The evaluation and prioritization of transportation investments at the portfolio level presents a complex decision-making problem for state and regional planning organizations. Historically, transportation investment decisions have been dealt with as a series of stand-alone problems to be resolved using straight-forward engineering solutions. In this context, improvement needs and project solutions were identified based on simple criteria, such as traffic congestion levels. Investment portfolio optimization was then accomplished by listing projects in order of most to least congestion reducing, and then allocating funding to projects by rank until funding was exhausted. Increasing awareness of the complex interdependencies among transportation, land-use, social, economic and ecological systems has fostered implementation investment prioritization approaches that incorporate increasingly more complex goals and metrics.

The simplest among these newer decision models is the Weighted Sum Model (WSM). More rigorous methods, such as the Analytical Hierarchy Process (AHP) and the Technique for Ordered Preference by Similarity to Ideal Solution (TOPSIS), provide increased functionality, and support prioritization driven by asset performance and financial return in addition to engineering criteria. However, the lack of transparency associated with many of these more rigorous methodologies has a downside, a certain level of distrust in the final results.

To build support for and confidence in the equity of project prioritization for the 2040 Regional Transportation Plan Update, the Pikes Peak Area Council of Governments has engaged collaborating partners in a process that utilized parallel project scoring and prioritization with four separate scoring methodologies: 1) Rank and Cut; 2)WSM; 3) TOPSIS; and LSP.

This paper examines the result produced by each of the methodologies, the suitability of each decision model for the optimization of transportation investment priorities across PPACG’s full program/portfolio, and partner acceptance of results. The application of each differing approach is contrasted alternative approaches. Functionality, advantages, and disadvantages of each approach are discussed, and potential enhancements are identified. The effectiveness of the multi-technique scoring exercise in building project prioritization consensus is also examined.