United States Department of Transportation
i
Capacity-Flow Feature-Based Restoration Strategy Optimization for Resilient Transportation Systems To Enhance Mobility, Accessibility, and Equity After Disruptive Events
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2020-08-20
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[PDF-2.30 MB]
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Edition:Final Report
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Abstract:Disruptive events lead to capacity degradation of transportation infrastructure, and a good restoration plan can minimize the aftermath impacts during the recovery period. This is considered one aspect of resiliency for transportation systems. Although unmet demand has been proposed as one measure of resilience for freight transportation, it has rarely been used for general transportation systems. This study takes unmet demand and total travel time as two measures in modeling the restoration plan problem and proposes a bi-objective bi-level optimization framework to determine an optimal transportation infrastructure restoration plan. The lower-level problem uses Elastic User Equilibrium to model the imbalance between demand and supply and measures the unmet demand for a given transportation network. The upper-level problem, formulated as bi-objective mathematical programming, determines optimal resource allocation for roadway restoration. The bi-level problems are solved by a modified active set algorithm and a network representation method derived from Network Design Problems. The Weighted Sum Method is adopted to solve the Pareto Frontier of this bi-objective optimization problem. The proposed restoration plan optimization method was applied to a typical road network in Sioux Falls, Idaho, to verify the effectiveness of the methodology. For a given failure scenario, the Pareto Frontier of this bi-objective bi-level optimization problem with various budget levels, cross-referring to the travel efficiency of each solution, was illustrated to demonstrate how the proposed method can support decision-making for road network restoration. To further study the performance of the proposed method, different scenarios were generated with one to five links disrupted, and the proposed methodology was applied with different budget levels. The statistical analysis of the optimized solutions for these scenarios demonstrates that a higher budget could help reduce unmet demand in the system by providing more restoration options.
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