# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Modelling the Car-Following Behavior
#### 3.2.1 Information of State in the Downstream  
With the V2X technologies, especially the V2I technology, the information of state in the downstream can be collected and transmitted to the driver. Thus, drivers can understand the operating situation of the front traffic from the system level, and, based on this, they can take appropriate measures to adjust and optimize their car-following behavior. Among the information of state in the downstream provided by the V2X (V2I) technology, [PERSON] et al. updated the parameters in the FVD model, introduced a new item to form an extended model, and analyzed the car-following behavior affected by the information about disturbance (represented by a traffic accident) in the downstream [25]. The results suggest that this kind of information can optimize the decelerating process, enhance driving safety and improve accessing efficiency. Later, they further introduced the lane-changing rules, proposed by [PERSON] and [PERSON] [26; 27], and extended the model for the two-lane scenario [28]. Based on analyzing the field data, [PERSON] et al. proposed an extended FVD model with consideration of the remaining time of green traffic signal [29]. Based on similar ideas and methods, [PERSON] et al. further extended the FVD model by updating the parameters and introducing a new item, and analyzed the car-following behavior affected by the information of real-time traffic signals, including the remaining duration of green light [30]. An FVD-based piecewise car-following model was proposed by [PERSON] et al. to explore the car-following behavior with consideration of the speed guidance information under four signal control conditions [31]. The results reveal that this kind of information of state in the downstream will exert a positive influence on drivers' car-following behavior and the degree of this influence is positively correlated with the transmission range and the proportion of connected vehicles. Soon after, they further studied the impacts of speed guidance information on drivers' car-following behavior in conflicting traffic flows in intersections without signal control [32]. Based on the above works, [PERSON] et al. further discussed the impacts of speed guidance information on drivers' car-following behavior in the signalized intersection with consideration of the number of vehicles in the queue [33]. The results suggest that the aforementioned information can help with optimizing the braking-to-stop access and improving the traffic efficiency of the interaction. The results of the above research reveal that the information on traffic incidents, traffic signals, speed guidance, and other information regarding traffic state in the downstream can assist drivers with taking appropriate measures to optimize their car-following behavior in advance before they realize the situation through visual contact, which was the way in the previous traffic environment without V2X technology. These positive effects are mainly embodied in the following:  
* The vehicles can decelerate to lower speed within a shorter time and take larger headway, which will enhance driving safety;
* The accelerating and decelerating processes, and especially the braking-to-stop process, are optimized;* The traffic efficiency of the road or interaction is improved, which means that the number of vehicles passing by the road or interacting within per time unit increase. These results also confirm that utilizing V2X technology to provide the information of state in the downstream, which reflects the operating or control situation, for the driver is of great significance for optimizing the traffic flow. In addition to the information reflecting the downstream state of the transportation system, the driver-vehicle unit can obtain abundant microscopic traffic information of multiple vehicles in the system including the motion state (position, velocity, acceleration, etc.) and adjust the car-following behavior according to the information.