United States Department of Transportation
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Edition:Final Report January 2022 - December 2022
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Abstract:This research first develops a descriptive model that is capable of capturing the inherent non-lane-based traffic behavior characteristics of bicycles. To that end, the research team expands upon the existing Fadhloun-Rakha bicycle-following longitudinal motion model by complementing it with a lateral motion strategy, thus allowing for overtaking maneuvers and lateral bicycle movements. For the most part, the following strategy of the FR model remains valid for modeling the longitudinal motion of bicycles except under the conditions of the collision avoidance strategy, which are modified in order to allow for overtaking when possible. The proposed methodology is innovative in that it makes use of the intersection of certain pre-defined regions around the bicycles to decide on the feasibility of angular motion as well as its direction and magnitude. The resulting model is the first point-mass dynamics-based model to describe the longitudinal and lateral behavior of bicycles in both constrained and unconstrained conditions. In fact, by having the FR bicycle-following model as both the governing module of longitudinal behavior and a dynamic lateral module, the proposed model is able to model bicyclist behavior variability. Furthermore, given that the longitudinal logic used in the model was previously validated against experimental cycling data, it is the only existing model that is sensitive to the condition of the bicycle, the roadway surface, and the bicyclists’ physical characteristics. Next, the research team expanded this study by collecting a new naturalistic cycling dataset. Given that the collection of naturalistic cycling data is not achievable in the traditional vehicle approach, machine learning and computer vision techniques were used to construct the naturalistic dataset from existing video feeds. The videos used in the study come from a dataset collected in a previous Virginia Tech Transportation Institute study conducted in collaboration with SPIN in which continuous video data was recorded at a non-signalized intersection on the Virginia Tech campus. The research team applied existing computer vision and machine learning techniques to develop a comprehensive framework for the extraction of naturalistic cycling trajectories. In total, the proposed methodology resulted in the collection of 619 bicycle trajectories at a high level of precision with respect to the location, speed, and accelerations of the bicycles.
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