Identification of on-road and separate bicycle lanes

Cyclists are the most vulnerable participants in the road traffic in these days. Therefore, it is important to protect them, for example, by physically separated bicycle lanes. Continuous position tracking can yield a lot of data, which can help to understand how moving objects behave. This information could be used, e.g. in routing applications aiming to find a more secure route to the users destination. In this thesis the goal is to explore the idea of the identification of bicycle lane types of (OpenStreetMap) road segments using GPS trajectories of bicycles and motorized vehicles. This goal is archieved by applying clustering to the pool of trajectory data. Different distance measures are explored to distinguish between car and bicycle clusters. Three different bicycle lane types are identified within this thesis in the region of Hannover. Each of them can be classified with at least 70 percent accuracy. There are multiple reasons for inaccuracies like GPS positioning errors and the fact that not all cyclists drive on the respective bicycle lanes but on the car lanes.