Climate change is expected to intensify rainfall and other hazards, increasing disruptions in urban transportation systems. Designing effective adaptation strategies is challenging due to the long-term, sequential nature of infrastructure investments, deep uncertainty, and complex cross-sector interactions. We propose a generic decision-support framework that couples an integrated assessment model (IAM) with reinforcement learning (RL) to learn adaptive, multi-decade investment pathways under uncertainty. The framework combines long-term climate projections (e.g., IPCC scenario pathways) with models that map projected extreme-weather drivers (e.g. rain) into hazard likelihoods (e.g. flooding), propagate hazards into urban infrastructure impacts (e.g. transport disruption), and value direct and indirect consequences for service performance and societal costs. Embedded in a reinforcement-learning loop, it learns adaptive climate adaptation policies that trade off investment and maintenance expenditures against avoided impacts. In collaboration with Copenhagen Municipality, we demonstrate the approach on pluvial flooding in the inner city for the horizon of 2024 to 2100. The learned strategies yield coordinated spatial-temporal pathways and improved robustness relative to conventional optimization baselines, namely inaction and random action, illustrating the framework's transferability to other hazards and cities.

翻译：气候变化预计将加剧降雨等灾害，从而增加城市交通系统的中断风险。由于基础设施投资具有长期性和时序性、存在深度不确定性以及复杂的跨部门相互作用，设计有效的适应策略具有挑战性。我们提出一个通用的决策支持框架，该框架将综合评估模型与强化学习相结合，以在不确定性下学习适应性的、长达数十年的投资路径。该框架将长期气候预测与多个模型相结合：这些模型将预测的极端天气驱动因子映射为灾害发生概率，将灾害传播至城市基础设施影响，并评估其对服务性能和社会成本的直接与间接后果。通过嵌入强化学习循环，该框架能够学习权衡投资维护支出与避免损失的气候适应策略。在与哥本哈根市政府的合作中，我们以2024年至2100年为规划期，针对内城雨洪问题对该方法进行了验证。学习得到的策略产生了协调的时空路径，相较于传统优化基准（即不作为和随机行动）展现出更强的鲁棒性，这证明了该框架可推广至其他灾害类型和城市。