Use of Multi-Objective Decision Analysis for Resource Allocation
Multi-Objective Decision Analysis (MODA) can be used to prioritize specific candidate investments considering multiple, potentially competing objectives. Though this approach is data-intensive, it provides the means for evaluating investments that combine multiple types of assets or investments that help achieve multiple objectives.
In recent years, interest has increased in using MODA to improve approaches for prioritizing investments across asset classes and investment categories. The basic benefit of this approach is that it provides a structure for prioritizing investments outside the scope of any one management system, such as projects combining pavement, bridge and safety improvements. It also provides a means to compare asset management investments with other investments to improve mobility and achieve other objectives outside the scope of a typical asset management system.
This approach is, however, more data intensive and may result in simplification of the asset-specific modeling performed in a pavement or bridge management system. MODA tools and approaches are typically intended for application in analyzing specific candidate projects, and used for prioritizing investments for a single decision period. However, it is possible to adapt a MODA approach for cases where data are sparse or where groups of investments are analyzed rather than specific investments, or where longer decision periods are considered.
NCHRP Report 806: Guide to Cross Asset Resource Allocation and the Impact on Transportation System Performance presents a framework and prototype tool for implementing a MODA-based approach. Additional research through NCHRP Project 08-103 extended the framework and updated the tool. A checklist based on this work is included in this section; it outlines key issues for an agency considering improvements to its resource allocation approach to better account for multiple objectives across asset classes or investment categories.
North Carolina DOT
Since 2009, North Carolina DOT has used a structured approach to help prioritize capital investments across modes and asset classes. The initial version of the approach (Version 1.0) focused on prioritizing mobility and highway modernization projects supported by data on congestion, crashes and pavement condition. Over time the process evolved to include additional investment types and data. North Carolina’s Strategic Transportation Investments Law adopted in 2013 helped formalize the process, requiring that NCDOT allocate 40% of its available funds for mobility to Statewide Mobility projects that address congestion and bottlenecks, 30% of funds to projects with Regional Impact that improve connectivity within Regions, and 30% of funds to projects that address local needs. Different approaches are used for prioritizing investments in each mode (highways, aviation, bicycle-pedestrian, public transportation, ferry and rail) within each of these three groups. In Version 5.0 of the process, implemented in 2018, 24 different types of improvements are considered for highways. Candidate projects are scored in 10 areas, including pavement condition, considering a mix of existing conditions and predicted conditions as a result of the proposed project, as illustrated in the figure.
Highway Scoring - Eligible Criteria with P5.0 Measures
|Criteria||Measure||Existing Conditions||Project Benefits (Future Conditions)|
|Congestion||Volume/Capacity + Volume|
|Benefit/Cost||(Travel Time Savings + Safety Benefits)/Cost to NCDOT|
|Safety/Score||Critical Crash Rate, Density, Severity, Safety Benefits|
|Economic Competitiveness||% Change in Jobs + % Change in County Economy|
|Accessibility/Connectivity||County Economic Indicator, Improve Mobility|
|Freight||Truck Volume, Truck %, Future Interstate Completion|
|Lane Width||Existing Width vs. Standard Width|
|Shoulder Width||Existing Width vs. Standard Width|
|Pavement Score||Pavement Condition Rating|
The approach for allocating funding within the Caltrans State Highway Operation and Protection Program (SHOPP) is an example of a “bottom-up” multi-objective, cross-asset resource allocation approach. The SHOPP funds repair, preservation, and safety improvements on the California State Highway System (SHS). The SHS is comprised of approximately 50,000 lane miles and the 2018 SHOPP will implement $17.96 billion in projects over four years. The SHOPP programming cycle results in a four-year program of capital projects that achieve the performance targets specified in the TAMP, consider the fiscal constraints, and address the needs identified in the State Highway System Management Plan.
In an effort to make the process more data-driven, Caltrans piloted a MODA approach to prioritize projects for inclusion in the SHOPP. The agency used the goal areas identified in their Strategic Plan (Safety and Health; Stewardship and Efficiency; Sustainability, Livability, and Economy; System Performance; and Organizational Excellence) and established criteria to evaluate projects across the five goals. In the initial pilot, Caltrans focused on obtaining the technical data necessary to evaluate how well each project progressed towards its goals. The agency is in the process of refining the approach based on the results of the pilot. They revised the goal areas to best account for all the activities included in the project. In addition, they represent each project score through a monetized benefit value, which addresses challenges related to scaling and weighting. With the revised approach, projects are scored based on the annual benefit of performing the project relative to deferring work for one decision period (two years). Benefits predicted using the approach are analogous to monetized benefits predicted using benefit/cost analysis tools and approaches, such as the Cal-B/C tool Caltrans uses to evaluate potential highway improvements. This approach leverages prior work performed to quantify the benefits of a proposed investment, and helps address issues with scaling and weighting different measures of benefit encountered in the initial pilot.
TAM Peer Exchange Presentation
NCHRP 08-103, Preliminary Draft Final Report