Modelling and calibrating situation adaptive lane changing and merging behavior on Chinese elevated roads

Abstract

This paper describes the extension and the calibration of a driver model in SUMO that enables a situation adaptive driver behavior for lane changing and merging processes. The extensions are tailored to improve the representation of Chinese traffic dynamics but are expected to yield improvements for other regions of the world as well. To design and calibrate the model extensions FOT (field operational test) Data is used to analyze which influence critical traffic situations have on driving behavior. The results are discussed and findings for driver models as well as for piloted driving systems are derived. To this end a detailed evaluation of merging and lane change behavior with respect to different traffic situations is conducted. Subsequently the model extensions of emotion-memory and lateral encroachment are explained and its implementation and calibration discussed. Finally the resulting Driver model is validated by using real world data from Chinese elevated road interchanges and merging areas.