# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 4 Discussion
On the one hand, the time delay cannot be totally eliminated in the traditional or the V2X environment, and thus it is necessary to incorporate the time delay in exploring the car-following behavior. On the other hand, the memory effect is one of the main actual characteristics of human drivers, and the models with consideration of the memory effect will better fit the actual car-following behavior and be able to reproduce several nonlinear traffic phenomena. Especially considering that the automatic controller cannot fully replace the human drivers in the foreseeable future, it is necessary to incorporate these two effects in modeling the car-following behavior. For the feedback control scheme, a series of CM-based models has moved the research on modeling car-following behavior into a new field by combining it with the control scheme. Considering the development and popularization of the automatic controller, such as the ACC/CACC system, it is of great significance to research modeling car-following behavior through combining it with the control scheme. The applications of car-following models are the direct embodiment of their value. As the core of traffic flow theory, the car-following models are the theoretical basis for many traffic flow studies. These studies are essentially the applications of the car-following models, and calibration of the model needs to be carried out before being used in these applications. Accordingly, the works on the applications of car-following models were reviewed from three aspects, which are the model calibration, traffic flow characteristics analysis, and estimating the energy consumption and the emission. With the aforementioned review contents in the previous sections, we can obtain that the core of research on car-following behavior in the V2X environment is modeling the behavior. In the previous works, the impact of information about aspects of the motion state of vehicles in the front or back, such as position, velocity, acceleration and ETOA, information about the motion state of the vehicles platoon, such as average velocity, and the driver's characteristics of applying information, such as the time delay and the memory effect, on car-following behavior were explored within a process. The results reveal that the characteristics of car-following behavior are affected by various sources of information and consider the information applying characteristics, and can provide an important reference for further updating traffic flow theory, the planning/designing/constructing/managing of the transportation system, and developing next-generation vehicles equipped with personalized ADAS or quasi-human automatic controller. However, there are still some imperfections in these previous works. To solve these problems, further exploration needs to be carried out. Meanwhile, the development of big data, cloud computing, autonomous driving, and other emerging technologies provides development opportunities for further efforts on the car-following model and its applications. The imperfections of previous studies and the trends that require further exploration will be discussed in the following sections.