Cycling through intersections: Patterns affecting safety

Abstract

Within the past years, a shift in mode of transport has been observed. The popularity of cycling has significantly increased and will increase even more in the time to come. On the downside, cycling is also associated with severe injuries or even fatalities, particularly when involved in a crash with a motorized vehicle. In urban areas, one of the most risky situations is crossing an intersection while a motorized vehicle turns right. The largest part of crashes involving cyclists are caused by wrong behavior when turning / crossing of which more than 40% result in major injuries or even death. In an effort to increase road traffic safety of cyclists in intersections, within the framework of EU-funded project XCYCLE, an infrastructural supported cooperative advanced driver assistance system (C-ADAS) will be developed. The objective is to develop an online risk assessment in order to predict critical encounters between motorized vehicles and cyclists in real-time. Understanding how critical situations/conflicts arise is the key to this situation and risk assessment. Therefore, it is inevitable to quantify and analyze human behavior, determine situational factors, but also analyze objective data, such as video-based recorded trajectory data in order to identify patterns between and within interacting partners (i.e. motorist and cyclist). At the AIM Research Intersection in Braunschweig, trajectory and video data has been collected since summer 2016. Based on the data collected between August 22nd and September 18th, 2017, in an observational study, situational factors contributing to critical situations were analyzed. According to the results, the relative position of cyclist and motorist to each other in the approach to the intersections indicates to contribute to critical situations. This knowledge allows us deciding whether an upcoming situation may end up as an encounter or critical situation already at an early stage possibly enabling a rapid-warning mechanism for motorists. In a next step, trajectory data will be analyzed in order to determine distinct patterns in, for example speed, between critical situations, encounters, cyclists crossing the intersection without interaction partner, and motorists turning at the intersection without interaction partner. Performance indicators used to assess the interaction or cooperation between motorists and cyclists are also defined and will be analyzed. Results of the analyses will be incorporated into the development of an online situation and risk assessment.