This study focuses on bicycle travel flow to prevent traffic accidents at non-signalized intersections. A bicycle's behavior can be characterized by various parameters, such as travel speed position, trajectory, acceleralion, and deceleration. The prevention of vehicle collisions with bicycles traveling at 10-15 km/h was regulated in the Advanced Emergency Braking System (AEBS) for passenger cars in regulation No. 152 of the World Forum for Harmonization of Vehicle Regulations in the United Nations. Therefore, it is essential to analyze the characteristics of bicycles in a reall trafflc environment to prevent traffic accidents involving cyclists. Meijer et. al. (2017) investigated bicycle behavior and charactericics using measurement devices installed on biccycles [1 ]. Ma et al. (2016) conducted a model of acceleration behavior on eleven cyclists using GPS data [2]. And it was pointed out that there was a need for modeling research for more cyclists.Hirose et al. (2021) examined bicycles' both travel speed and trajectory as bicycle travel flows based on data obtained from fixed-point observations at a non-signalized intersection in Tokyo, Japan [3]. This used fixed-point observalions to obtain raw data of bicycle travel flows in real traffic environment and reported various traffel speed, trajectory, and acceleration/deceleration patterns for bicycles entering intersections. The purpose of this study was to construct a model of bicycle travel flows based on fixed-point observations. It could simulate actual bicycle behaviors based on data that was obtained from measuring bicycle travel flows for 2828 cases from fixed-point observations. Furthermore, the data was divided into five patterns of bicycles entering intersections, and the accuracy of the model was evaluated for each pattern.

Most of the existing bicycle flow traffic research is limited to characterizing the longitudinal motion of bicyclists based on the assumption that there is no significant differences between the dynamics of a single-file bicycle traffic and the longitudinal motion behavior of cars. This research reparametrizes an existing car-following model to describe bicycle-following and motion behavior. Furthermore, the lack of naturalistic data has limited the validation of this model. This research aims at developing a descriptive model that is capable of capturing the inherent non-lane-based traffic behavior characteristics of bicycle traffic and provides a methodology for extracting naturalistic cycling data from video feeds for use in safety and mobility applications. In this study, The Fadhloun-Rakha (FR) bicycle-following longitudinal motion model was extended 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, and 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. In relation to the development of the naturalistic cycling dataset, 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 applied computer vision and machine learning techniques to develop a comprehensive framework for the extraction of naturalistic cycling trajectories. In total, this study resulted in the collection and classification of 619 bicycle trajectories based on their type of interactions with other road users. The results confirm the success of the proposed methodology in relation to extracting the locations, speeds, and accelerations of the bicycles with a high precision level. Furthermore, preliminary insights into the acceleration and speed behavior of bicyclists around motorists are determined. / Master of Science / The behavior of bicycle traffic differs from the that of cars. Bicycle traffic flow dynamics is unconstrained in lateral motion and overtaking when compared to car traffic flow. Based on this inherent behavior, existing car-following can only model the longitudinal motion of the bicycle flow traffic and it does not describe the non-lane base traffic that characterizes bicycle traffic dynamics. Furthermore, the existing experimental controlled dataset used for validating bicycle traffic flow models does not capture the naturalistic behavior of cyclists. Therefore, this research aims to develop a descriptive model that is capable of capturing the inherent non-lane-based traffic behavior characteristics of bicycle traffic and provides a methodology for extracting a naturalistic cycling data from a video dataset for use in safety and mobility applications. In this study, the Fadhloun-Rakha (FR) bicycle-following longitudinal motion model was extended 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, and 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. In relation to the development of the naturalistic cycling dataset, 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 applied computer vision and machine learning techniques to develop a comprehensive framework for the extraction of naturalistic cycling trajectories. In total, this study resulted in the collection and classification of 619 bicycle trajectories based on their type of interactions with other road users. The results confirm the success of the proposed methodology in relation to extracting the locations, speeds, and accelerations of the bicycles with a high precision level. Furthermore, preliminary insights into the acceleration and speed behavior of bicyclists around motorists are determined.