# Speed-Flow-Geometric Relationship for Urban Roads Network
## 3 Methodology
### Data Collection  
A total of 11,812 sample data were collected from the above-mentioned selected roads segment located in Johor using the moving observer method (MOM) and direct visual inspection. Being the most common method for collecting travel time data in an urban traffic environment, moving observer method (MOM) was implemented in this study [47]. In addition, this method is advantageous in measuring the times of travel and flow of traffics simultaneously [48; 49; 50; 51; 52]. Figure 3 illustrates the schematic presentation of the moving observer method (MOM) that was applied to measure the traffic flow and travel speed in the chosen urban areas. In this method, both directions (M: main direction; O: opposite direction) of the traffic movements were considered. During the circular motion around the road segment, the travel time, number of vehicles, and distance (length of the road segment) was measured by a group of people riding the test vehicles or placing a GPS device in the test cars. Figure 2: Data collection sites in Johor, Malaysia ([[https://commons.wikimedia.org/wiki/File](https://commons.wikimedia.org/wiki/File)]([https://commons.wikimedia.org/wiki/File](https://commons.wikimedia.org/wiki/File)): Districts_and_PBT_of_Johor.svg, accessed on 16 June 2021). Table 2 shows the details of various traffic and geometric parameters obtained using the MOM, which were further classified as longitudinal parameters (LP) and cross-sectional parameters (CSP). The value of cross-sectional parameters (CSP) stayed at a constant value over the entire urban roads segment, whereas the value of longitudinal parameters (LP) was varied over the roads segment. These parameter classifications were useful because they defined the number of the developed models in the regression analysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Name of Parameters**} & **Symbol of Parameters** & **Measurement** & **Type of** & **Parameter Group** & **Method of Data** \\  & & **Parameters** & **Unit** & **Variable** & **Type** & **Collection** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Average Travel Speed} & \multirow{2}{*}{ATS} & \multirow{2}{*}{km/h} & Dependent & \multirow{2}{*}{–} & \\  & & & & & variable & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Traffic Volume} & Traffic Volume & \multirow{2}{*}{TV\({}_{\text{vch/n}}\)} & \multirow{2}{*}{veh/h} & \multirow{2}{*}{–} & Moving \\  & & (TV) for all Vehicle Classes & & & & Observer \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{(TV)} & Equivalent Traffic & \multirow{2}{*}{TV\({}_{\text{pcu/h}}\)} & \multirow{2}{*}{pcu/h} & \multirow{2}{*}{–} & \\  & & Volume (TV) for Passenger Car & & & Independent & \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Traffic Calming Speed Density} & TCSD & \multirow{2}{*}{No./km} & \multirow{2}{*}{\begin{tabular}{c} Type I parameters \\ (Longitudinal \\ Parameters, LP) \\ \end{tabular} } \\ \cline{1-1} \cline{5-5}  & & Intersection Density & IntersD & & & Type I parameters \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{Access Driveway Density} & AccessD & & (Longitudinal \\  & & & & Parameters, LP) & \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{Median Existence} & \multirow{2}{*}{M} & 0 = No Median & \multirow{2}{*}{\begin{tabular}{c} (Natural Human \\ Eye) \\ \end{tabular} } \\ \cline{1-1} \cline{5-5}  & & & 1 = Haye Median & & & Eye \\ \hline \multirow{2}{*}{9} & \multirow{2}{*}{Side Friction} & \multirow{2}{*}{SF} & 0 = Low, & \multirow{2}{*}{\
\begin{tabular}{c} Type II parameter \\ (Cross-sectional \\ Parameters, CSP) \\ \end{tabular} } \\ \cline{1-1} \cline{5-5}  & & & 1 = One lane & & & Parameters, CSP) \\ \hline \multirow{2}{*}{10} & \multirow{2}{*}{Number of Lane} & \multirow{2}{*}{NL} & 2 = Two lanes & \multirow{2}{*}{
\begin{tabular}{c} Visual Direct \\ Measurement \\ (Natural Human \\ Eye) \\ \end{tabular} } \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details of various parameters and collected data.