# Emergency Evacuation Simulation Study Based on Improved YOLOv5s and Anylogic
## 4 Conclusions and Future Work  
In this paper, we propose an emergency evacuation scheme based on a combination of YOLOv5s-SE target detection and Anylogic simulation. We first detect the head of a pedestrian inside a yard station by using the YOLOv5s target detection network. We added the SE attention mechanism to the YOLOv5s network and used both NWD and IoU metrics together to not only avoid the model converging too slowly, but also to improve the accuracy of the model for small target detection. To demonstrate the advantages of the improved YOLOv5s network, we compared the improved network with other advanced target detection networks, and the experimental results showed that the improved network model has higher detection accuracy and faster detection speed. Once the location of the pedestrians was determined, they were fed into Anylogic's pre-built emergency evacuation model for simulation. The simulation results showed that pedestrians inside the yard station would eventually leave at the exit nearest to them, and those who were not on the  
Figure 12: Emergency evacuation pedestrian density map.  
ground floor would first reach the ground floor by the stairs nearest to them, and then follow the exit nearest to them. In practice, the simulation results can be communicated to pedestrians by means of screens, announcements, and staff inside the yard, guiding them to follow the simulated emergency evacuation route. The Anylogic-based emergency evacuation model is easy to modify, allowing staff to adapt the model to the actual situation on site and quickly simulate a new evacuation route. In summary, both the YOLOv5s target detection model and the Anylogicy emergency evacuation model are fast enough to provide a suitable solution within a short period of time after an emergency event and are well suited to the emergency evacuation of pedestrians within a yard.  
Although the proposed method is able to detect pedestrians inside the station and simulate a reasonable emergency evacuation route, there are still areas for improvement. In terms of target detection, due to the complexity of the actual situation inside the station, there are still some missed detections. In terms of emergency evacuation simulation, the method only achieves the shortest distance for pedestrians under reasonable circumstances but does not take into account the impact on evacuation caused by unreasonable behavior of pedestrians due to fear and other psychological factors during actual emergencies.  
In future research, on the one hand, the number of target detection datasets needs to be expanded, and a better-performing target detection model needs to be trained through continuous attempts to improve the network structure. On the other hand, the psychological factors of pedestrians, personality factors, and interactions between pedestrians will be taken into account in the emergency evacuation model to further improve the structure of the model and simulate the emergency evacuation process more realistically.  
Conceptualization, C.N.; Data curation, C.N., W.W. and H.G.; Formal analysis, C.N.; Funding acquisition, K.L.; Investigation, C.N.; Methodology, C.N.; Project administration, C.N.; Resources, C.N.; Software, C.N.; Supervision, K.L.; Validation, K.L.; Visualization, C.N.; Writing--original draft, C.N.; Writing--review and editing, C.N., W.W. and H.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Research Project of the Jilin Provincial Education Department, grant number JIKH20220057 KJ.  
Not applicable.  
Data operlly available in a public repository.  
The authors declare no conflict of interest.