Institute of Information Theory and Automation

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System identification based on Gaussian-process models for traffic control applications

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Many engineering systems can be characterised as complex since they have a nonlinear behaviour incorporating a stochastic uncertainty. Urban traffic systems or traffic pollution propagation models are typical representatives of such complex systems. One of the most appropriate methods for modelling such systems is based on the application of Gaussian processes. Gaussian process models provide a Bayesian probabilistic non-parametric modelling approach for black-box identification of nonlinear stochastic systems. They can highlight areas of the input space where prediction quality is poor, due to the lack of data or to its complexity, by indicating the higher variance around the predicted mean. The aim of this project is to show the benefits of predicting the modelled uncertain variables in urban traffic or pollution models using a probabilistic nonlinear Gaussian process models and solving some issues that are related to the application of this method.
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2013-01-10 12:18