Modeling LaneChanging Behavior in Freeway OffRamp Areas Using Naturalistic Driving Data

Abstract:

The main focus of this paper is to study lanechanging decisionmaking characteristics in freeway offramp areas and analyze the mechanism of lanechanging decisionmaking. Using driving behavior samples and vehicle operating data collected from Shanghai Naturalistic Driving Study, the departure samples are selected according to the driving path and specific range of exit with Google Earth. Then lanechanging behaviors can be identified by lane offset position and steering wheel angle. Furthermore, illustrated by the example of the 8lane freeway (four in each direction), this paper comprehensively considers driving route information and traffic flow environment as factors. On this basis, relying on the random utility theory, the binary logic (BL) model is adopted for fitting lanechanging decisionmaking model so that the lane utility function is obtained. After graphing utility of different lanes in offramp areas respectively in free flow, steady flow and congested flow, the homogeneity and heterogeneity analysis are conducted. The result indicates that the accuracy of the model comes to 86.21% and the influence of each factor can be reasonably explained. Besides, according to utility analysis, the lanechanging behavior of leaving vehicles is a consequence of the balance between leaving willingness and requirement for improving driving environment. In other words, it combines the characteristics of mandatory and discretionary incentives. Moreover, in the process of traffic transforming from free flow to heavy flow, the influence of the latter increases: the position at which drivers start to perform right lanechanging gradually approaches the diverging area and left lanechanging behavior is more active in upstream section.