# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Empirical Models of Lane Change
#### 2.1.1 Logit Models
Based on the difference in speed and density between lanes, a binary logistic lane change model is proposed to describe the lane change. The binary logistic lane change model is then integrated into the refined CTM, which describes how the vehicle moves vertically from one unit to another. The performance of the model was evaluated by comparing the simulated cell occupancy of the optimized model with the cell occupancy of the US-101 Next Generation Simulation (NGSIM) [28] data. The arbitrary lane change behavior is closely related to the incidence of accidents. [PERSON] et al. [29] conducted a random parameter logit method analysis on the mean and variance heterogeneity of distance- and speed-related factors in the micro-vehicle trajectory dataset. This new method was proposed based on three sets of the 1:1 matching logit model [29], which uses the NGSIM dataset and smooths it before use [30]. Researchers have shown that three adjacent vehicles have a significant influence on lane-changing behavior [31; 32; 33]. Therefore, four types of vehicles--vehicles requiring lane change (SV), vehicles ahead of lane change (CLV), vehicles in front of target lane (TLV), and vehicles behind target lane (TFV)--are selected, as shown in Figure 1 below. The four types of vehicles are analyzed in any combination, and the dynamic factors of the lane change decision process are extracted using the algorithmic model. The results show that the random parameter corresponding to the distance-correlated variable has mean and variance heterogeneity, while only one random parameter corresponding to the speed-correlated variable has mean and variance heterogeneity. The average heterogeneous relationship between the speed of the vehicle preparing to change lane and the distance between the leading vehicle in the same lane and the vehicle about to change lane indicates that the driver considers the distance and speed between the vehicle and the neighboring vehicles when making a lane change. This algorithm can allow us to better understand and quantify the driver's habitual decisions in lane change behavior. Table 1 provides a summary of the logit models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Input Variables** & **Algorithm** & **Key Issues to Be Resolved** \\ \hline \multirow{2}{*}{[23]} & \multirow{2}{*}{Multi-lane macro traffic flow} & \multirow{2}{*}{Logit} & The multi-lane traffic flow is described from a \\  & & & macroscopic perspective \\ \hline \multirow{2}{*}{[24]} & \multirow{2}{*}{Traffic accident data} & Mixed (random parameters) & Increased awareness of the dangers of changing \\  & & logit model & lanes near large commercial vehicles or driving \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Input parameters of different logit lane change models and key problems to be solved. Figure 1: The red vehicle is the lane changing vehicle (SV) in the process of lane change, the green vehicle (CLV) is the position relationship between the vehicle in front of the lane changing vehicle, the vehicle in front of the target lane (TLV), and the vehicle behind the target lane (TFV) and the lane changing vehicle (SV), and the blue vehicle represents the related vehicles in the surrounding lane [29].