# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Applications of the Car-Following Models
#### 3.3.1 Calibrating the Car-Following Model
The results suggest that the FVD model simplified by [PERSON]'s method is easier to calibrate and the performance of the calibrated model is still relatively high. Then, employing the factor fixation and variance sensitivity method, [PERSON] et al. proposed a robust calibration method for simplifying parameters in the car-following models and verified the proposed method with the ID model and NGSIM data set [122]. The results reveal that the proposed method can reduce the parameters in the ID model from 6 to 3 on the basis of not decreasing the model's performance, which will significantly improve the calibrating efficiency. Data Set Used in Calibrating  
The calibrating process is essentially the process of calculating the undetermined parameters in the model using the data set. Thus, the quality of the data set can significantly influence the performance of the model calibrated with the data set. To be specific, the detecting error, the trajectory length as well as trajectory integrity are the key indexes that determine the quality of the data set. In the V2X environment, the multi-advanced sensors can relatively reduce the detecting error. However, there still are some defects. On the one hand, using more advanced sensors means more cost, making this approach unacceptable in many conditions. On the other hand, no matter how advanced the sensors are, the errors cannot all be eliminated. Considering this, [PERSON] et al. proposed a two-step calibration algorithm with consideration of the detecting error of the data set and verified the algorithm employing the Van-Aerde model along with the data set collected from field experiments organized by themselves [123]. In the same work, [PERSON] et al. pointed out that the large length of the trajectory in the data set can improve the performance of models calibrated with this data set [122]. Complete and accurate trajectory data are the basis of effective calibration. However, the data set collected from the field experiments, especially the natural driving experiments, are not all about car-following behavior. Considering this, [PERSON] et al. proposed a recognition method for extracting complete car-following trajectory data from the driving data set and a method to evaluate the integrity of extracted data [124]. Evaluating Method of the Calibration Results  
When the calibration is finished, the evaluation of the calibration results and the comparison between the calibrated model and other representative models are the following key steps in the whole calibration process. However, the previous evaluation methods are usually suitable for only one specific type of model, making it difficult to conduct the comparison. Aiming at this, [PERSON] et al. proposed a general calibration method based on the Bayesian framework for most kinds of car-following models, and the relatively good performance of this method was verified by them based on seven typical car-following models [125]. However, for most researchers, they would like to use the traditional but widely used calibration methods, such as the genetic algorithm. To compare and analyze the calibration results using these traditional methods, the general evaluation method needs to be constructed. Considering this, [PERSON] et al. proposed an evaluation method using the calibration results of the car-following model based on the Spider Web Graph in the work mentioned above [121]. From data collection to results evaluation, the whole process of calibrating the car-following model is continuous and complex. The before and after steps are connected in this process, and there are a series of scientific and technological issues. Several scholars discussed these issues based on their experience and typical car-following models. [PERSON] et al. discussed the challenges in the process of utilizing natural driving experiments to collect data and then storing, processing, and using the data based on the experience of research centers in three countries [125].