A model-based adaptive planning framework using surrogate modelling for urban transport systems under uncertainty

Abstract

Adaptive planning helps cities adapt to an uncertain future environment by providing guidance on the required interventions, conditional on how the future evolves, to best achieve planning goals. Such plans can be identified through detailed agent-based models, but they are usually computationally expensive, limiting their ability to run repeatedly for multiple scenarios. This paper proposes a framework for developing adaptive plans for urban transport systems using surrogate models (i.e. fast approximations) of detailed models to determine which adaptive plans, as sequences of interventions, best achieve multiple objectives for a large number of possible future scenarios. The framework is empirically validated with an agent-based urban transport model (MATSim) of a Singaporean neighbourhood and a surrogate model that reduces computational time by five orders of magnitude. The framework allows the interaction between planners and stakeholders to evaluate a broad set of potential plans and build consensus towards a best plan.