An Extended Car-Following Model Considering Generalized Preceding Vehicles in V2X Environment

Abstract

Vehicle-to-everything (V2X) technology will significantly enhance the information perception ability of drivers and assist them in optimizing car-following behavior. Utilizing V2X technology, drivers could obtain motion state information of the front vehicle, non-neighboring front vehicle, and front vehicles in the adjacent lanes (these vehicles are collectively referred to as generalized preceding vehicles in this research). However, understanding of the impact exerted by the above information on car-following behavior and traffic flow is limited. In this paper, a car-following model considering the average velocity of generalized preceding vehicles (GPV) is proposed to explore the impact and then calibrated with the next generation simulation (NGSIM) data utilizing the genetic algorithm. The neutral stability condition of the model is derived via linear stability analysis. Numerical simulation on the starting, braking and disturbance propagation process is implemented to further study features of the established model and traffic flow stability. Research results suggest that the fitting accuracy of the GPV model is 40.497% higher than the full velocity difference (FVD) model. Good agreement between the theoretical analysis and the numerical simulation reveals that motion state information of GPV can stabilize traffic flow of following vehicles and thus alleviate traffic congestion.

Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment

“…With the aforementioned connected technologies enabled by fifth generation mobile networks, drivers can obtain motion state information of multiple preceding vehicles including the left/right preceding vehicle. According to this, we proposed a generalized preceding vehicles (GPV) model, which can describe drivers' car-following behavior with consideration of GPV information in a relatively accurate way, in the previous research [33]. The research results confirmed the stabilizing effect of GPV information on traffic flow.…”

“…Although there are multiple extended models mentioned in Section 1 with consideration of the impacts exerted by the motion state information of an arbitrary number of preceding vehicles in the current lane, there was no research considering the influence caused by preceding vehicles in the adjacent lanes, of which information is available for the driver in the V2X environment. Based on these, we considered the motion state information of GPV, which consists of the preceding vehicle, the non-neighboring preceding vehicle and the left/right preceding vehicle in the adjacent lanes, and proposed an extended model named the GPV model [33]. The equation of this model is as follows:…”

“…According to the data measured in the field and the previous research [33,66], the length of the road and the vehicle are respectively set as L = 1500 m and h c = 5 m. The velocity limits are set as v max = 30 m/s and v min = 8 m/s. The acceleration limits are set as a max = 3 m/s 2 and a min = −4 m/s 2 .…”

“…The FVD model is employed to represent the unconnected environment for comparison. The propagation of disturbance has been discussed in our previous work [33], and the attention is attached to the impacts of the disturbance on traffic flow in this research. The simulation results are as shown in Figures 9 and 10.…”

An Improved Cellular Automaton Traffic Model Based on STCA Model Considering Variable Direction Lanes in I-VICS

“…At the same time, with the road as a vehicle carrier, the I-VICS uses advanced wireless communication technology and sensing detection technology. The purpose of I-VICS is to obtain data from vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) sensing devices, and other related information to realize the fusion of heterogeneous data from multisource, as well as to share the information to achieve reasonable speed and lane-changing behavior suggestions during vehicle operation so that viatic traffic can develop regarding intelligence, networking, and automation [4][5][6]. Therefore, in the I-VICS environment, more effective information can be transmitted to the driver to improve the correctness and systematization of driving decisions, especially in dynamic traffic control.…”