# Speed-Flow-Geometric Relationship for Urban Roads Network
## 4 Results
### Models Application
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Roods’ Group Features**} & \multirow{2}{*}{\begin{tabular}{c} **Max. FFS** \\ **(km/h)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Type of Longitudinal Parameters (LP) and** \\ **Range of** \\ **Values (No/km)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Range of Traffic** \\ **Volume (TV)** \\ **(veh/h)/(pcu/h)** \\ \end{tabular} } \\ \hline
1 & [M0, NL1, SF0] (Model B) & 39.7 & AccessD (0, 10, 20, 50, 100) & (0–1989) \\ \hline
2 & [M0, NL2, SF1] (Model A) & 32.14 & TCSD (0, 5, 10, 25, 50) & (0–2650) \\ \hline
3 & [M1, NL2, SF1] (Model A) & 37.47 & AccessD (0, 10, 20, 40) & (0–3730) \\ \hline
4 & [M1, NL2, SF0] (Model B) & 40.77 & (No-Longitude Parameters) & (0–4077) \\ \hline
5 & [M0, NL1, SF1] (Model A) & 34.82 & \begin{tabular}{c} TCSD \\ (0, 2, 5, 10) \\ \end{tabular} & \
\begin{tabular}{c} IntersD \\ (0, 2, 4, 8) \\ \end{tabular} &
\begin{tabular}{c} (0–3482) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Scenarios for models’ application. Figure 10: (a,b) Speed-flow models for road’s categories [M1, NL2, SF1] and [M1, NL2, SF0]. Figure 9: (a,b) Speed-flow models for road’s categories [M0, NL1, SF0] and [M0, NL2, SF1]. It is obvious that the application of the speed-flow models was limited by the free-flow speed (FFS). In the proposed model, the free-flow speed (FFS) was defined as the intercept value of each succeed developed model (Table 5). The predicted average travel speed (ATS) used a particular model or equation without exceeding or equal to the free-flow speed (FFS) for any condition. In other words, if the value of the longitudinal parameter or the summation of the longitudinal parameter's value and traffic volume was greater than the predefined value of the free-flow speed (FFS) (intercept), the model was not accepted for application because the average travel speed (ATS) values were negative. The proposed models can fundamentally contribute in terms of the applicability in real situation wherein a broad range of geometric and traffic parameters variability in the urban road network can physically be incorporated. Therefore, the models can effectively be used as significant tools to measure the average travel speed (ATS) with diverse geometric and  
Figure 11: (**a**,**b**) Speed-flow models for road’s category [M0, NL1, SF1] for TCSD = 0/km, and 2/km. (**c**,**d**) Speed-flow models for road’s category [M0, NL1, SF1] for TCSD = 5/km, and 10/km. traffic features on the urban roads. A series of steps must be followed to achieve these goals. First, the cross-section features of the urban roads must be identified that can be determined by three parameters like the median existence, number of lanes, and condition of side friction. Consequently, it can be measured as two different integer values such as 1 = having a median, 0 = without a median, 1 = high-side friction, 0 = low-side friction, and the number of lanes (1, 2, and 3). Second, following the unification of these three cross-section features the proposed model one must select all other five models (Table 5). Third, the values of the longitude features of the urban roads must be quantified accurately that requires the density values of the facilities i.e., their numbers over the entire urban road length. Essentially, any combination of the density for the traffic calming speeds, access driveways, and intersections can be substituted in the proposed model. Fourth, the traffic volume information must be inserted in the proposed model for accurate prediction. Finally, the impact of the traffic composition in the respective models must be considered by applying the pcu conversion factor to the traffic volume.