Impact of V2V Warning Information on Traffic Stream Performance Using Microscopic Simulation Based on Real-World Connected Vehicle Driving Behavior

Abstract

Vehicle-to-vehicle (V2V)-based forward collision warning is a core connected vehicle (CV) service for preventing traffic crashes. Since the driver’s reaction affects the surrounding traffic conditions, an analysis of the effects of the CV application is required in terms of traffic stream performance. This study compared the differences in driving behaviors of CVs depending on whether V2V-based forward collision warning information was provided. The driving characteristics of CVs based on the analysis of probe vehicle data (PVD) were defined, and a methodology was proposed to simulate the driving behaviors of CVs in VISSIM environments. Additionally, the effectiveness of improving mobility and traffic safety by various market penetration rates (MPRs) of CVs in an accident situation was identified by evaluating the average travel speed and time-to-collision (TTC)-based crash risk. The simulation results for the two-lane blocking accident scenario indicated that the average speed increased by 23.18% with an MPR of 100%, and the TTC-based crash risk decreased by 18.34% compared with an MPR of 0%. It has been demonstrated that V2V-based warning information is useful for not only safety benefits but also mobility improvement. The results of this study could be employed as fundamentals to establish various policies to expedite the implementation of CV systems in practice. In addition, it is suggested that the outcome of this study will be useful for developing traffic flow management strategies technology to improve traffic safety. IEEE