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1 Background Transportation agencies face a complex set of challenges as they make cross-asset resource allocation decisions. Such decisions entail deciding how much to invest in an agencyâs roads, bridges, intelligent transportation systems (ITS), and other traffic and safety assets to achieve a variety of competing objectives, such as improving pavement and bridge conditions, increasing mobility, and enhancing safety. An agency must consider a number of factors in making decisions about specific projects to fund across different investment objectives and asset categories, including, but not limited to, what funds are available, what candidate projects may be performed, and the impacts different projects may have on system performance. A number of stakeholdersâagency staff, legislators; private citizens; metropolitan planning organizations (MPOs); and other local, state and federal agenciesâmay participate in the decision-making process. Making resource allocation decisions that satisfy all stakeholders is inherently complicated and is made even more difficult when coupled with the challenges of constrained resources, aging infrastructure, and increasing traffic. Increasingly transportation agencies are embracing the use of data-driven and performance- based approaches for allocating funds across asset and investment categories considering multiple objectives. Using a structured approach to making decisions about investments has a number of potential benefits. Primarily, it can guide an agency to make investments that best support their goals and objectives and that make the best use of available funds. Also, a structured approach is repeatable and can be readily documented, providing increased transparency to investment decisions and enhancing agency credibility. Recognizing these benefits, a number of transporta- tion agencies have implemented, or are in the process of implementing, structured approaches for making cross-asset, multi-objective investment decisions. Two prior National Cooperative Highway Research Program (NCHRP) research efforts have explored the topic of cross-asset resource allocation. NCHRP Report 545: Analytical Tools for Asset Management (1), completed in 2005, reviews available resource allocation tools and approaches. More recently, NCHRP Report 806: Guide to Cross-Asset Resource Allocation and the Impact on Transportation System Performance (2), published in 2015, presents a framework and guidance for cross-asset resource allocation and includes a prototype spreadsheet tool illustrat- ing the approach detailed in the guidance. Research Objective This report details the results of NCHRP Project 08-103. This project was undertaken to implement and extend the results of NCHRP Report 806. The project focused on performing a set of case studies of transportation agencies implementing cross-asset, multi-objective C H A P T E R 1 Introduction
2 Case Studies in Implementing Cross-Asset, Multi-Objective Resource Allocation decision-making approaches. In some instances, the case studies utilized an updated version of the NCHRP Report 806 tool, while in other cases they used a variety of different agency- developed and commercial-off-the-shelf (COTS) software tools. The case studies were intended to help illustrate key issues in implementing a cross-asset resource allocation approach. These lessons were then used to improve the guidance and tools developed previously for NCHRP Report 806. Research Approach The research detailed in this report was performed in two phases: design and execution. In the design phase, the research team performed a literature review and detailed the case study approach to be executed in the second phase. Also in this phase, the research team conducted an âalpha testâ with the Utah Department of Transportation (UDOT) to guide the development of the case studies. The alpha test provided valuable feedback on the case study format and lessons learned in implementing decision support software. This helped define the approach for the execution phase of the research. In the execution phase of the research, the team conducted case studies with four agencies: â¢ Arizona Department of Transportation (ADOT), â¢ California Department of Transportation (Caltrans), â¢ Delaware Valley Regional Planning Commission (DVRPC), and â¢ Maryland Department of Transportation (MDOT) and State Highway Administration (MDOT SHA) While the activities for each case study varied slightly, generally all the case studies involved walking through the process of implementing a cross-asset resource allocation approach for the first time or documenting work the agency had done in the past. This also involved implement- ing the spreadsheet tool developed in NCHRP Report 806. Using the lessons learned from these case studies, several improvements were made to the spreadsheet tool to make it more easily accessible and useful for the user. In addition, a web tool was developed to enable the use of Data Envelopment Analysis (DEA) in the optimization step of the implementation process and to demonstrate use of a web service transportation agencies can use in their own web applications to automate DEA analyses. This technique is described in the Appendix and was explored in two of the case studies. Finally, the case studies helped inform the development of guidance on implementing a cross-asset resource allocation approach that builds on the initial guidance detailed in NCHRP Report 806. Report Organization The remainder of this document is organized into the following chapters: â¢ Chapter 2 provides an overview of cross-asset resource allocation. â¢ Chapter 3 describes the four case studies performed for the project: ADOT, Caltrans, DVRPC, and MDOT/MDOT SHA. â¢ Chapter 4 details step-by-step guidance on implementing a cross-asset resource allocation approach at a state DOT or local transportation agency. â¢ Chapter 5 provides documentation of the spreadsheet tool and web tool developed to aid in implementing cross-asset resource allocation. â¢ Chapter 6 presents the conclusions of the research and recommends areas for further research. â¢ The Appendix provides an overview of DEA.
Introduction 3 Note that this report is intended as a companion to NCHRP Report 806. The reader should refer to this earlier report for additional information, including an overview of cross-asset resource allocation, discussion of the benefits of a cross-asset resource allocation approach, and the data required to support cross-asset resource allocation. References 1. Cambridge Systematics, Inc., PB Consult, and System Metrics Group, Inc. NCHRP Report 545: Analytical Tools for Asset Management. Transportation Research Board of the National Academies, Washington, D.C., 2005. 2. Maggiore, M., K. Ford, High Street Consulting Group, and Burns & McDonnell. NCHRP Report 806: Guide to Cross-Asset Resource Allocation and the Impact on Transportation System Performance. Transportation Research Board of the National Academies, Washington, D.C., 2015.