Pattern Recognition of Movement Behavior for Intersection Classification using GPS Trace Data

The aim of this thesis is to classify different regulator types of traffic road intersections based on GPS trace data. To reach this aim a variety of features is calculated to describe the driving behavior at intersections. These are derived from the measured units of the GPS trace data that compose an individual’s movement trajectory. Among other things, the influence of turning trajectories as well as the number of trajectories used for feature calculation on the overall accuracy were investigated. Additionaly, various over-sampling methods were tested for overcoming the imbalance of the dataset. A Random Forest classifier trained to identify road regulation at junctions scored over 84% (accuracyscore) at the various experimental settings been explored within the scope of the thesis work.