Abstract
Previous research has examined vehicle-pedestrian and vehicle-cyclist interactions, but there have been few studies that examined cyclist-pedestrian interactions at intersections. This study addresses this gap by analyzing cyclist-pedestrian interactions at an unsignalized intersection in Germany using publicly available drone data. The study presents a framework and proof of concept for analyzing cyclist behavior proactively, without relying on crash data. The primary objectives are to identify the variables influencing cyclist yielding behavior and obstructed travel time (OTT) within a predefined zone at a zebra crossing and to classify cyclist behaviors. Using logistic and linear regression models, several key predictors were identified, including cyclist speed, trajectory changes, pedestrian time-to-conflict-point, and interaction proximity, which significantly impacted yielding behavior. Speed reduction and pedestrian presence on the zebra crossing were found to improve yielding rates. Additionally, clustering analysis revealed two optimal and distinct cyclist behavior groups: one cluster exhibiting less yielding behavior, while the other demonstrated greater compliance with traffic laws. This proactive approach provides a valuable alternative in environments where crash data acquisition is complicated by privacy regulations. It offers critical insights for traffic management strategies aimed at enhancing pedestrian safety at unsignalized intersections, making it applicable to broader contexts with similar data challenges.