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Transportation Network Data Requirements for Assessing Criticality for Resiliency and Adaptation Planning
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    This report is one of two NCST Research Report documents produced as part of a project to advance the technical modeling tools for resiliency and adaptation planning, especially those used for criticality rankings. The official final technical report, Climate Adaptation and Resiliency Planning: Agency Roles and Workforce Development Needs, summarizes a climate adaptation framework and describes current planning practices and workforce needs of Departments of Transportation and other planning agencies. This additional technical documentation report looks specifically at the network data challenges of objectively assessing asset criticality, one step in the larger adaptation planning framework and a prerequisite for efficient allocation of limited adaptation resources. Specifically, this report explores the modeling resolution (in terms of the completeness of the road network and the spatial disaggregation of origin and destination matrices) needed to produce accurate criticality ratings. Original modeling work using a well-establish criticality measure, the Network Robustness Index (NRI), on both a small hypothetical network and the road network for Chittenden County, Vermont, demonstrated a need for higher resolution networks for criticality modeling. Since this part of the work was published in the Transportation Research Record it is only summarized here. A conceptual discussion of methods explored for creating networks forlarger real-world areas that are sufficiently complete for criticality assessment is also included based on exploratory work using the travel demand model for the Greater Sacramento California area. Our work demonstrated that network resolution has a significant impact on link criticality rankings using both hypothetical and small real-world networks. Resolution reductions that removed 40% of the road mileage and only 25% of road capacity in the hypothetical network resulted in nine of the top 10 most critical links being misidentified in the hypothetical network tests. On the real-world network in Chittenden County, resolution reductions that eliminated 68% of road centerline miles and 57% of network capacity to a typical planning agency model resolution resulted in the misidentification of half of the top 10 most critical links and a quarter of the top 200 most critical links. These results provide strong evidence that lower resolution networks such as those typically used for travel demand models by state and regional planning agencies are inadequate for criticality analysis.

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