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

2026-01-26

AI Summary

This study addresses the long-term, uncertain, and cross-sectoral risks posed by flood-induced disruptions to urban transportation systems under climate change. To this end, it proposes a decision-support framework that integrates an integrated assessment model with deep reinforcement learning. The framework couples IPCC climate scenarios, flood hazard simulations, and traffic disruption impact assessments to quantify both direct damages and indirect societal costs, enabling the optimization of adaptive investment pathways over multi-decadal timescales. Innovatively, it achieves the first synergistic integration of reinforcement learning with integrated assessment modeling, yielding spatially and temporally consistent, robust resilience investment strategies. Applied to Copenhagen from 2024 to 2100, the proposed approach significantly outperforms both no-action and random baselines, demonstrating superior adaptability and transferability.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• climate resilience
• transport adaptation
• flood impacts
• long-term planning
• urban infrastructure

Innovation

• reinforcement learning
• climate adaptation pathways
• integrated assessment model
• urban transport resilience
• pluvial flooding