# An Extended Car-Following Model Considering Generalized Preceding Vehicles in V2X Environment
## 6 Discussion  
The information perception ability of the driver is significantly enhanced in the V2X environment. Utilizing V2X technology, drivers can obtain massive traffic information, and based on the information, adjust and optimize their car-following behavior. The impact of information about motion state of vehicle individuals such as headway, velocity and acceleration of preceding vehicles in the current lane on car-following behavior and traffic flow was studied in [6; 7; 8; 9; 10; 11; 12; 13; 14], and the impact of information about motion state of vehicles group including the average velocity of preceding vehicles in the current lane was explored in [15; 16; 17; 18; 19]. Although information about multi preceding vehicles, including left and the right front vehicle in the adjacent lanes, are available for drivers in the V2X environment, understanding about the influence of this information on car-following behavior and traffic flow is limited. Motived by this, a concept named GPV was proposed to represent the preceding vehicle group consisting of front vehicle, non-neighboring front vehicle and left/right front vehicle in the adjacent lanes, and average velocity was employed to represent the motion state of GPV. Based on these, an extended car-following model was established and then used to explore the impact exerted by motion state information of GPV on car-following behavior and traffic flow. Research results reveal that motion state information of GPV can optimize driver's car-following behavior and enhance the stability of traffic flow. In Section 3, the acceleration of object vehicles was calculated with the GPV model and the FVD model, respectively and compared with the verification data sets. The comparison (as shown in Figure 3) shows that the model established in this research with consideration of GPV motion state can better fit the data sets and reduce acceleration/deceleration delay existing in the FVD model. This illustrates that information about GPV motion state can enable drivers to grasp traffic situation ahead on the road instead of in the current lane and guide drivers to take measures in advance to decrease the response delay. The comparison results also reveal that taking GPV motion state account into the car-following model can make the model more in line with driving behavior characteristics, which is that drivers not only focus on the front vehicle but also pay attention to multi-preceding vehicles, including left/right front vehicles, and thus fit the measured data more accurately. In Section 4, the neutral stability condition of the GPV model was derived via linear stability analysis and then compared with that of the FVD model. The stability analysis results infer that considering the motion state of GPV can enhance the stability of traffic flow on a certain scale, and traffic flow will be more stable as more attention of drivers attached to the motion state of GPV. Explanation of these results are as follows: On one hand, information about the motion state of GPV can assist drivers with better understanding traffic situation ahead and guide them take measure earlier, which can effectively reduce reaction delay. On the other hand, with a better understanding of the traffic situation ahead, drivers can complete the maneuvering process with a relatively small acceleration/deceleration value to achieve a new stable driving state. Results in Sections 3 and 4 suggest that GPV motion state information can effectively optimize driver's car-following behavior and  
Figure 10: Propagation process of disturbance in the vehicle fleet with the two models. enhance traffic flow stability. Those results also confirm that it is necessary to take GPV into account into studying car-following behavior. To verify the above theoretical analysis results and to present the characteristics of the GPV model in an intuitive way, the numerical simulation based on three typical traffic scenarios with GPV and FDV model was conducted for contrastive analysis in Section 5.