Revised Analysis of stopping behaviour of cyclists at a traffic light-controlled intersection using trajectory data

Cyclists have various route options to get to their destination. They can share lanes with vehicles, share lanes with pedestrians, or have their own lane. In Germany there are often marked lanes across intersections and stop lines in front of the crossing, guiding the cyclists their way. However, these markings are not always respected in the way they should be. This study is intended to examine the stopping behaviour of cyclists at a traffic light-controlled intersection. A distinction was made between single cyclists (n = 1,411) and cyclist groups (more than one cyclist; n = 475). The stopping area was divided into polygons to understand where most people stop before an intersection. Furthermore, it was examined where people continued to ride after stopping (bicycle or pedestrian crossing) and this was compared with cyclists who did not stop. The aim of this study is to investigate cyclists’ stopping behaviour (e.g. stopping position) at intersections with consideration of the impact of groups, wrong-way cycling and road usage. It is to be investigated whether single cyclists behave differently than cyclist groups and whether there are differences in the two groups for wrong way cyclists. Both - single cyclists (69.38%) and cyclist groups (84.57%) - crossed the intersection more frequently without stopping within the observation period. In all cases, cyclists stopped mostly at the bicycle stopping line or used the bicycle crossing, thereby complying with the law. Most wrong way cyclists on the bicycle crossing were found for single cyclists with stopping (10%, n = 27) and cyclist groups with stopping (8%, n = 12). The speeds between single cyclists and cyclist groups differ slightly, and the stopping behaviour is very similar if the bicycle crossing is used after the stop. The study shows that cyclists also stop far away from the intersection at unexpected positions that may not be in the field of vision of autonomous vehicles. Nevertheless, the cyclists still cross the intersection, partly also on the footpath, which is not rule compliant and unexpected. The information can be used to improve models of cyclists’ behaviour, for example, in microscopic simulations, in which cyclists only stop at clearly defined locations. Knowledge about the stopping position and the further crossing of the intersection can help to make simulations more realistic, e.g. when planning or changing new infrastructure. Furthermore, the results of this study will provide further knowledge, which helps developing autonomous driving functions to correctly anticipate cycling behaviour at intersections.