###### Abstract  
With the application of vehicles to everything (V2X) technologies, drivers can obtain massive traffic information and adjust their car-following behavior according to the information. The macro-characteristics of traffic flow are essentially the overall expression of the micro-behavior of drivers. There are some shortcomings in the previous researches on traffic flow in the V2X environment, which result in difficulties to employ the related models or methods in exploring the characteristics of traffic flow affected by the information of generalized preceding vehicles (GPV). Aiming at this, a simulation framework based on the car-following model and the cellular automata (CA) is proposed in this work, then the traffic flow affected by the information of GPV is simulated and analyzed utilizing this framework. The research results suggest that the traffic flow, which is affected by the information of GPV in the V2X environment, would operate with a higher value of velocity, volume as well as jamming density and can maintain the free flow state with a much higher density of vehicles. The simulation framework constructed in this work can provide a reference for further research on the characteristics of traffic flow affected by various information in the V2X environment. traffic flow; V2X environment; cellular automata; car-following model; numerical simulation +
Footnote †: journal: Future Internet  
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theory. The impacts of individual motion state information of vehicles, including headway, velocity and acceleration, on car-following behavior and traffic flow were explored in [19; 20; 21; 22; 23; 24; 25; 26; 27]. Compared with the arbitrary number of preceding vehicles in the current lane, drivers would pay more attention to the vehicles within their view field. For the vehicles out of their view, drivers tended to focus on the overall motion state of these vehicles rather than the individual motion state. Based on these, the influence of overall motion state information of the vehicle fleet, such as average headway and average velocity, was discussed in [28; 29; 30; 31; 32]. Results of the above research suggest that individual as well as overall motion state information of preceding vehicles in the current lane can assist drivers to optimize their car-following behavior and, thus, stabilize the traffic flow to a different extent. However, realistic roads are not all one-lane roads, and the motion of the vehicle fleet in the different lanes of the multi-lane road is not independent. Drivers would also pay attention to the motion of front vehicles in the adjacent lanes, especially the left/right preceding vehicle in the adjacent lanes, when they are at the car-following state. 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. However, there is only qualitative analysis of the impacts of various information on the stability of traffic flow in the V2X environment, and the quantitative analysis of operating characteristics of traffic flow is still absent, which makes the above research fail to provide a sufficient and appropriate theoretical basis and reference for the design, organization and management of the transportation system in the V2X environment.