# How Human Mobility Models Can Help to Deal with COVID-19
## 4 Generation of Scenarios
### Station Scenario  
Finally, this scenario is a closed space, a subway station, also located in the city of Valencia and locally known as \"Alameda Station\" (we will refer to it simply as the Station). This scenario represents a typical example of a crowded place, with different degrees of renewal and flows of people. This station has four tracks and three platforms: two side platforms and a central one, with an area of approximately 150 m\(\times\)50 m. The platforms are accessible by stairs that can be reached through the four entrance doors located at each corner of the station, as can be seen in Figure 2. Four metro lines pass through this station, with an average interval between 5 min and 10 min.  
Figure 1: Plaza scenario, located in Valencia, Spain. (**a**) Aerial view of the plaza. It is a very touristic square, with dimensions of approximately \(120\times 120\) m. The walkable space is surrounded by buildings, with a fountain in the centre and seven entrances. (**b**) Design of the PedSim scenario, showing the walking pedestrians (small blue points) and the points of interest delimited by black circles.  
To recreate the scenario, we took real measurements on 17 December 2020, from 8:00 a.m. to 9:00 a.m., by observing in-situ people arriving or leaving the station at different times of the day. Particularly, we measured the arrival time of the trains, the number of people who entered or left those trains, and the people who arrived from the four entrance doors. Based on these values, which are still valid for the current pandemic status, we simulated the subway station, considering as in the Plaza scenario, the social distancing of the current pandemic times.  
In order to parametrise this scenario we have defined several metrics: the average rate of arrival \(\beta_{D}\) and departure \(\delta_{D}\) of the station using the entrance gates, the average time between trains \(A_{T}\), and the average number of people arriving \(P_{A}\) and leaving \(P_{L}\) on each train. Using these values, we can obtain the whole number of people who have entered or left the station through the entrance doors (\(N_{I}=\beta_{D}\cdot 3600\) and \(N_{O}=\delta_{D}\cdot 3600\)), as well as the number of people who have been at the station: \(N=N_{I}+N_{O}\). The main parameters of the Station scenario are shown in Table 1.  
Regarding PedSim, the scenario generation and pedestrian's movement were implemented taking into account the previous values and the following aspects.  
1. People entering the Station. People enter the station through the doors of the main entrances. Specifically, for each of the four entrances, new pedestrians are generated according to a Poisson process with rate \(\beta_{D}/4\), entering the station and passing through the turnstiles located at the two ends of the station. Then, each pedestrian is randomly directed with equal probability to one of the four platforms.
2. Arrival of trains. The arrival times of the train are generated with the values obtained from the measurements. When a train arrives, pedestrians wait on the corresponding platform, get on the train, and disappear from the PadSim simulation. At the same time, pedestrians get off the train and enter the platform. From this platform, and for  
Figure 2: Alameda Station, in Valencia, (**a**) Inside view of the station. It is a subway station with four tracks and three platforms. (**b**) Scenario generated with PedSim. We can see several pedestrians entering and leaving the place (blue dots).  
each pedestrian, a station exit door is randomly selected. Then, the pedestrians go directly to the selected gate and leave the station.
3. Pedestrian's movement: As in the Plaza scenarios, the movement of pedestrians follows the social force model with an average speed in the range of 0.3-1.5 m/s.