A Hybrid Modelling Approach for Traffic State Estimation at Signalized Intersections

Traffic state estimation is an important part of the traffic control process and aims to creates an accurate understanding of the current situation in traffic system. Bayesian Filtering is a statistical modelling framework that is useful in representing traffic state update as well as the relation between traffic state and detection data. This study develops a hybrid approach and uses non-parametric Gaussian Process (GP) to model the state-space transition of traffic system. Through representing the system models as either fully data-driven GP or as a hybrid model using a parametric mean function fusing the conventional principle of traffic flow with the data-driven approach, the requirement of an analytical model can be removed or relaxed. The computational results show that the proposed approach for lane based TSE can capture both short-term fluctuations and larger demand changes. In particular, the Bayesian nature of the GP models offer relative ease in quantifying the model uncertainties in combination with a conventional traffic flow model.