Optimal investment decision-making for highway transportation asset management under risk and certainty

Abstract

Efficient highway investment decision-making becomes increasingly important in transportation. In order to facilitate such a decision process, first issue is to estimate benefits of highway projects and utilize those values for project selection to yield optimal investment decisions. The existing methodologies for highway project evaluation are limited to probabilistic risk assessments of input factors such as construction, rehabilitation, and maintenance costs, travel demand, and discount rates that are inherited with risks. This research introduces a new approach for highway project evaluation extended from Shackle's model to explicitly address cases where those factors are under uncertainty with no definable probability distributions. Then, a generalized methodology for highway project evaluation with input factors under certainty, risk, and uncertainty is established. If an input factor is under certainty, its single value is directly used. If an input factor is under risk, the mathematical expectation of the factor based on probabilistic risk assessment can be determined. If an input factor is under uncertainty, a single-valued outcome of the factor can be estimated according to a preset decision rule in the extension of Shackle's model. The values of input factors separately determined under certainty, risk, and uncertainty can be used to compute the overall benefits of a highway project in the physical asset's one service life-cycle and in perpetuity horizon, respectively. The developed methodology offers flexibility for the decision-maker to consider any combination of input factors under certainty, risk or uncertainty and it could be applied to estimate the amount of benefits associated with sub-project benefit items (if a specific benefit item is further separable) under certainty, risk, or uncertainty in accordance with available information. For project selection, a stochastic optimization model is developed as the multi-choice multidimensional Knapsack problem with ?-stage budget recourses. The model facilitates the selection of a subset of candidate highway projects across a multiyear period under budget uncertainty in order to achieve maximized overall project benefits. Contract-, corridor-, and deferment-based tradeoff methods are employed to assess the impacts of spatial and temporal restrictions on project selection results. An efficient solution algorithm with the computational complexity of O(N2) is developed for the proposed stochastic model. A case study using data on state highway programming in Indiana for period 1996-2006 is conducted to apply the methodology for project evaluation with input factors under certainty, risk, and uncertainty, and the stochastic model for project selection under budget uncertainty. Cross comparisons of project benefits estimated with and without uncertainty considerations are made. The overall benefits of projects selected using different tradeoff analysis methods in the stochastic model are compared. Furthermore, the respective project selection results are matched with the actual programming decisions and relatively high consistency rate is obtained. The new methodology and model can be adopted by state transportation agencies to improve the efficiency of highway investment decisions.