## 4 Experiments  
### Experimental Setup  
To evaluate the performance of the proposed multimodal panic recognition system, we employed a combination of real-world and simulated datasets. Specifically, we utilized the Stampede in Itaewon, South Korea, on 29 October 2022 [33], as a case study for model validation. On 29 December 2024, real-world videos were collected from the Internet and converted into a dataset to capture the flow of pedestrians on Itaewon's Y-shaped micro-road, as illustrated in Figure 7. Panic scenarios in the Stampede in Itaewon dataset were annotated by three experienced annotators with an inter-annotator agreement (Cohen's kappa) of 0.85. This dataset extracts multimodal features, including crowd density maps, trajectory patterns, and semantic cues, to facilitate panic behavior detection. In addition to real-world data, we incorporated benchmark open-source and simulated datasets to assess the model's generalization capability. The UCF Crowd dataset [34], a publicly available dataset comprising diverse crowd density scenarios, was utilized to test the proposed model's robustness across varying congestion levels. Furthermore, we generated synthetic Simulated Panic Behavior Datasets using SUMO (Simulation of Urban Mobility) and Unity 3D, enabling controlled experimentation of pedestrian movement dynamics under sudden emergency scenarios. To adapt to the heterogeneous crowd conditions observed in real-world scenarios such as festivals, rural agglomerations, and nighttime events, the simulation framework should include diverse panic triggers, crowd densities, movement patterns, and lighting variations. These include both structured (e.g., concert-like) and unstructured (e.g., dispersed rural) layouts with synthetic cameras configured for different viewing angles and illumination levels. This diversity aims to bridge the gap between controlled simulations and the unpredictability of real-life emergencies. To create a controlled yet diverse set of panic scenarios, we followed a three-stage pipeline. * Traffic simulation; Origin-destination pairs were randomly generated from a Poisson arrival model (\(\lambda=0.8-1.2\)\(ped/s\)) and assigned to three canonical micro-road templates (Y-junction,  
Figure 7: The real scene of the Stampede in Itaewon, South Korea, at around 10:10 p.m. on 29 October 2022. Note that the red, yellow and blue arrows indicate the direction of the flow of pedestrians, and H and L indicate the high and low areas of the micro-road, respectively. bottleneck corridor, and serpentine queue) implemented in SUMO v1.20. Pedestrians initially walk at 1.8 m/s according to the Wiedemann model. * Traffic Panic trigger; When the local density exceeds 6 ped/m\({}^{2}\) or a virtual fire alarm is broadcast, the agents switch to the Helbing social-force model with an elevated desired speed (2.8 m/s) and a reduced personal-space radius (0.25 m), thus generating irregular flow and counter-moving trajectories. * Visual rendering; The resulting trajectories are exported via OpenScenario and imported into Unity 2022 (HDRP). Five camera angles, day/night lighting presets, and \(\pm\)10% speed noise are sampled to increase appearance diversity. Each run is recorded at 25 FPS, 1280 \(\times\) 720 px, and H.264, yielding 160 unique video clips. All SUMO configuration files, Unity scenes, and conversion scripts (sumo2 unity.py) are publicly available for reproducibility. Table 3 shows the key parameters of the SUMO\(-\)Unity simulation pipeline. While the Itaewon dataset provides a concrete real-world reference, we acknowledge its singular nature. To mitigate potential overfitting and enhance generalization, our evaluation framework leverages the complementary strengths of real and simulation-based datasets to ensure both realism and diversity in recognizing panic scenarios.