# Multi-Objective Routing and Categorization of Urban Network Segments for Cyclists
## 2 Materials and Methods
### Experiments
#### 2.3.2 Data Collection
It is stated that in the case of using links with \(k=p1,p2,b1\), i.e., links of pavements, footpaths, pedestrian crossings, and cycle lanes, the value of the variable will be 0 since on these links cycle users are not confronted with buses or trolleybuses and are, therefore, assumed to treat these links in the same way as they would treat a road link (\(k=r1,r2\)) which is not crossed by a bus or trolleybus with respect to this parameter. Existence or not of a bicycle lane: The values that this parameter can take are {0, 1}. Data were collected through the Google Maps platform to identify the bicycle lanes (see Figure 6). Travel time: The data collection of this variable was performed through the Strava application [39]. This application collects data from athletes worldwide, professionals, and amateurs to provide appropriate data for sports activities such as running, walking, cycling, and others. In this application, there is a platform that allows creating a route on a map for  
Figure 4: Special case of link 1, 82, r1 where overtaking is impossible. Source: Google Earth. Figure 5: Bus routes in the study area. which it gives data such as distance, altitude difference, ground type, and estimated travel time. In the case of cycling, the application gives an estimate of the travel time but does not consider the influence of slope on this estimation. However, the influence of slope is considered in the case of running. To extract valuable data from this application, we have to rely on a more reliable source on its environment--which is the running time--and connect it to our desired data--which is the cycling time. To address this, we used Pearson correlation, a common measure in data analysis, that helps to evaluate the strength of linear relationships between variables [40]. Sixteen random routes of various lengths were selected inside Strava's environment, all of which had zero slope. The choice of the zero slope is set to avoid Strava's lack of consideration of the slope in cycling travel time estimation. The application calculated the journey time in minutes for the running and cycling. For these 16 routes, the results of the running and cycling travel times were extrapolated in Table 1, and for this sample, the Pearson correlation coefficient of the travel times (running and cycling) was calculated as 0.993 (\(\geq\)0.8), indicating a strong linear correlation between the two times. Dividing the columns of the table shows that the average coefficient is 2.5. Therefore, the bicycle travel time for this survey is calculated as \(t_{\text{i,j,k}}\) = Travel Time of Strava \(\times\) 2.5. \begin{table}
\begin{tabular}{c c c} \hline
**Running Travel Time** & **Cycling Travel Time** & **Multiplied Factor** \\
**(Minutes)** & **(Minutes)** & **Multiplied Factor** \\ \hline
0.1 & 0.033 & 3 \\
0.217 & 0.1 & 2.167 \\
0.433 & 0.183 & 2.364 \\
0.467 & 0.183 & 2.545 \\
0.483 & 0.183 & 2.636 \\
0.5 & 0.2 & 2.5 \\
0.55 & 0.233 & 2.357 \\
0.567 & 0.25 & 2.267 \\
0.633 & 0.25 & 2.533 \\
0.717 & 0.283 & 2.529 \\
0.733 & 0.283 & 2.588 \\
0.8 & 0.317 & 2.526 \\
0.8 & 0.333 & 2.4 \\
0.85 & 0.333 & 2.55 \\
0.9 & 0.35 & 2.571 \\
1.817 & 0.75 & 2.422 \\ \hline \end{tabular}  
* Average value = 2.5; correlation = 0.992649361.  
\end{table} 
Table 1: Pearson correlation coefficient estimation for cycling and running travel times in Strava’s environment. Figure 6: Bicycle lane links N\({}_{\text{i,j,k}}\). Source: Microsoft Bing Maps. Function \(t_{\text{i,j,k}}=\text{Travel Time of Strava }\times 2.5\) can be applied with acceptable accuracy to links with \(k=r1,r2\), i.e., the road links, but also the sidewalks of the Zappeion Gardens and the National Garden. However, in the case of pedestrian pavements \(i,j,p1(or\ p2)\), the travel time will be significantly longer than that of the corresponding road link \(i,j,r1(or\ r2)\). This is due to numerous factors, such as the existence of obstacles, curb ramps, and uneven surfaces.