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【背景】震后道路网络修复过程中，不确定性与修复决策、出行行为之间的相互作用对网络韧性具有关键性影响。然而，现有研究多基于确定性假设或静态拓扑指标，往往忽视对修复决策与交通流分配机制之间耦合关系的系统刻画。【目标】本文旨在构建一个能够同时刻画震后不确定性与出行者路径选择行为响应的优化框架。同时，为了提升震后道路网络修复策略的有效性，本文建立考虑损伤状态和修复效率不确定性的双层规划修复方法。【方法】上层模型以网络韧性与修复速度韧性为双目标优化，下层模型基于交通分配刻画修复决策驱动下的流量分布。在求解方面，本文设计NSGA-Ⅱ与Frank-Wolfe算法相结合的嵌套求解框架，并通过Monte Carlo模拟评估方案在不确定环境下的表现。【结果】通过在汶川地震德阳市路网开展数值实验，结果表明所提方法得到的路网韧性与修复速度韧性指标的期望值分别达到0.776和0.394，且波动性较低；相较于确定性模型，网络通行效率提升约20%；此外，模型倾向于优先修复对网络连通性与通行效率具有关键影响的路段，从而提高资源配置效率。【结论】本文所提方法能够有效刻画修复决策与出行行为之间的交互影响，提升震后道路网络修复策略的稳健性与系统恢复性能，可为灾后交通基础设施恢复决策提供参考。
Abstract：[Background] During post-earthquake road network restoration， the interplay among uncertainty， restoration decisions， and traveler behavior critically affects network resilience. However， existing studies largely rely on deterministic assumptions or static topological metrics and often overlook the coupling between restoration decisions and traffic assignment mechanisms. [Objective] This paper aims to develop an optimization framework that captures both post-earthquake uncertainties and traveler route choice behavior. Meanwhile， to enhance the effectiveness of restoration strategies， the bi-level programming method is established that accounts for uncertainties in damage states and restoration efficiency. [Method] The upper level model optimizes network resilience and restoration speed resilience as bi-objectives， while the lower level model uses a traffic assignment model to describe the restoration-driven flow distribution. The nested solution framework combining NSGA-II and Frank-Wolfe algorithms is designed， and Monte Carlo simulation is employed to evaluate strategy performance under uncertainty. [Results] Through a numerical experiment is conducted on the road network of Deyang City in the Wenchuan Earthquake. The results show that the proposed method achieves expected network resilience and restoration speed resilience of 0.776 and 0.394， respectively， with low variability. Compared with the deterministic model， network traffic efficiency improves by approximately 20%. Moreover， the model tends to prioritize restoring critical links that significantly affect network connectivity and traffic efficiency， thereby improving resource allocation. [Conclusion] The proposed method effectively captures the interaction between restoration decisions and traveler behavior， enhances the robustness and system recovery performance of post-earthquake restoration strategies， and provides a quantitative reference for post-disaster transportation infrastructure restoration decision-making.
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基本信息:
DOI：10.19961/j.cnki.1672-4747.2026.04.008
中图分类号:U491
引用信息:
[1]叶子欣,陈正贤,徐占东,等.考虑震后不确定性的道路网络韧性修复决策方法[J].交通运输工程与信息学报().DOI:10.19961/j.cnki.1672-4747.2026.04.008.
基金信息:
国家自然科学基金项目（72571224）; 四川省科技计划“骈骥”项目（2025HJPJ0011）
2026-05-18
2026-05-18
2026-05-18