| Resumo: | The increase in tourism activity globally has led to overcrowding, causing damage to local ecosystems and degradation of the tourism experience. To plan tourist activity it is necessary to define adequate indicators and understand the dynamics of tourist crowds. The main goals of this dissertation are the development of (1) an algorithm for assessing spatially fine-grained, physical carrying capacity (PCC) for a complex urban fabric, (2) an agent-based simulation model for the egress of participants in public open space tourism attraction events and (3) an agent-based simulation model using the PCC algorithm for tourism crowding stress analysis in urban fabric constrained scenarios. OpenStreetMap open-data was used throughout this research. The proposed PCC algorithm was tested in Santa Maria Maior parish in Lisbon that has a complex ancient urban fabric. The GAMA agent-based platform was used in the two simulation studies. The first compared two scenarios (normal and COVID-19) in three major public spaces in Lisbon and the second focused on the simulation of a real-time tourism crowding stress analysis scenario of visitors’ arrival at the Lisbon Cruise Terminal. The results show the proposed algorithm’s feasibility to determine the PCC of complex urban fabrics zones and its application as an initial reference value for the evaluation of real-time crowding stress, namely in simulations for assessing overtourism scenarios, both in public open spaces as in highly constrained urban fabrics.
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