Introducing the Effects of Road Geometry Into Microscopic Traffic Models for Automated Vehicles

Road geometry (e.g. slope and curvature) has significant impacts on driving behaviours of low-level automated vehicles (AVs), but it been largely ignored in microscopic traffic models. To capture these effects, this study proposes a generic approach to extend any (free-flow or car-following) models characterized by acceleration functions. end, three model extensions are developed, each which can use different submodels for comparison. Their effectiveness is demonstrated with free-flow that represents the AVs’ control logic. Finally, all possible combinations calibrated cross-validated against data sets, contain empirical trajectories commercial adaptive cruise (ACC) systems test tracks. Results suggest two submodels, i.e., nonlinear vehicle dynamics (NVD) radius difference method (RDM), effectively effects road curvature, respectively, driving. Specifically, NVD dominant factor contributing increasing (by 34.9% average) accuracy. In addition, when simulating reckless turning (i.e. turns at high speeds), inclusion developed RDM performance, extended both achieve largest accuracy gains (39.6%).