Transportation infrastructure restoration optimization considering mobility and accessibility in resilience measures
Transportation infrastructure restoration optimization considering mobility and accessibility in resilience measures
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DOI：
10.1016/j.trc.2020.102700
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发表时间：
2020-08
影响因子：
8.3
通讯作者：
Tingting Zhao;Yu Zhang
中科院分区：
文献类型：
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作者：
Tingting Zhao;Yu Zhang
破坏性事件导致交通基础设施的能力下降，良好的恢复计划可以在恢复期间最大限度地减少后果影响。这被认为是运输系统弹性的一个方面。虽然未满足的需求已被提议作为衡量货运弹性的一个指标，但很少用于一般运输系统。本研究以未满足需求和总行程时间为两个衡量标准，建立了一个双目标双层优化框架，以确定最优的交通基础设施恢复计划。下层问题使用弹性用户平衡来模拟需求和供应之间的不平衡，并测量给定交通网络的未满足需求。上层问题，制定为双目标数学规划，确定最佳的资源分配巷道恢复。两层问题的求解采用改进的有效集算法和网络设计问题的网络表示方法。采用加权和法求解该双目标优化问题的Pareto前沿。以苏福尔斯市典型路网为例，验证了该方法的有效性。在给定的故障场景下，通过求解不同预算水平下的双目标双层优化问题的Pareto边界，并与各解的出行效率进行交叉对比，验证了该方法对路网恢复决策的支持作用.为了进一步研究所提出的方法的性能，产生了不同的场景，其中一到五个链路中断，并在不同的预算水平下应用所提出的方法。对这些场景的优化解决方案的统计分析表明，更高的预算可以通过提供更多的恢复选项来帮助减少系统中未满足的需求。
Disruptive events lead to capacity degradation of transportation infrastructure, and a good restoration plan could 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, 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.