Essential Bicycle Dynamics for Microscopic Traffic Simulation: An Example Using the Social Force Model

Microscopic simulation is an established tool in traffic engineering and research in which aggregated traffic performance measures are inferred from the simulation of individual agents. 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 without considering the constraints of two-wheeler dynamics (mechanics of a bicycle in motion) that limit feasible maneuvers in a cycling conflict. 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 more realistic 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 the yaw angle step response and four test cases with up to three cyclists to show that the generated trajectories notably differ from results obtained from a 2D bicycle model without roll 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 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.