## 3 Problem Statement and Data Collection
### Data Collection  
Traffic volume, as the most important parameter in VISSIM simulation, significantly impacts simulation results in terms of authenticity and accuracy. Compare and evaluate different models by inputting realistic traffic volumes and finally obtain a suitable auxiliary lane design form. Based on AutoNavi Traffic Big Data's traffic congestion index statistics for Xi'an in March 2023, the average congestion index for each hour is plotted in Figure 3. It can be seen that traffic in Xi'an City has the highest traffic volume in the morning from 7:00 to 9:00 a.m. and in the afternoon from 17:00 to 19:00 p.m. Therefore, the morning and evening peaks were selected to collect traffic volume data. Good external conditions are a prerequisite for the accuracy of the collected data. Therefore, the data collection was carried out under optimal conditions, ensuring the absence of traffic congestion, road \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **Traffic Volume** & **Diverging/** & **Number of** & **Vehicle** & **Traffic Flow** \\  & **Volume** & **Emerging Ratio** & **Mainline Lanes** & **Composition** & **Characteristics** \\ \hline Traffic Volume & 1 & 1 & 5 & 5 & 7 \\ Diverging/Emerging ratio & 1 & 1 & 7 & 8 & 9 \\ Number of mainline lanes & 1/5 & 1/3 & 1 & 3 & 5 \\ Vehicle composition & 1/5 & 1/8 & 1/3 & 1 & 2 \\ Traffic flow characteristics & 1/7 & 1/9 & 1/5 & 1/2 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Expert 1’s judgment matrix. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **Traffic Volume** & **Diverging/** & **Number of** & **Vehicle** & **Traffic Flow** \\  & **Emerging Ratio** & **Mainline Lanes** & **Composition** & **Characteristics** \\ \hline Weight & 0.3037 & 0.4739 & 0.1307 & 0.0541 & 0.0376 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Weighing of factors. maintenance or construction, good weather conditions, and the absence of traffic accidents. The collection tool was the UAV. Two UAVs were utilized to collect data simultaneously in the emerging and diverging areas to ensure that traffic volumes on the mainline, emerging, and diverging areas could be captured. The collected data included the following information:  
* Traffic volume on the mainline, entrance ramp, and exit ramp;
* The ratio of vehicle types on the mainline and ramps;
* The lane widths of the mainline and ramps and the auxiliary lanes' length. As the measurement location is the Xi'an around-city freeway, no buses are operating, only a few intercity coaches. Since the intercity coach count is minimal, and they have a large size and share the same speed limit of 100 km/h as trucks, they are considered trucks in this study. As for minibusses and mid-sized buses, their power characteristics and dimensions are similar to cars; thus, they are categorized as cars. There is no need to differentiate the types of trucks since they all have a speed limit of 100 km/h and are restricted to the outermost lane of the freeway. In the VISSIM software (2022 student), vehicles are classified into three categories: car, truck, and bus. Therefore, this study only focuses on counting the number of trucks and passenger cars. By processing the collected data, the traffic volume during the morning and evening peak hours was obtained to be 2853 veh/h and 3188 veh/h, respectively. Therefore, the traffic volume in the evening peak hour was selected as the simulation input data (see Table 4) and the collected traffic volume data exhibits the following characteristics:  
* Trucks are restricted to the outermost lane due to the mainline lane splitting restriction;
* The proportion of diverging is 51.44%, which is quite large, even larger than the proportion of straight traffic, and the proportion of emerging is 20.33%;
* The proportion of trucks is small, only about 7%.