## 5 Results
Based on the results obtained, it is evident that nine out of the twelve models proposed for modeling the continuous traffic flow condition achieved similar results in terms of correlation and determination coefficients. Most of the traffic flow models considered show an approximately equal determination coefficient value (r\({}^{2}\) between 0.28 and 0.27). The lowest determination coefficient value (r\({}^{2}\) = 0.24) was obtained for [PERSON]'s model. On the other hand, the results obtained for the models fitted to empirical data collected under interrupted traffic stream conditions show significantly lower correlation and determination coefficients compared to the models proposed for modeling continuous traffic stream conditions. The determination coefficient for regression models adjusted for interrupted traffic stream conditions ranges from 0.07 to 0.11, respectively. The obtained values of the determination coefficient (r\({}^{2}\)) suggest that, depending on the traffic flow model considered, the amount of variability in the speed variable, which can be effectively explained by the density variable, varies from 15.63% to 38.33%. The R-squared values presented in Tables 1 and 2 indicate that the models considered can only explain the relatively small percentage of variability present in the measured vehicle speeds and flows. The low R-squared values obtained for \"speed-density\" and \"flow-density\" models fitted to the empirical data are a result of several factors. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Model**}} & \multicolumn{2}{c}{**Regression Function for**} & \multicolumn{2}{c}{**Regression Function for**} & \\ \multicolumn{1}{c}{\multirow{2}{*}{**Model**}} & \multicolumn{2}{c}{**Continuous**} & \multicolumn{2}{c}{**Interrupted**} & \multicolumn{1}{c}{**R\({}^{2}\)**} \\ \multicolumn{1}{c}{\multirow{2}{*}{**Traffic Stream Condition**}} & & **Traffic Stream Condition** & & \\ \hline [PERSON] (1935) [7] & \(v(g)=53.53(1-\frac{g}{16.000})\) & 0.29 & \(v(g)=34.36(1-\frac{g}{16.000})\) & 0.09 \\ [PERSON] (1959) [9] & \(v(g)=18.75\ln\left(\frac{16.000}{g}\right)\) & 0.24 & \(v(g)=15.03\ln\left(\frac{16.00}{g}\right)\) & 0.06 \\ [PERSON] (1961) [10] & \(v(g)=54.32\text{exp}\big{(}-\frac{g}{16.00\text{g}}\big{)}\) & 0.28 & \(v(g)=50.00\text{exp}\big{(}-\frac{g}{80.99}\big{)}\) & 0.07 \\ [PERSON] (1967) [13] & \(v(g)=50.00\text{exp}\big{(}-\frac{1}{2}\left(\frac{g}{8.00}\right)^{2}\big{)}\) & 0.28 & \(v(g)=50.00\text{exp}\Big{(}-\frac{1}{2}\left(\frac{g}{8.37}\right)^{2}\big{)}\) & 0.09 \\ Modified Morgan (1975) [64] & \(v(g)=\frac{51.87}{1+(0.02\text{g})^{1.86}}\) & 0.28 & \(v(g)=\frac{30.47}{1+(0.02\text{g})^{1.88}}\) & 0.09 \\ [PERSON] (3P) (2011) [23] & \(v(g)=\frac{87.95}{1+(0.02\text{g})^{1.89}}\) & 0.29 & \(v(g)=\frac{43.98}{1+(0.02\text{g})^{1.89}}\) & 0.09 \\ [PERSON] (5P) (2011) [23] & \(v(g)=\frac{66.92}{\left[1+\text{exp}\left(\frac{g}{8.00}\right)^{1.89}}\right] ^{1.00}\) & 0.28 & \(v(g)=\frac{31.32}{\left[1+\text{exp}\left(\frac{g}{4.68\text{g}}\right)^{1.00 }\right]^{1.00}}\) & 0.15 \\ Kucharski and Drabicki (2017) [4] & \(v(g)=\frac{54.66}{1+(0.08\left(\frac{g}{6}\right))^{1.82}}\) & 0.28 & \(v(g)=\frac{35.08}{1+\left[0.01\left(\frac{g}{6}\right)\right]^{1.00}}\) & 0.08 \\ [PERSON] and Rao (5P) (2019) [58] & \(48.2\left\{\frac{\text{exp}\big{[}-\left(\frac{g}{8.7}\right)^{3}\text{exp} \big{[}-\left(\frac{g}{8.7}\right)^{3}\big  
The R-squared values obtained are especially low for models fitted to the empirical sample collected under interrupted traffic stream conditions. This can primarily be explained by the fact that interrupted traffic streams, especially near signalized intersections, have inherent variability due to stop-and-go movements. This introduces additional randomness in the data and therefore lowers the obtained values of the determination coefficient.