## 4 Simulation Results and Analysis
### Validating the Model via Comparative Case Analysis
The initial panic source was identified as five passengers intentionally spreading terror-inducing information. Therefore, the model initialized with five adjacent passengers in a state of intense panic, with their initial emotional levels randomly distributed in the range of (0.8,1.0]. The remaining passengers were assumed to be in a calm state, with an initial emotional level of 0. During this incident, metro operators intervened only after all passengers had exited the train carriage. Consequently, it was assumed that no authority intervention occurred, setting \(R=0\). The simulation scenarios at different moments during the Shenzhen Metro 5.17 incident are depicted in Figure 10.  
Figure 10: Simulation scenarios at different moments during the Shenzhen Metro 5.17 incident: (**a**) \(t=0\); (**b**) \(t=10\); (**c**) \(t=24\); (**d**) \(t=40\); (**e**) \(t=80\); (**f**) \(t=120\); (**g**) \(t=122\). At the initial moment (\(t=0\)), passengers were evenly distributed within the train carriage, with five passengers identified as the sources of panic for this incident (as shown in Figure 10a). By \(t=10\), passengers near the panic sources were the first to be affected, and panic began spreading within a small area. This process was manifested by calm passengers (represented by white figures) near the panic sources gradually transitioning to slight panic (represented by green figures), moderate panic (represented by yellow figures), and intense panic (represented by red figures). Three major crowd clusters formed around the panic sources, and these clusters began moving toward the nearest train doors (as shown in Figure 10b). As time progressed, these small crowd clusters continued to expand. On the one hand, they infected new calm passengers, and, on the other, the emotional interactions within the clusters intensified the panic. The density of these clusters increased gradually, with the number of green figures (slight panic state passengers), yellow figures (moderate panic state passengers), and red figures (intense panic state passengers) rising steadily. Meanwhile, the number of white figures (calm state passengers) steadily decreased (as shown in Figure 10c-e). Eventually, the distribution of passengers in the carriage showed a pattern of crowd clusters primarily gathering around the train doors. At the same time, a few calm passengers were scattered in the corners farthest from the panic sources (as shown in Figure 10f). When the train arrived at the station and the doors opened, passengers in the carriage quickly evacuated (as shown in Figure 10g). Figure 11 illustrates the simulation of passenger emotional changes during the Shenzhen Metro 5.17 incident. The distribution of passengers across different emotional states is as follows: passengers in a slight panic state, moderate panic state, intense panic state, and calm state are in the ratio of 10:6:3:1. Overall, the level of panic remains relatively low. This aligns with the official report on the incident, which stated that it was caused by the spread of false information, posed no substantial harm, involved no injuries, and lasted for a short duration. In summary, the simulation results of this model not only correspond with the footage from surveillance videos but also align with the official incident report. This demonstrates the model's accuracy and effectiveness in depicting and predicting panic emotion contagion in real-world scenarios.