Learning long term climate-resilient transport adaptation pathways under direct and indirect flood impacts using reinforcement learning
This work addresses the problem of climate-resilient transport planning for cities, offering a decision-support tool that is incremental as it builds on existing RL and IAM methods.
The researchers tackled the challenge of designing long-term climate adaptation strategies for urban transportation systems under uncertainty by proposing a reinforcement learning framework coupled with an integrated assessment model, which learned adaptive investment pathways for Copenhagen from 2024 to 2100, showing improved robustness compared to baselines like inaction and random action.
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.