## 4 Experimental Evaluation
### Results  
The previous analysis was used to study the mobility in Athens and adapt the model accordingly. For example, the time to start work was based on the insights from the data, setting the minimum value at 03:00 and the maximum at 09:00. Other parameters of the simulation framework were fed with statistical information from governmental and European Commission reports. For instance, the percentage of people walking in the simulation was set to 42%, following the percentage of citizens who most often use walking as their mode of transport in Athens, based on the 2016 State of European cities report [92]. The maximum driving speed was adapted to the maximum driving speed in the Athens centre, which is 50 km/h. Realistic default values were also used for some parameters to apply to all simulations, such as the minimum driving speed, which was set to 10 km/h, and the minimum and maximum walking speed for people agents, set to 3 and 6 km/h, respectively [93]. Some random value selections had to be made, such as the starting position for the missing child and the POIs to search for. In general, all simulation parameters can be easily adapted to the specificities of a particular city or neighbourhood when there are data available or statistics for the people's mobility there (e.g., car use, traffic hours). They can also take values from probability distributions when, based on the available data, the empirical distributions for the simulation parameters fit well on specific distributions (e.g., gauss, gamma), having the least Sum of Square Error (SSE) among the examined distributions. In the simulation, we explore two different indicators, how much time has passed for the missing child to be found by one of the people agents, i.e., citizens of the simulated city, and the number of times the missing child was found in 2 days to explore the impact that the number of mobile application users, denoted as _nb_people_ in the simulation framework, have over the simulations' result. The _nb_people_ is the independent variable that we aim to explore to investigate the critical mass of application users. The 2 days' timeframe was selected since this was decided to be the threshold below which a missing child should be found with the new mobile application, as indicated and explained in Section 3.2. The simulation step is set at one second, as a quite small step that can enable realistic representation of the motion of both people agents and the missing child, not affecting the accuracy of the results. The aim is to run many simulations using the same conditions and analyse the impact of the number of engaged citizens on the time needed for the missing child to be found. In addition, different values for the probabilities of  
Figure 3: (**a**) The location of the 39 cell sites participating in the analysis. Blue markers show the exact location of a cell site, while green circles represent marker clusters with a number showing the number of cell sites in the vicinity of the circle’s location. Marker clusters group markers at varying zoom levels. (**b**) The mobile phone users’ number per hour and weekday. the probabilistic discovery model, discussed in Section 3.1, are explored to examine the impact of stochasticity on the simulation results. We launch ten (10) simulations for each experiment examining a different number of people agents and display the indicators' mean and standard deviation values, keeping the seed values constant for the random number generators among the experiments. The variable _nb_people_ took values from 50 to 2200 with a step of 20 to examine a great range of people agents' numbers in the simulation. The batch experiments were conducted twice, one for each indicator explored. For each batch experiment, each different _nb_people_ value ran 10 times, as mentioned above, leading to 2160 simulations. For each experiment of an increment of 20 users added, the number of iterations was set to 10.