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The network-level infrastructure management problem involves selecting and scheduling Maintenance, Repair, and Rehabilitation (MR&R) activities on networks of infrastructure facilities so as to maintain the level of service provided by the network in a cost-effective manner. This problem is frequently formulated as a Markov Decision Problem (MDP) solved via Linear Programming (LP). The conditions of facilities are represented by elements of discrete condition rating sets, and transition probabilities are employed to describe deterioration processes. Epistemic and parametric uncertainties not considered within the standard MDP/LP framework are associated with the transition probabilities used in infrastructure management optimization routines. This paper contrasts the expected costs incurred when model uncertainty is ignored with those incurred when this uncertainty is explicitly considered using Robust Optimization. A case study involving a network-level pavement management MDP/LP problem demonstrates how explicitly considering uncertainty may limit worst case MR&R expenditures. The methods and results can also be used to identify the costs of uncertainty in transition probability matrices used in infrastructure management systems.

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The utility of route guidance and trajectory prediction tools in air traffic management is directly related to how well such tools anticipate pilot and controller reactions to weather. This paper presents a new method for translating weather data into patterns in aggregated aircraft trajectories. Techniques are described that limit the human and computational effort required to analyze large sets of data and enable formulation and discovery of mathematical relationships among large numbers of weather-and flight plan-related variables. The method is used to examine the effects of thunderstorms on aggregate aircraft operations near Atlanta in the spring and summer of 2007. Measures of precipitation intensity and storm cell height were related to aircraft positions over a period of 40 days. A mathematical model of the relationship between precipitation intensity, storm cell height, flight level, and airspace occupancy was constructed using multivariate adaptive polynomial spline regression. Explanatory power was lost when aircraft altitude and storm cell height readings were combined into a measure of their difference. Precipitation intensity contributed surprisingly little discriminatory power to the built model. Aircraft sought to avoid airspace within 5 km of storm activity, rerouting to airspace 10 km to 20 km and farther from the storm.

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