[PERSON] [20] found that when the heading vehicle is large, the time headway (THW), time to collision (TTC), and safety margin (SM) of the following vehicle are significantly increased. That means the drivers have a lower level of risk acceptance. According to [PERSON] [21], drivers are inclined to adjust their driving behaviors in response to varying traffic conditions. [PERSON] [22] proposed the concept of proximity resistance and demonstrated that in congested traffic flow, a higher tolerance for approach resistance leads to lower following distances being maintained. Traffic flow has a larger impact on the proximity resistance of truck drivers than that of car drivers. Table 1 summarizes the heterogeneity factors:  
As we can see, heterogeneous factors will affect the car-following behavior of human drivers. By using a large amount of trajectory data, [PERSON] et al. (2017); [PERSON] et al. (2017); [PERSON] et al. (2018); [PERSON] et al. (2019); [PERSON] et al. (2020). confirmed the impact of different factors on car-following behavior, such as vehicle type, preceding vehicle type, and traffic flow. Research shows these factors can have a significant impact on drivers' behavioral habits or decision-making. For example, the behavior of truck drivers in car-following scenarios tends to exhibit more robust patterns compared to that of passenger car drivers. The drivers tend to adjust their driving behaviors in response to varying traffic conditions. [PERSON] confirmed that how to comprehensively consider these factors is an important research topic in areas such as traffic modeling and autonomous driving. These factors have an important impact on the car-following performance of human drivers, so it is very beneficial to consider these factors when building an autonomous driving system that is closer to human behavior. In this research area, some researchers have made contributions. [PERSON]'s model [PERSON] (2016) takes into account different traffic flows. Its equation is as follows:  
\[a_{n}(t)=\alpha^{8}\frac{V_{n}(t-\varphi\tau_{n})^{\beta g}}{X_{n}(t-\varphi \tau_{n})^{\gamma g}}k_{n}(t-\varphi\tau_{n})^{\delta g}V_{n}(t-\varphi\tau_{n} )^{\rho g}+\varepsilon_{n}^{g}(t) \tag{1}\]  
where \(k_{n}(t-\varphi\tau_{n})\) represents the traffic flow by calculating the traffic density of following vehicle within its view (a visibility distance of 100 m was used) at time \((t-\varphi\tau_{n})\), \(g\in[acceleration, deceleration]\), \(V_{n}(t-\varphi\tau_{n})\) is the velocity of the following vehicle at time \((t-\varphi\tau_{n})\), \(X_{n}(t-\varphi\tau_{n})\) is the spacing from the heading vehicle. Where \(\varphi\in[0,1]\) is the sensitivity lag parameter and \(\alpha,\beta,\gamma,\delta,\rho\) are the constant parameters. [PERSON] et al. (2018) proposed the model, which considered driving habits and is shown in Equation (2):  
\[a_{n}=\alpha(\Delta X_{n}/\Delta\widetilde{X}_{n})+\beta(\Delta V_{n})+\lambda+\varepsilon \tag{2}\]  
where \(\lambda\) represents the influence of the driving habit and \(\Delta\widetilde{X}_{n}\) is the desired distance of the driver. Other variables are similar to Equation (1). However, the model lacked validation using real-world data. As aforementioned, data-driven models possess a powerful capability to replicate human behavior. They encode human preferences implicitly within the model. For example, [PERSON] et al. (2018) combined numerous deep neural network structures, such as GRU and CNN, to learn the behavior of human drivers. Human factors such as drivers' preferences, memory effects, prediction, and attention mechanisms could be automatically addressed by the machine learning model. [PERSON] et al. (2018) and [PERSON] et al. (2018) also support this point. [PERSON] (2018) applied the local linear model tree (LOLIMOT) model to build a car-following model and incorporated the vehicle type into the model.