## 2 Literature Reviews
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Reference** & **Equation** & **Parameters * \\ \hline
1 & Greenshields model [5] & \(v=v_{f}\cdot\left(1-k/k_{j}\right)\) & \(v_{f}\), \(k_{j}\) \\
2 & Greenberg model [6] & \(v=v_{c}\cdot ln\left(\frac{k_{j}}{k}\right)\) & \(v_{c}\), \(k_{j}\) \\
3 & Underwood model [7] & \(v=v_{f}\cdot exp(-k/k_{c})\) & \(v_{f}\), \(k_{c}\) \\
4 & GHR\_M1 model [22] & \(v=v_{f}\cdot exp\left[-(k/k_{c})^{2}/2\right]\) & \(v_{f}\), \(k_{c}\) \\
5 & GHR\_M2 model [22] & \(v=v_{f}\cdot\left[1-\left(\frac{k}{k_{j}}\right)^{m}\right]\) & \(v_{f}\), \(k_{j}\), \(m\) \\
6 & GHR\_M3 model [22] & \(v=v_{f}\cdot\left(1-\frac{k}{k_{j}}\right)^{m}\) & \(v_{f}\), \(k_{j}\), \(m\) \\
7 & [PERSON] model [10] & \(v=v_{f}\left(1-\left(\frac{k}{k_{j}}\right)^{n+\frac{1}{2}}\right)\) & \(k_{j}\), \(n\) \\
8 & Kuhne and Rödiger model [11] & \(v=v_{f}\left(1-\left(\frac{k}{k_{j}}\right)^{a}\right)^{b}\) & \(k_{j}\), \(a\), \(b\) \\
9 & S3 model [12] & \(v=v_{f}\left[1+\left(\frac{k}{k_{j}}\right)^{m}\right]^{-\frac{2}{m}}\) & \(v_{f}\), \(k_{c}\), \(m\) \\
10 & Two-fluid model [13] & \(v=v_{f}\cdot\left[1-\left(\frac{k}{k_{j}}\right)^{p}\right]^{n+1}\) & \(v_{f}\), \(k_{j}\), \(p\), \(n\) \\
11 & NF model [14] & \(v=v_{f}\cdot\left[1-exp\left(-\frac{\Delta_{f}}{\Omega}\left(\frac{k}{k}-\frac {1}{k_{j}}\right)\right)\right]\) & \(v_{f}\), \(k_{j}\), \(\lambda\) \\
12 & Kerner model [15] & \(v=v_{f}\left[\frac{1}{\left(1+exp\left(\frac{1}{2}\left(\frac{k}{k_{c}-1} \right)\right)\right)}-c_{3}\right]\) & \(v_{f}\), \(k_{c}\), \(c_{1}\), \(c_{2}\), \(c_{3}\) \\
13 & [PERSON] model [16] & \(v=v_{j}+\left(v_{f}-v_{j}\right)\cdot\left(1-k/k_{j}\right)^{m}\) & \(v_{f}\), \(v_{j}\), \(k_{j}\), \(m\) \\
14 & [PERSON] model [17] & \(k=1/\left(c_{1}+\frac{c_{2}}{v_{f}-v}+c_{3}\cdot^{p}\right)\) & \(v_{f}\), \(c_{1}\), \(c_{2}\), \(c_{3}\) \\
15 & [PERSON] model [18] & \(v=v_{f}\cdot\left[\frac{k_{c}^{m}-a^{m}}{[v_{f}+c_{k}]^{m}},m\geq 1,c\geq 0\) & \(v_{f}\), \(k_{j}\), \(m\), \(c\) \\
16 & 5 PL model [19] & \(v=v_{j}+\frac{v_{f}-v_{j}}{\left(1+exp\left[(k-k_{j})/\theta_{1}\right]\right) ^{q_{2}}}\) & \(\theta_{1}\), \(\theta_{2}\) \\
17 & Ni model [20] & \(k=\frac{1}{\left(\gamma v^{  
Existing studies have proved that the queuing model can appropriately simulate the traffic flow of the road section, Based on the queuing theory, the characteristics of traffic flow can be studied. The influence of the behavior of the traffic flow on the road section on the macroscopic characteristics can be analyzed from a more detailed level. The traffic flow model derived from queuing theory is also more advantageous in MED curve sensitivity analysis. Nevertheless, how to model and how to calibrate the corresponding multiresolution parameters still remains a challenge. The rest of this paper is organized as follows: Section 3 illustrates details of the methodology used, including how the queueing model and the FD models are built based on the properties of the roadway connecting to the transportation hub pick-up zone, as well as the measurement methods of the parameters at different resolutions. Section 4 analyzes the basic characteristics of the model and compares the performance with other models using a set of numerical experiments.