Statistical calibration of agent-based models of transport from aggregate data

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This research focuses on statistical calibration of agent-based models (ABMs) of transport based on aggregate data. ABMs aim to understand the effect of interacting individual choices (e.g. route travel choices) on emergent behaviour (e.g. network traffic patterns) via a set of probabilistic rules. ABMs' recent rise in popularity in epidemic modelling raised important questions about their identifiability and predictive power especially in policy scenario testing (e.g. introduction of pedestrianised streets). The research aims develop computational statistics and machine learning techniques to address those questions by understanding discrete individual agent choices from coarsely observed data. This poses great computational challenges as it necessitates the exploration of a combinatorial space of agent decisions. The problem is tackled in two contexts: population synthesis and ABM simulation. The former is a pre-processing step that creates artificial agents whose socio-economic attributes have the same statistical properties as those described by population averages while the latter uses coarse data to reconstruct agent trajectories in space-time generated by discrete agent choices and/or continuous decision parameters.