## 4 Simulation Results and Analysis
### Validating the Model via Comparative Case Analysis  
To effectively demonstrate the authenticity of this model in simulating the spread of panic among metro passengers, the panic incident that occurred on Shenzhen Metro Line 7 at around 6:30 PM on 17 May 2019 is introduced as a comparative case study. Screenshots from the carriage surveillance video during the train's operation phase are shown in Figure 8, while screenshots from the platform surveillance video during the train's stop phase are presented in Figure 9.  
Figure 8: Panicked crowd in the carriage during the Shenzhen Metro 5.17 incident: **(a)** Panic source; **(b)** First infected passengers; **(c)** Localized panic spread; **(d)** Gathered passengers; **(e)** Secondary panic source; **(f)** Widespread panic spread. During the train's operation, mass panic was triggered by a few passengers intentionally shouting and falsely claiming the presence of a weapon (as shown in Figure 8a, where the red circle marks the source of the panic). Passengers near the panic source were the first to perceive the visual or auditory sensory stimuli, leading them to believe that a potential terror attack might occur. This caused panic and prompted them to flee in the direction of the nearest train door (as illustrated in Figure 8b,c, with red arrows indicating the movement direction of the passengers). Since the train was still moving and the doors remained closed, these passengers gathered and crowded near the doors (as shown in Figure 8d, where the red dashed lines mark the areas near the doors). Meanwhile, other passengers who were farther away from the source, and thus did not perceive any visual or auditory sensory stimuli, remained unaware of the situation and did not take any action. However, the aforementioned passengers carrying panic emotions acted as secondary panic sources (as shown in Figure 8e, where the red circles highlight the secondary sources). While running in panic, they further spread and transmitted their panic to nearby passengers. These nearby passengers, upon perceiving visual or auditory sensory stimuli from the secondary sources, also experienced panic, sought the nearest train door, and fled in that direction (as depicted in Figure 8f, with red arrows representing the movement direction). Eventually, through emotional contagion, the panic rapidly spread. Each passenger carrying panic emotions had the potential to become a secondary panic source, transmitting visual or auditory sensory stimuli to others and escalating the mass panic. As a result, passengers flocked to the nearest train doors, waiting for them to open. When the train finally arrived at the station and the doors opened, passengers rushed to escape the train carriage (as shown in Figure 9). The descriptive analysis of the surveillance video content above has validated the authenticity and effectiveness of the model's emotional contagion rules and passenger movement rules. Next, we further verify the model's validity by comparing the simulation results with real-world scenarios. In this case, Shenzhen Metro Line 7 operates A-type trains, which align with the simulation scenario described in Section 3. The parameters were set as follows: \(L=23\), \(W=3\), and \(M=69\). The incident occurred at 6:30 PM, during the evening rush hour, so the passenger count was set at \(N=300\), with the personality distribution of passengers assumed to follow an even split among the three traits, each accounting for one-third of the total. Based on the surveillance video, passengers had a high likelihood of leaving the train carriage once the doors opened, so the probability was set to \(P=0.9\). The recorded duration of the panic incident from the surveillance footage  
Figure 9: Panicked crowd on the platform during the Shenzhen Metro 5.17 incident: (**a**) The train just arrived; (**b**) Some passengers left; (**c**) More passengers left; (**d**) All passengers left. was approximately 2 min, leading to an _arrival-tick_ value of 120.