Vulnerable road user safety is paramount for increasing shares of active travel modes and introducing automated vehicles. Microscopic traffic simulation is a prevalent method in research and practice with a growing focus on safety and cyclists. Its practical benefits make it an essential tool for developing safe future transportation. We review the methodology of simulation studies and the validation of their microscopic models to evaluate cycling safety assessment in microscopic simulations. We find that current work relies predominantly on the lane-based models of established traffic flow simulation packages that separate longitudinal and lateral dynamics. These models do not sufficiently capture diverse behaviors and conflict causality to predict cycling safety. In contrast, new models with successful calibrations and validations advance simulated interactions towards capturing conflict causality. Of 42 reviewed studies, six calibrate, and three validate models for safety prediction. Other studies disregard calibration and validation, posing a threat of unfounded safety predictions and unsafe design recommendations. We present a methodological framework conceptualizing best practices for reliable assessment. It calls for the identification of safety-relevant behaviors of cyclists and other road users in conflicts. Specialized behavioral models must be developed, calibrated, and validated. The selected safety indicators must enable capturing the expected unsafe events. To create these tools, improved models of cycling behavior must be transferred to established simulation packages. Following the framework, researchers and practitioners can use simulation as a practical and ethical means to assess the cycling safety impact of innovations ranging from infrastructure to automation and connectivity.

Microscopic simulation is an established tool in traffic engineering and research, where aggregated traffic performance measures are inferred from the simulation of individual agents. Additionally, measures describing the safety and efficiency of road user interactions gain importance for recent developments such as automated vehicles and urban cycling. However, current simulation frameworks model interactions including cyclists only with limited realism. To address this issue, we propose to bring bicycle dynamics to traffic simulation. We demonstrate that a novel reformulation of the social force framework can create input signals for a controlled inverted pendulum bicycle model and thereby enable a fully two-dimensional open space simulation of cyclist interactions. The inverted pendulum model introduces the need to stabilize the bicycle as a constraint to the reactive behavior of simulated cyclists. Furthermore, it enables the simulation of countersteering and weaving for stabilization. Our cyclist social forces have anisotropic force fields with respect to relative interaction position and orientation to describe the varying interaction constellations in open space. With these models, we simulate five single- and multi-cyclist test cases and show that the generated trajectories notably differ from results obtained from a 2D bicycle model without lean angle simulation. Measurements of the maximum lateral path deviation and post-encroachment time show that these differences are relevant for typical applications. Our work demonstrates the potential of introducing physics-based realistic bicycle dynamics to the microscopic simulation of individual road user interactions and the fundamental capability of our reformulated cyclist social forces to do so. Going further, we plan to calibrate and validate our model based on naturalistic cycling data to support the initial results of this work.