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

Abstract

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 cyclists riding alone (n = 1,411) and cyclists riding in 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 (path marked for cyclists or path marked for pedestrians) 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 riding and road usage. It is to be investigated whether cyclists alone behave differently than cyclists in groups and whether there are differences in the two groups for wrong way cyclists. Both - cyclists alone (69.38%) and cyclists in groups (84.57%) - crossed the intersection more frequently without stopping within the observation period. In all cases, cyclists stopped more often at the bicycle stopping line or used the special marked bicycle lane, thereby complying with the law. Most wrong way cyclists on the special marked bicycle lane were found for cyclists alone with stopping (10%, n = 27) and cyclists in groups with stopping (8%, n = 12). The speeds were also compared. The speeds between cyclists alone and cyclists in groups differ slightly, and the stopping behaviour is very similar if the special marked bicycle lane is used after the stop. 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. Furthermore, the results of this study will provide further knowledge, which help developing autonomous driving functions correctly anticipating cycle behaviour at intersections.