###### Abstract
This study showed that traffic shockwaves for bicycle traffic could be realistically reproduced through the IDM. Furthermore, they concluded that, for bicycle traffic, the prediction power of the IDM is better than that of the NDM. Using the Inverse Reinforcement Learning concept [17], the interactions between cyclists and pedestrians were modelled, e.g., following and overtaking behaviors, when they share the space. The comparison of the simulated and actual trajectories explained that the model predicts the cyclists' speed more accurately than the position. [PERSON] et al. [18] enhanced the social force model to simulate bicycle flow in groups. Numerical simulations were conducted in this study considering shoulder groups and following groups. The results explained that the negative impacts of shoulder groups are higher as compared to following groups and, therefore, shoulder grouping should be prohibited. This study is purely based on numerical simulation and validation is needed using experimental or real-world observational data. [PERSON] et al. [19] also modified the social force model to simulate the interactions between bicycles and cars at mixed-traffic intersections under high density bicycle traffic conditions. In the modified model, they considered a dynamic boundary model to incorporate the lateral dispersion of cyclists and a behavior force model to model interactions. The behavior force model consisted of four behaviors, namely, free moving, following, overtaking, and merging. Bicycle trajectories obtained through observations at a signalized intersection have been used to calibrate this model and the simulation results indicated that the modified social force model performs better than the original social force model. [PERSON] et al. [20] proposed a heuristic-based model to simulate the interactions between pedestrians and cyclists in shared spaces. The model was calibrated using the experimental data collected under pedestrian-bicycle mixed traffic conditions. This model reproduced lane formation, which is a commonly observed self-organization phenomenon in pedestrian dynamics. [PERSON] et al. [21] calibrated a model to simulate bicycle traffic on wide roadsusing the data collected from bicycle experiments conducted on 3-m-wide tracks. Free riding, following, and overtaking behaviors have been explored in this study. In the model, they considered the movement direction, which was based on the view angle range. Furthermore, three relaxation times to represent acceleration, deceleration, and turning behaviors were used in their model. The findings of this study explained that the model could realistically reproduce bicycle flow dynamics. These previous studies explain how existing microscopic models, such as the social force model and car-following models, can be re-calibrated to simulate bicycle traffic. While gleaning insights from these previous studies, the current study will comprehensively investigate the microscopic behavioral characteristics of cyclists when they interact with other cyclists. That is, in addition to the behaviors identified in established models, interaction characteristics and behavioral rules will be derived from the empirical data. The key objective of this study is to explore the microscopic characteristics of the interactions between cyclists when they are following each other in a single file movement. Such situations could often be observed on narrow bicycle lanes. In particular, the characteristics of the interactions between cyclists, e.g., spacing, speeds, and acceleration, will be quantitatively assessed in detail. A key hypothesis of the proposed study is that the acceleration (or deceleration) of the follower is determined by the spacing and relative speed between the following and the leading cyclists. The findings of this study will be useful in developing microscopic behavior models that can be used in designing and assessing bicycle infrastructure and in evaluating the level of service and safety of bike lanes.