LAUSR

When there are too many people in large shopping malls, crowd congestion accidents are likely to occur. To ensure the rapid and safe evacuation of indoor crowds, this paper uses crowd density maps to determine the location of crowded areas and uses an improved ant colony algorithm to optimize the evacuation route from this location to the exit. First, a crowd density map is generated from the collected image data by the improved multiscale convolutional neural network algorithm, and the location of the high-density crowd is determined as the initial location. Then, the pheromone volatility coefficient $\rho $ is measured through adaptive adjustment by using the exponential decline method and the introduction of elite ants to optimize and update the ant colony pheromone to improve the ant colony algorithm, and the optimal evacuation route from the location of the crowded area to the exit is obtained. The research in this paper uses Beijing Xidan Joy City as an example. The results show that the method in this paper can optimize evacuation routes and reduce the turning points of the evacuation route by 25% and reduce the route length by 10%. Therefore, it can be seen that the proposed method can achieve the optimal evacuation path with the shortest distance and the least turning points, which has feasibility and practicability.