## 5 Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Fundamental**} & \multicolumn{1}{c}{**Calibrated**} & \multicolumn{1}{c}{**R-**} \\  & **“Speed-Density” Equation** & **“Speed–Density” Equation** & **Squared** \\ \hline [PERSON] (1935) [7] & \(v(g)=v_{max}\left(1-\frac{g}{g_{max}}\right)\) & \(v(g)=52.05(1-\frac{g}{166.00})\) & 0.2690 \\ [PERSON] (1959) [9] & \(v(g)=v_{c}\ln\left(\frac{g}{g_{max}}\right)\) & \(v(g)=18.02\ln\left(\frac{g(0)}{g}\right)\) & 0.1563 \\ [PERSON] (1961) [10] & \(v(g)=v_{max}\exp\left(-\frac{g}{g_{c}}\right)\) & \(v(g)=54.88\exp\left(-\frac{g}{g_{max}(g)}\right)\) & 0.3760 \\ [PERSON] (1967) [13] & \(v(g)=v_{max}\exp\left(-\frac{1}{2}\left(\frac{g}{g_{c}}\right)^{2}\right)\) & \(v(g)=50\exp\left(-\frac{1}{2}\left(\frac{g}{g_{c}}\right)^{2}\right)\) & 0.3559 \\ Modified Morgan (1975) [64] & \(v(g)=\frac{v_{max}-v_{min}}{1+(v(g)g)}+v_{min}\) & \(v(g)=\frac{49.84+4.32}{1+(0.018\,g)^{1.72}}-4.32\) & 0.3825 \\ [PERSON] (3P) (2011) [23] & \(v(g)=\frac{v_{max}}{1+(v(g)\ln(g-\frac{v_{min}}{g_{c}}))}\) & \(v(g)=\frac{72.43}{1+(0.018\,g)^{1.72}}\) & 0.3817 \\ Kucharski and Drabicki (2017) [4] & \(v(g)=\frac{v_{max}}{\left[1+g\left(\frac{g}{g_{c}}\right)\right]^{3}}\) & \(v(g)=\frac{54.87}{\left[1+0.01\,\left(\frac{g}{g_{c}}\right)\right]^{3.60}}\) & 0.3611 \\ Gaddam and Rao (5P) (2019) [58] & \(v_{max}\left\{\frac{\exp\left[-\left(\frac{g}{g}\right)^{1.411}\right]-\exp \left[-\left(\frac{g_{max}}{g_{c}}\right)^{1.411}\right]}{1-\exp\left[-\left( \frac{g_{max}}{g_{c}}\right)^{1.411}\right]}\right\}^{\delta 2}\) & \(v(g)=44\left\{\frac{\exp\left[-\left(\frac{g}{g}\right)^{1.411}\right]-\exp \left[-\left(\frac{g_{max}}{g_{c}}\right)^{1.411}\right]}{1-\exp\left[-\left( \frac{g_{max}}{g_{c}}\right)^{1.411}\right]}\right\}^{\delta 8}\) & 0.3730 \\ Gaddam and Rao (6P) (2019) [58] & \(v_{max}\left[1-\left(\frac{g}{g_{max}}\right)^{1.71}\right]^{2}\left[1+E \left(\frac{g}{g_{max}}\right)^{1.73}\right]^{-1}\) & \(v(g)=63.97\left[1-\left(\frac{g}{g_{c}}\right)^{0.41}\right]^{3.60}\left[1+492.5 \left(\frac{g}{g_{c}}\right)^{0.40}\right]^{-1}\) & 0.3810 \\ [PERSON] et al. (2020) [58] & \(v(g)=v_{c}\ln\left(\frac{g_{max}+g_{min}}{g_{c}+g_{max}}\right)^{\phi}\) & \(v(g)=25.88\ln\left(\frac{g_{max}}{g_{c}}\right)^{0.57}\) & 0.3123 \\ [PERSON] et al. (2021) [63] & \(v(g)=\frac{v_{max}}{\left[1+\left(\frac{g}{g_{c}}\right)^{2}\right]^{1.776}}\) & \(v(g)=\frac{56.13}{\left[1+\left(\frac{g}{g_{c}}\right)\right]^{2}}\) & 0.2681 \\ \hline \hline \end{tabular} The meaning of the parameters in the table is as follows: \(v(g)\) represents speed as a function of traffic density, expressed in kilometers per hour; \(g\)—represents traffic density as an independent variable, expressed in the number of vehicles per kilometer; \(v_{max}\) denotes free-flow speed; \(g_{max}\) denotes jam density; \(v_{c}\) is the critical speed at road capacity; and \(g_{c}\) is the critical density at road capacity.  
\end{table} 
Table 1: Comparative overview of fundamental and calibrated “speed–density” equations of traffic flow models considered, obtained by fitting the “speed–density” regression functions to empirical data contained in the overall statistical sample collected from selected representative segments of regional road ZC5210. considered to empirical data contained in the partial statistical sample collected on an open road segment (road segment A) and the second one by fitting the models considered to the observations included in the partial sample gathered on the road segment with signalized intersection (road segment B). Table 2 gives a comparative overview of the specific mathematical formulations of the traffic flow models considered, which were identified as representative of modeling continuous and interrupted traffic stream conditions on the observed regional road, together with the correlation and determination coefficient values obtained.