Microscopic crowd simulation usually uses ad-hoc models. While these have been proven to be useful, they are difficult to calibrate and do not always reflect real behaviour. For this reason we propose a machine learning approach using neural networks. The main contribution of the project is a first exploration of prediction of agent trajectories using two specific types of neural networks, Support Vector Machine (SVM) and Spiking Neural Networks (SNN).
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© 2016 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
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