Real-Time Simulation of Urban Systems Using Deep Learning

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My research aims to address some of the complex challenges inherent in modelling urban systems, which exhibit non-deterministic and non-linear behaviour's. It highlights the limitations of existing solutions, particularly agent-based models (ABMs), due to the absence of robust validation methods, and inability to handle real-time data. My project will take employ a hybrid deep-learning model to create an 'emulator' of an ABM, bridging the gap between theoretical models and real data. By leveraging existing centralised repositories of real-time urban data, the research aims to predict the spatio-temporal dynamics of urban agents. This approach has the potential to move us closer to the creation of an 'urban digital twin,' offering insights into the behaviour of complex urban systems and non-independent sensors' spatio-temporal dependencies, allowing for more effective response to developing events, like overcrowding or flooding.