Maximizing economic returns for large, publicly funded road infrastructure investments through improved economic, engineering and construction information management

The objective of this thesis is to develop a framework to maximize economic returns for large road infrastructure projects in developing countries. The literature has outlined many of the numerous challenges in roads projects in developing countries from corruption, poor quality work, and cost and time overruns. This is confirmed, to various degrees, by recent field experience in developing countries in the delivery of roads projects. Incentives of both the donor agency and the road agency have contributed to this underperformance, with a mix of insufficient accountability for results. Some authors question the economic efficiency of roads investments in developing countries by donor agencies, pointing to some evidence indicating that the ex post economic returns are not matching up to the ex-ante economic returns promised, to the extent an economic analysis was performed. The framework developed under this thesis attempts to align the incentives of the donor agency and road agency, while providing a higher degree of transparency and accountability into the investment decision making process, but also the engineering design and construction phase with a focus on the longer term sustainability of the road investment through increased adaption of modern pavement asset management techniques, graphical capabilities, engineering diagnosis, and statistical analysis. The first part of the framework is the development of a fast-track road network assessment tool in MS Excel, using the HDM-4 approach to rapidly identify the most economically lucrative investments in a country to consider for further investment. This approach also allows the donor agency to validate the ability of the road agency to properly assess two prime elements of the pavement asset management system: the International Roughness Index (IRI) and Average Annual Daily Traffic (AADT); this is an important confirmation of the road agencies’ ability to properly maintain the pavement asset post investment. The second part of this framework is the development of a graphic-based itinerary diagram approach to identify the cause(s) of deterioration and develop appropriate engineering solutions that minimize investment costs while maximizing economic returns. This framework is also built around the HDM-4 model to show the primary engineering elements driving the economic assessment, which provide the needed justification of the economic models, but also serve as a project implementation monitoring and evaluation tool that can be used at the end of the project to determine the ex post economic analysis. The third and final part of the framework is the development of a Bayesian approach in coordination with the itinerary diagram approach to help road agencies determine the most probable cause(s) of deterioration on their road network. This will improve overall pavement design through empirical methods, but also improve the economic prioritization of the road network. The Bayesian method is meant to improve the accuracy of the pavement management system through adaptive learning. Through the identification of the cause of deterioration on similar road families, the pavement management system will be able to forecast the most likely cause(s) of deterioration on similar roads families, which allows the road agency the ability to intervene through a more timely and economically efficient manner through the reduction of vehicle and road agency costs. The objective of this part is to begin the development of the needed pavement design “know how” at the road agency to improve overall long-term sustainability of the road investments, which can improve long-term economic returns. The primary recommendations of the thesis are to continue the refinement of each of these three parts, which have already been field-tested to various degrees, to optimize each element for further use and deployment.