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

Automated summary

This study proposes a generic approach to extend microscopic traffic models to capture the impacts of road geometry on the driving behaviors of low-level automated vehicles. The developed nonlinear vehicle dynamics and radius difference method can effectively increase model accuracy. When simulating reckless turning behaviors, utilizing both of these extensions results in the largest accuracy gains.