Traffic State Estimation for Signalized Intersections : A Combined Gaussian Process Bayesian Filter Approach
Abstract: Traffic State Estimation (TSE) is a vital component in traffic control which requires an accurate viewof the current traffic situation. Since there is no full sensor coverage and the collected measurementsare inflicted with random noise, statistical estimation techniques are necessary to accomplish this.Common methods, which have been used in highway applications for several decades, are state-spacemodels in the form of Kalman Filters and Particle Filters. These methods are forms of BayesianFilters, and rely on transition models to describe the system dynamics, and observation models torelate collected measurements to the current state. Reliable estimation of traffic in urban environmentshas been considered more difficult than in highways owing to the increased complexity.This MsC thesis build upon previous research studying the use of non-parametric Gaussian Processtransition and measurement models in an extended Kalman Filter to achieve short-term TSE. To dothis, models requiring different feature sets are developed and analysed, as well as a hybrid approchcombining non-parametric and parametric models through an analytical mean function based on vehicleconservation law. The data used to train and test the models was collected in a simulated signalizedintersection constructed in SUMO.The presented results show that the proposed method has potential to performing short-term TSE inthis context. A strength in the proposed framework comes from the probabilistic nature of the GaussianProcesses, as it removes the need to manually calibrate the filter parameters of the Kalman Filter. Themean absolute error (MAE) lies between one and five vehicles for estimation of a one hour long dataseries with varying traffic demand. More importantly, the method has desirable characteristics andcaptures short-term fluctuations as well as larger scale demand changes better than a previously proposedmodel using the same underlying framework. In the cases with poorer performance, the methodprovided estimates unrelated to the system dynamics as well as large error bounds. While the causefor this was not determined, several hypotheses are presented and analysed. These results are takento imply that the combination of BF and GP models has potential for short-term TSE in a signalizedintersection, but that more work is necessary to provide reliable algorithms with known bounds. In particular,the relative ease of augmenting an available analytical model, built on conventional knowledgein traffic modelling, with a non-parametric GP is highlighted.
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