Disasters can happen anytime in urban areas, forcing people to evacuate buildings to safeguard their lives. Therefore, it is important that safe routes for the transit of people are first identified. However, when defining evacuation routes to safer places, the basic condition considered is the shortest distance, excluding other criteria related to the security of environmental elements. We propose the simulation of feasible evacuation routes under the influence of importance indexes obtained from the validation and consistency of the security criteria of building elements performed by the analytical hierarchy process (AHP). We find that the security criteria of building elements can be classified into ranges of a suitability model and validated in comparison matrices with consistency ratios (CR) < 10%, which guarantee data consistency. Furthermore, we obtain the importance indices S(n) interpreted by a variant of the A-star (A*) pathfinding algorithm, showing evacuation routes traced through safe areas, when performing simulations. Our results demonstrate the importance of the consistency of security criteria employed in the AHP, whose indexes highly influence the execution of the variant of the A* pathfinding algorithm when it determines safer rather than shorter evacuation routes, for different simulation scenarios.

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Different types of fire accidents in the urban area of Seoul, Korea are continuously occurring, causing risk and damage to property and life. In this study, we analyze various spatial and nonspatial fire risk factors by applying machine learning techniques to predict their level of importance in future events. We use the data on fire accident for three years (2017-2019) published by the Korean Fire Service and the Seoul Metropolitan Government. Regarding the machine learning techniques, we use support vector machine (SVM), random forest (RF), and gradient boosted regression tree (GBRT). As the first phase, a multiple regression analysis is performed to select seven main factors related to fire occurrence. In the second phase, we calculate the mean absolute error (MAE) and root mean squared error (RMSE) using validation and test data for the machine learning techniques, revealing that RF obtains ideal results. In the third phase, we analyze the importance of the seven fire factors using RF, resulting in the ignition condition (produced by electrical, mechanical, and chemical reasons) being the main factor in fire occurrence. This study is expected to be used as an important guideline to define urban fire reduction and management measures in Seoul, the capital of South Korea.

Evacuation plans in buildings where people perform activities must be clearly defined. Children's facilities are a special case in which indoor navigation must be traced by safe routes. However, usually, the routes follow the shortest path. We propose the calculation of safer evacuation routes inside a multi-agent kindergarten environment using the angle propagation theta*-multilayer vulnerability analysis (AP-Theta*-MVA) algorithm, a novel variant of the angle propagation theta* (AP-Theta*) pathfinding technique. In this variant, we perform the multilayer vulnerability analysis (MVA) of geometric objects based on international standards to obtain importance indexes (Sn). In addition, we include rules of the reciprocal n-body collision avoidance approach (ORCA) and the conditioning variables of the location of the hazard, the number of people, and their speed of movement and reaction ability. We apply the algorithm in different scenarios of evacuation due to fire smoke propagation within a children's facility. Our results show that for each scenario, AP-Theta*-MVA provides orders through signals obtained by supervised learning to the multi-agent system to react and move away from dangerous areas. Thus, we achieve safer evacuation patterns and routes for a multi-agent system. This demonstrates the suitability of the AP-Theta*-MVA algorithm, which is influenced by the MVA, for children's facilities when it is performed in a multi-agent system, enabling the calculation of safe and feasible evacuation routes with realistic times to improve evacuation plans.