Analysis of two rule-based lane change models utilising naturalistic vehicle trajectory data

Lane change modelling is a key challenge in driver behaviour research, as lane-changing involves complex interactions with surrounding vehicles and significantly impacts traffic flow. Despite advancements in lane change modelling, calibration is often limited to macroscopic indicators, limiting microscopic performance assessment. Yet, micro-level accuracy is crucial, in particular for simulating mixed traffic and setting up realistic testing environments for automated driving functions. Hence, this study evaluates the micro-level performance of existing lane change models (LMRS and MOBIL) by utilising naturalistic trajectory data. Additionally, comprehensive data processing aspects are addressed, and trajectory-based model parameter optimisation is conducted, enabling manoeuvre-level performance evaluation. The results reveal that MOBIL struggles to capture anticipatory human driver behaviour. This issue arises because MOBIL perceives close but faster vehicles in the target lane as obstacles, whereas in reality, drivers often do not treat them as such. LMRS, on the other hand, shows difficulties with gap acceptance.