings and regions. Minimal decision support is currently available to estimate preservation needs and allocation adjustments for asset groupings outside the bridge or pavement domains. The research team performed extensive literature review and conducted numerous inter- views on the processes followed and state-of-the-practice for preservation resource alloca- tion. With analysis of this information, the team derived a clarified statement of the highway preservation resource allocation problem to be considered, and the key decision factors for resource allocation. The research team then formulated a mathematical model to represent the essence of the problem, and to guide solution logic and computations. A solution was then derived based on the mathematical model, applying standard linear programming algorithms and appropriate objective functions. To validate the logic and practicality of the mathematical model, the analysis and linear optimization were developed further, using Microsoft Office Excel and the integrated Solver function. An initial test solution was developed for demonstration of the logic based on plausible state preservation program structures and underlying data sets for asset inventories, costs, deterioration rates, asset condition, and performance goals. The initial data set was adapted from real state DOT data gathered from document research and prior project experience of the research team. This model was demonstrated and then further tested in two state DOT case applications. Based on the results of the case studies and recognizing that there is a significant variety of user approaches and taxonomies for preservation resource allocation, the research team Resource Allocation Logic Framework for Highway Asset Preservation

2 resource allocation Logic Framework for Highway asset Preservation developed a streamlined Excel-based model that permits users to enter appropriate preser- vation program taxonomies, inventory performance and deterioration estimates, priorities, and performance goals. The logic demonstration model is scalable to a wide set of user-defined asset/activity groupings (AAG) and multiple districts. The model offers optimized alloca- tions across all AAGs that are supported by data or reasonable estimates of inventory, aver- age condition, deterioration rates, and unit costs. Alternative allocation solutions are built in for specific AAGs that are not supported by sufficient data or reasonable estimates. The demonstration model is available in Excel workbook format on the NCHRP Project 14-21 web page at www.trb.org. Logic Framework and Model The Resource Allocation Logic Framework and supporting mathematical model deter- mine the optimal investment allocations by AAG for specific allocation cycles and specific regions or districts given statewide goals and objectives, available funding constraints, and performance thresholds. Figure S-1 is a high-level view of the allocation logic. Key outputs from the resource allocation model include realistic performance/condition expectations (both performance/condition and timelines to achieve targets) by AAG after allocations are adjusted to conform to funding limitations and other strategic variables. Once the strategic inputs and targeted performance/condition (left side of the diagram), and the data inputs (top right side of the diagram) are introduced, the computations and objective function optimize and compute allocations to match available resources, as well as achievable performance results that are accountable to the investments (unshaded blocks). If the performance and timeline results are unacceptable to decisionmakers, adjustments to AAG performance goals or to the overall funding commitment can be made, resulting in new allocation and performance results. The predicted end-of-cycle performance/condition rating results for each AAG (after funds-available adjustments), when compared with desired targets, is useful to assess the AAG Data and Estimates Compute Preservation Needs for all AAGs Inventories Average Performance/Condition Rating Average Unit Cost Average Deterioration Rate Strategic Decisions And Goals: Priorities AAG Rank Timelines Constraints, Funding Decisions Performance and Condition Targets Compare to Available Preservation Funding and Constraints Optimize AAG Allocation Adjustments Compute Expected Performance and Timeline Results NO NOYES YES Adjust Funding or Goals? Funding & Constraints Met? DONE Figure S-1. Allocation logic overview.

Summary 3 effects of funding shortfalls on preservation of the road network. This will help to calibrate stakeholder expectations and high-level decisionmaking on funding in subsequent cycles. The allocation solution is primarily designed to address situations where (a) available pres- ervation funding is less than estimated or computed overall preservation needs and (b) asset performance or condition ratings for specific AAGs are below desired targets. Where the opposite is the case, we assume the allocation challenge is met by simply estimating the preservation needs for each asset group and jurisdiction and funding the need or possibly by reassessing the rating targets and priorities for a more aggressive preservation program. The allocation solution and model are not intended to have granularity below the District/ AAG level; rather the solution is intended to provide a logical total preservation funding envelope for an AAG within a jurisdiction—either district or state. Neither selection of specific projects nor preservation tactics are intended to derive from this logic model. These are considered technical decision within the work planning process. Solution options were developed for users to treat specific AAGs that lack key data or estimates to compute and optimize allocation needs in the way intended by the framework. Key Conclusions The Resource Allocation Logic Framework is fundamentally based on needs. Optimiza- tion applies to the adjustments of needed allocations to match available funds. A needs-based determination of allocation resources means that it is necessary (a) to connect preservation investments directly to expected performance/condition results and (b) to enable correction of expected performance/condition outcomes commensurate with any positive or negative allocation adjustments. Connecting investments to results requires reasonably reliable data or estimates on AAG-specific inventories, average performance/condition, average deterio- ration, and average preservation unit costs. Key conclusions include the following: • Based on literature review and interviews, each state DOT has unique practices, defini- tions, account structure, and taxonomies for the allocation of funds to preservation— there is no one-size-fits-all solution. • Inventory, performance/condition, deterioration, and preservation unit cost data avail- ability for non-bridge pavement (NBP) assets is very scarce among DOTs, making it challenging to apply a complete analytical approach for allocating resources, without sig- nificant estimating and judgment. • Agencies track performance metrics and asset inventory in unique ways, so the framework is flexible for a wide range of definitions of both asset-activity groupings and performance standards. • Deterioration is a very strong driver of preservation need. Where deterioration-based pres- ervation need exceeds funding allocations for any particular AAG, performance improve- ment is not possible; rather performance can be expected to regress. In these cases, optimized allocation of available funds would seek to minimize this regression across AAGs. Guidance Several suggestions are included for activities to support and enhance adoption of the Resource Allocation Logic Framework. These are further discussed in Chapter 7. 1. Average Deterioration Rates. Research is suggested to support improved methods for determination or estimating of average deterioration rates for various AAGs. This is a very important factor in the allocation logic framework.

4 resource allocation Logic Framework for Highway asset Preservation 2. Asset Inventory and Condition Management. Compilation of practical NBP inventory and condition assessment and management approaches (in use by DOTs) would be use- ful and helpful to potential users of the allocation logic framework, both for enhanced resource allocation and for other asset management purposes. 3. Objective Functions for Optimizing Resource Allocation. It makes sense that preferred optimization strategies (and therefore objective functions) would vary considerably across multiple DOTs. Prospective users of the allocation logic framework would benefit from the exercise of developing the right object functions and collecting ideas and prefer- ences from multiple peer agencies. 4. Added Case Applications for Logic Framework. Additional case applications would sup- port, build, and communicate confidence among prospective adopters of the Resource Allocation Logic Framework. 5. Methodology to Facilitate Initial Adoption of the Resource Allocation Logic Frame- work. The team envisions that an adopting DOT will want to populate a full version of the framework with the actual AAG taxonomy and real-time estimates or facts on inven- tory, unit costs, performance status, deterioration, and so forth. It will likely run and calibrate the allocation results in parallel with the normal allocation process. 6. Development and/or Sponsorship of Practitioner Training. Training support is likely to be needed at both strategic and modeler/operator levels for effective assessment, adoption, and implementation of the Resource Allocation Logic Framework, and to build accep- tance and deployment of the allocation framework. 7. Application of the Framework to Other Resource Allocation Areas. It may be use- ful to explore application of the logic more broadly to maintenance and infrastructure improvement programs. The logic model can support any application with any taxon- omy, a quantifiable inventory, performance ratings that can be normalized, and known average cost factors to link investment to results.