Abstract
A better understanding of cyclist behavior during various interactions is needed to enhance bicycle microsimulation models. This study aims to characterize cyclist maneuvers in following and overtaking interactions using multivariate finite mixture model-based clustering. Several variables that potentially affect cyclist state and future decisions are extracted from video data using computer vision techniques, including the longitudinal distance, lateral distance and speed difference between interacting cyclists. Observations of cyclists in following interactions are clustered into constrained and unconstrained states. Observations of overtaking cyclists are clustered into initiation, merging and post-overtaking states. Multivariate distributions within each cluster are examined, along with state transitions for each type of interaction. These characterizations are a key step toward development of agent-based bicycle traffic microsimulation models, which can be used to enhance bicycle facility planning and design, safety modeling, and energy modeling.
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