Even though the agent-based simulation modelling has become a standard tool in transport research, current implementations still treat travellers as passive data structures, updated synchronously at infrequent, predefined points in time, thus failing to cover within-the-day decision making and negotiation necessary for cooperative behaviour in a dynamic transport system. Leveraging the fully agent-based modelling approach, we have built large-scale activity-based models of multimodal mobility covering areas up to thousands of square kilometres and simulating populations of up to millions of inhabitants of several European cities. Citizens are represented by autonomous, self-interested agents which schedule and execute their activities (work, shopping, leisure, etc.) and trips in time and space. Individual decisions are influenced by agent's demographic attributes and modelled using the data from mobility surveys. The model is statistically validated against origin-destination matrices and travel diary data sets.
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