Learning long term climate-resilient transport adaptation pathways under direct and indirect flood impacts using reinforcement learning

TL;DR

The paper tackles climate-induced disruptions to urban transport by presenting a modular IAM+RL framework that learns long-horizon, multi-zone adaptation pathways under deep uncertainty. It formulates the planning problem as an MDP defined over a graph-based city representation, coupling climate forcing, hazard propagation, transport dynamics, and monetized impacts within a graph-enabled RL policy trained by PPO. In a Copenhagen inner-city case study, the learned policies outperform No Control and Random baselines, producing coordinated spatial-temporal investments and demonstrating robustness across climate scenarios. The work provides a practical, decision-support tool for stress-testing adaptation portfolios, with potential extensions to other hazards and metropolitan scales, and highlights the trade-offs between precautionary investment and avoided disruption.

Abstract

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.

Figures (4)

• Figure 1: Integrated Assessment Model using reinforcement learning to find the best sequence of transport-related adaptation policies for rainfall events in Copenhagen from 2024--2100.
• Figure 2: Comparison of different adaptation strategies. Total reward (top-left, scaled by $10^8$), and reward components are shown: infrastructure damage costs (top-middle), travel delays (top-right), travel cancellations (bottom-left), actions direct costs (bottom-middle), and action maintenance costs (bottom-right). All values correspond to Danish Kroner (DKK). Please note the different y-axis scale across figures.
• Figure 3: Average (and standard deviations shaded) for all five reward components over the period 2024--2100 under the RCP4.5 climate scenario. While Random Control reduces impacts through high and persistent investment costs, the RL policy achieves sustained impact reduction with substantially lower and more stable adaptation expenditures. All values correspond to Danish Kroner (DKK). Please note the different y-axis scale across subfigures.
• Figure 4: Adaptation measures taken over time per zone in Copenhagen's city center for one episode (run) for a RCP4.5 scenario.