Transport infrastructure public private partnership (PPP) projects are very diverse and complex in nature not only because of their mode-specific intricacies but also because of their inherent economic characteristics that relate to the scope of involvement of the private sector in the project, the large sunk costs incurred, and ultimately, the competition to which these projects are exposed. The allocation of revenue risk is of paramount importance for the successful implementation of such projects and a sub-optimal allocation may lead to project structuring that is unnecessarily expensive and vulnerable to failure. At the same time, the revenue risk depends critically on the remuneration model used (user-based versus budget-based) and may, in turn, take the form of demand risk, counterparty risk or combinations of the two. This review explores the issues related to revenue risk allocation for transport infrastructure PPP projects. Overarching principles for the allocation of revenue risk that transcend mode-specificity are identified and compared to case studies generated in the context of the COST Action TU1001. The results show that theory and practice are divergent, leading to suboptimal structuring and exposing projects to potential failure.
Transportation agencies engage in extensive data collection activities to support their decision processes at various levels, but not all data collected supply useful information. This article summarizes research aimed at formally identifying links between data collection and the supported decision processes, particularly at the level of project selection. The aim was to help transportation agencies optimize their data collection and cut down data collection and management costs. The methodology included a comprehensive literature review that collected information from various academic and industry sources around the world and the development of a Web survey that was e-mailed to specific expert individuals within the 50 U.S. Departments of Transportation and Puerto Rico. The responses obtained from the Web survey were analyzed statistically and combined with the additional resources to extract conclusions about the current state of the practice and develop data collection recommendations in the form of a proposed stepwise framework.
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