This research effort starts by developing a descriptive model that is capable of capturing the inherent non-lane-based traffic behavior characteristics of bicycles. In that regard, the research team extends the Fadhloun-Rakha bicycle-following longitudinal motion model through 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 for the activation 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 along with its direction and magnitude. The resulting model is the first point-mass dynamics-based model for the description of the longitudinal and lateral behavior of bicycles in both constrained and unconstrained conditions. In fact, by having the FR bicycle-following model as the governing module of longitudinal behavior and a dynamic lateral module, the proposed model is found to be both robust and able to model bicyclist behavior variability. Furthermore, it is the only existing model that is sensitive to the bicyclist physical characteristics and the bicycle and roadway surface conditions given that the used longitudinal logic was previously validated against experimental cycling data. Next, this study describes a new framework for the collection of naturalistic cycling data. In that process, a new naturalistic cycling dataset is collected for the purpose of validating the developed bicycle lateral motion model. 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 used videos come from a dataset collected in a previous Virginia Tech Transportation Institute study in collaboration with SPIN in which continuous video data at a non-signalized intersection on the Virginia Tech campus was recorded. 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 in relation to extracting the locations, speeds, and accelerations of the bicycles. Besides providing preliminary insights into the naturalistic acceleration and speed behavior of bicyclists around motorists, the collected dataset is used to further confirm the validity and robustness of the proposed model for bicycle lateral motion behavior modeling. That is achieved by verifying the model’s ability to generate simulated trajectories that are consistent with the naturalistically observed lateral behavior.