Miguel Toski

Buildings and their supporting infrastructure are vulnerable to both natural and human-made disasters, which pose significant risks to the safety of the occupants. Evacuation models are essential tools for assessing these risks and for ensuring the safety of individuals during emergencies. The primary objective of an evacuation model is to realistically simulate the process by which a large group of people can reach available exits efficiently. This paper introduces an agent-based evacuation model that represents the environment as a rectangular grid, where individuals, obstacles, and exits interact dynamically. The model employs only five rules to simulate evacuation dynamics while also accounting for complex factors such as movement and stagnation. Different from many evacuation models, this approach includes rules that account for common behaviors exhibited in stressful evacuation situations such as accidents, hysteria, and disorientation. By incorporating these behavioral conditions, the model more accurately reflects the real-life reactions of individuals during evacuation, leading to more realistic and applicable results. To validate the effectiveness of the proposed model, comprehensive experiments and case studies are conducted in diverse urban settings. The results of these experiments demonstrate that the model offers valuable insights into the evacuation process and provides a more precise assessment of its behavior in emergency scenarios.

In recent years, statistical methods have been applied to the study of crime patterns. However, these schemes have several drawbacks that prevent accurate modeling of complex behaviors. Agent-based models (ABM) allow the modeling of human behavior by employing simple rules that consider each agent’s neighborhood. In this paper, a new agent-based model is proposed to emulate crime patterns produced by the interaction of different urban actors, such as offenders (criminals), citizens, and defenders (police officers). Using this approach, the simulation results provide escape trajectories and robbery frequencies that can be used to create or improve public security strategies. Although our scheme can be generically applied, we validated the model by considering different scenarios for the case of Guadalajara, Mexico. Experimental results show that the proposed scheme creates realistic offender behaviors that efficiently predict criminal patterns and provides essential data that allow the creation and improvement of public security strategies to reduce the number of crimes.