# Emergency Evacuation Simulation Study Based on Improved YOLOv5s and Anylogic
## 1 Introduction
The four factors include pedestrians' familiarity with the layout of the place, pedestrians' grouping behavior, the internal layout of the place, and the safe carrying capacity of the place. [PERSON] et al. [14] demonstrated that all these non-adaptive evacuation behaviors have some negative impact on pedestrian evacuation efficiency by simulating non-adaptive evacuation behavior pairs such as inertial behavior, folding behavior, herding behavior, and partnering behavior of pedestrians. [PERSON] et al. [15] used [PERSON]'s algorithm to calculate the optimal evacuation path under a fire scenario and demonstrated that the method is consistent with the actual evacuation road situation and can effectively determine the optimal evacuation route for traditional village complexes under fire. [PERSON] et al. [16] proposed an FL model based on fuzzy logic to cope with the heterogeneous behavior of evacuees after disasters such as earthquakes and chemical plant explosions, resulting in emergency evacuation measures due to large uncertainties. [PERSON] et al. [17] proposed a model for sudden fire evacuation of large shopping malls through fire dynamics simulation. [PERSON] et al. [18] proposed a hybrid mathematical planning model for uncertain multi-objective, multi-commodity, multi-cycle location assignment using a multi-objective optimization and location assignment model. [PERSON] et al. [19] developed an algorithm using the CFAST model to generate the shortest evacuation route in the form of a clear tree diagram in the shortest evacuation time. On the basis of pedestrian traffic modeling, with the continuous improvement of computer models, a variety of commercial pedestrian traffic simulation software has emerged, including Legion, Vissim, Simwork, Anylogic, etc. The improvement of models and the development of simulation software have enabled the emergency evacuation problem to be solved to a certain extent, but in the process of extracting information about evacuation targets, most of the data acquisition methods used by research scholars are non-machine vision methods to acquire data, including the use of broadband satellite networks, wireless local area networks (WLAN), Bluetooth sensors, etc. Although better research results have been achieved with non-machine vision-based methods, they are difficult to widely promote and apply due to the many uncertainties in the data acquisition process. Therefore, this paper, based on previous research, applies the YOLOv5 target detection algorithm to the emergency evacuation problem for the first time, with the aim of using the stronger detection capability of YOLOv5 to compensate for the shortcomings of traditional methods in the process of data acquisition. This paper also uses YOLOv5 in combination with Anylogic simulation software to input the pedestrian locations detected by YOLOv5 into Anylogic for emergency evacuation simulation and to simulate a suitable evacuation plan. This article uses Anylogic version 8.5.0. The method proposed in this paper has the advantage of faster data information collection than traditional methods and reduces the overall process time for emergency evacuation by reducing the time used in the data information collection process. In an emergency situation where time is of the essence, making a quick decision and choosing the most appropriate solution can greatly reduce the damage to people and property. The remainder of the paper is structured as follows: Section 2 outlines the structure of the YOLOv5 target detection network and the improvement strategy of this paper, together with an introduction to the Anylogic simulation software. Section 3 outlines the experimental procedure, parameter configuration, and evaluation metrics, and analyzes the experimental results of the method in this paper. Finally, Section 4 concludes the paper and presents future research plans.