Cycling as an inexpensive, healthy, and efficient mode of transport for everyday traveling is becoming increasingly popular. While many cities are promoting cycling, it is rarely included in transport models and systematic policy evaluation procedures. The purpose of this study is to extend the agent-based transport simulation framework MATSim to be able to model bicycle traffic more realistically. The network generation procedure is enriched to include attributes that are relevant for cyclists (e.g. road surfaces, slopes). Travel speed computations, plan scoring, and routing are enhanced to take into account these infrastructure attributes. The scoring, i.e. the evaluation of simulated daily travel plans, is furthermore enhanced to account for traffic events that emerge in the simulation (e.g. passings by cars), which have an additional impact on cyclists’ decisions. Inspired by an evolutionary computing perspective, a randomizing router was implemented to enable cyclists to find realistic routes. It is discussed in detail why this approach is both feasible in practical terms and also conceptually consistent with MATSim’s co-evolutionary simulation approach. It is shown that meaningful simulation results are obtained for an illustrative scenario, which indicates that the developed methods will make real-world scenarios more realistic in terms of the representation of bicycle traffic. Based on the exclusive reliance on open data, the approach is spatially transferable
