## 4 Results
### Results Using the Simulation Framework  
Based on the pilot study at Universidad de Chile station, a simulation model was developed using Anylogic to simulate different escalator configuration scenarios to improve the efficiency of usage of the escalator. The simulation scenarios considered the following variables that affect the behavior of passengers:  
* The location of the passengers on the escalator (e.g., right or left of each step). * The way passengers reach the escalator (e.g., from the front or from the side). * The number of lines of flow of passengers when boarding the escalator. * The number of passengers (e.g., density). The simulation model is available online via the web so that other researchers, practitioners, and students can use it [31]. In the simulation, three scenarios were tested:  
* Scenario A: Passengers do not walk and they stand on both sides of the steps. * Scenario B: Passengers walk from the left side and stand on the right side of the steps. * Scenario C: Passengers walk on both sides of the steps. The results are presented in Figure 7, in which the density of passengers is shown, with the blue representing the lowest density and the red representing the highest density on the escalator. It was noticed that Scenario C reached the lowest density level, as passengers \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Flow Direction** & **Escalator** & \begin{tabular}{c} **Location on the** \\ **Steps** \\ \end{tabular} & \
\begin{tabular}{c} **Average Speed** \\ **[kpx/h]** \\ \end{tabular} &
\begin{tabular}{c} **Count** \\ **[Passengers]** \\ \end{tabular} \\ \hline Downstream & 3 & Right & 232 & 597 \\ Downstream & 3 & Left & 231 & 340 \\ Downstream & 4 & Right & 320 & 16 \\ Downstream & 4 & Left & 265 & 12 \\ Upstream & 1 & Right & 218 & 253 \\ Upstream & 1 & Left & 218 & 131 \\ Upstream & 2 & Right & 221 & 267 \\ Upstream & 2 & Left & 220 & 138 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results using the detection techniques on the escalator at Universidad de Chile station during the off-peak hour. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Flow Direction** & **Escalator** & \begin{tabular}{c} **Location on the** \\ **Steps** \\ \end{tabular} & \
\begin{tabular}{c} **Average Speed** \\ **[kpx/h]** \\ \end{tabular} &
\begin{tabular}{c} **Count** \\ **[Passengers]** \\ \end{tabular} \\ \hline Downstream & 3 & Right & 221 & 30 \\ Downstream & 3 & Left & 224 & 15 \\ Downstream & 4 & Right & 220 & 42 \\ Downstream & 4 & Left & 227 & 14 \\ Upstream & 1 & Right & 233 & 857 \\ Upstream & 1 & Left & 413 & 216 \\ Upstream & 2 & Right & 238 & 857 \\ Upstream & 2 & Left & 405 & 183 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results using the detection techniques on the escalator at Universidad de Chile station during the peak hour. walked on both sides of the steps, reducing the agglomeration at the entrance of the escalator. On the other hand, Scenario B reached the highest density level, due to passengers walking only from the left and standing on the right of the steps. As a consequence, in Scenario B, a bottleneck was reached at the entrance of the escalator. In the case of Scenario A, a high density was observed on the escalator due to passengers standing on both sides of the steps. In terms of travel time, the following figure should be considered. Figure 8 shows the time passengers spent accessing the escalator (e.g., before boarding) and the time spent on the escalator (e.g., walking or standing). It is interesting how the travel time changes for each scenario, in which Scenario C reached the lowest travel time, while Scenario A reached the highest. In addition, the interface is presented in Figure 9, in which passengers are represented as agents who are walking or standing on the escalator.