One of the key steps in the highway investment decision-making process is to conduct project evaluation. The existing project level life-cycle cost analysis approaches for estimating project benefits maintain limited capacity of probabilistic risk assessments of input factors such as highway agency costs, traffic growth rates, and discount rates. However, they do not explicitly address cases where those factors are under uncertainty with no definable probability distributions. This paper introduces an uncertainty-based methodology for highway project level life-cycle benefit/cost analysis that handles certainty, risk, and uncertainty inherited with input factors for the computation. A case study is conducted to assess impacts of risk and uncertainty considerations on estimating project benefits and on network-level project selection. First, data on system preservation and expansion, usage, and candidate projects for state highway programming are used to compute project benefits using deterministic, risk-based, and uncertainty-based analysis approaches, respectively. Then, the three sets of estimated project benefits are implemented in a stochastic optimization model for project selection. Significant differences are revealed with and without uncertainty considerations.

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A heuristic approach is developed for systemwide highway project selection. It can assess changes in total project benefits using different project implementation options under budget uncertainty and identify the best option to achieve maximized total benefits. The proposed approach consists of a stochastic model formulated as the zero/one integer doubly constrained multidimensional knapsack problem and an efficient heuristic solution algorithm developed using the Lagrange relaxation technique. A method is also introduced to improve the upper bound for the objective function by simultaneously changing multiple Lagrange multipliers. The approach is applied in a computational study to obtain a comprehensive highway investment plan for a Statemaintained highway system in the United States.

This paper develops a multiple criteria and multimethod approach for evaluating the suitability of alternative right-of-way (ROW) corridors to accommodate high-speed intercity passenger rail (HSIPR) operations. An alignment decision for HSIPR involves consideration of multiple criteria and trade-offs. Policy makers often face uncertainty in evaluating and selecting alternative options and usually consider alternatives in a fuzzy environment in which subjectivity and vagueness are present. This paper develops an integrated evaluation model that uses the fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methods to address some of these issues. The study identifies potential sketch-planning performance metrics and demonstrates their usefulness and inclusion in the integrated fuzzy AHP–TOPSIS methodology in comparing alternative ROW corridor options. In contrast to detailed engineering evaluation, the developed sketch-planning metrics provide a cost- and time-effective way of assessing alternative suitability. A case study application with existing highway ROW in Texas demonstrates the applicability of the proposed framework. Apart from ranking alternatives, a detailed sensitivity analysis assesses the effect of performance metrics weights on the preferences between alternatives using a displacement index. The proposed framework creates a more effective and systematic decision support tool for preliminary corridor alternative evaluation.