Developing adequate ship domain models may significantly benefit vessel navigation safety. In essence, navigation safety is collectively affected by the navigable waterway condition, the size and shape of the ship, and operators' skills. The existing ship domains mainly use constant values for the model input parameters, making them incapable of handling site-specific conditions. This study proposes dynamic ship domain models that take into consideration navigable waterway conditions, ship behaviours, ship types and sizes, and operators' skills in a holistic manner. Specifically, the conditions of restricted waterways are classified into navigating along the channel, crossing the channel, joining another flow and turning. The ship types considered include ships that transport non-hazardous goods and Liquid Natural Gas (LNG) ships that are in need of additional security zones. A computational experiment is conducted for model application using data on water channel design and ship traffic volumes related to navigating along the channel, joining another flow and turning. Comparisons of results obtained between the proposed dynamic models with real ship traffic counts reveal that the proposed models could achieve a higher level of accuracy in estimating the capacity of restricted water channels. It therefore could potentially deliver safety enhancements of waterway transportation.

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A methodology is proposed for multicriteria decision making involving trade-off analyses between candidate projects as well as project selection and programming in highway asset management under certainty, risk, and uncertainty. A set of system goals in highway asset management structure was first identified, and relative weights of the system goals were determined. Performance indicators under each goal were identified. Benefits achieved under various system goals as a result of implementing a project are typically measured with noncommensurable units; they need to be converted into nondimensional units so that trade-offs between projects can be measured under equal parameters. Where such conversion processes involve certainty and risk, this paper develops systemwide multiattribute utility functions for individual asset management programs to form the basis of trade-off analyses. Because of the limitation of utility theory for situations under uncertainty, an alternative approach based on Shackle's model was introduced, and corresponding functions for project trade-offs were calibrated. The two sets of functions were collectively termed "systemwide trade-off functions" and used to deal with cases under certainty, risk, and uncertainty. A case study was conducted for systemwide highway project selection using information on candidate projects in past state highway programming in Indiana. Case study results were further compared with actual highway programming practice in Indiana to validate the proposed methodology. High matching rates of at least 85% were achieved for decision making under certainty, risk, and uncertainty. The approach could be adopted by other state transportation agencies for asset management practice.

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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