Deep Reinforcement Learning-based Project Prioritization for Rapid Post-Disaster Recovery of Transportation Infrastructure Systems

Among various natural hazards that threaten transportation infrastructure, flooding represents a major hazard in Region 6's states to roadways as it challenges their design, operation, efficiency, and safety. The catastrophic flooding disaster event generally leads to massive obstruction of traffic, direct damage to highway/bridge structures/pavement, and indirect damages to economic activities and regional communities that may cause loss of many lives. After disasters strike, reconstruction and maintenance of an enormous number of damaged transportation infrastructure systems require each DOT to take extremely expensive and long-term processes. In addition, planning and organizing post-disaster reconstruction and maintenance projects of transportation infrastructures are extremely challenging for each DOT because they entail a massive number and the broad areas of the projects with various considerable factors and multi-objective issues including social, economic, political, and technical factors. Yet, amazingly, a comprehensive, integrated, data-driven approach for organizing and prioritizing post-disaster transportation reconstruction projects remains elusive. In addition, DOTs in Region 6 still need to improve the current practice and systems to robustly identify and accurately predict the detailed factors and their impacts affecting post-disaster transportation recovery. The main objective of this proposed research is to develop a deep reinforcement learning-based project prioritization system for rapid post-disaster reconstruction and recovery of damaged transportation infrastructure systems. This project also aims to provide a means to facilitate the systematic optimization and prioritization of the post-disaster reconstruction and maintenance plan of transportation infrastructure by focusing on social, economic, and technical aspects. The outcomes from this project would help engineers and decision-makers in Region 6's State DOTs optimize and sequence transportation recovery processes at a regional network level with necessary recovery factors and evaluating its long-term impacts after disasters.在威胁交通基础设施安全的各类自然灾害中，洪涝灾害是第六大区（Region 6）各州公路面临的主要灾害类型，其对道路的设计、运营、通行效率与安全性能均构成严峻挑战。灾难性洪涝事件通常会引发大面积交通阻断，直接损毁公路、桥梁结构及路面，同时对经济活动与区域社区造成间接损害，甚至可能导致大量人员伤亡。灾害发生后，对海量受损交通基础设施系统开展修复与维护工作，要求各运输部（Department of Transportation, DOT）投入巨额成本并历经漫长周期。此外，统筹规划与组织交通基础设施灾后重建及维护项目，对各DOT而言极具挑战性：此类项目体量庞大、覆盖范围广泛，且涉及社会、经济、政治与技术等多类复杂因素及多目标决策问题。然而目前，尚未形成一套全面集成、以数据为驱动的方法，用以统筹组织并优先排序交通基础设施灾后重建项目。与此同时，第六大区的各DOT仍需优化现有实践与系统，以稳健识别并精准研判影响灾后交通恢复的各类具体因素及其作用机制。本拟开展研究的核心目标，是开发一套基于深度强化学习（Deep Reinforcement Learning）的项目优先级排序系统，以实现受损交通基础设施系统的快速灾后重建与恢复。本项目还旨在聚焦社会、经济与技术维度，为交通基础设施灾后重建与维护计划的系统化优化及优先级排序提供支撑框架。本项目的研究成果，将助力第六大区各州DOT的工程师与决策者，在区域路网层面优化交通恢复流程并确定实施时序，同时纳入必要的恢复影响因素，并评估灾害发生后的长期综合影响。