Emergency Evacuation Model and Algorithm in the Building with Several Exits

Human behaviour and evacuation time for large underground comprehensive buildings during fire risk process

The development of evacuation models in the last three decades has mainly contributed to the assessment of occupant safety and evacuation procedures in a variety of building designs, under a range of environmental conditions. The effectiveness of such evaluation relies mainly on the models ability to reflect the detailed interactions between the occupant, building design, and environment. The purpose of this study is to present emergency evacuation modeling as a novel approach to layout designs and evacuation procedures. The approach is based on the development of a novel evacuation model that adjusts its outputs to evaluate a range of layout designs. The proposed evacuation model relies on the application of evolutionary computation techniques to assess the means of egress by evolving the location and number of exits needed to ensure occupants safety. The performance of the algorithms varies by occupant behavior. The study suggests that the algorithms have the potential to be implemented in more complex design problems. The study further suggests the need to validate the configurations found by the algorithms by conducting actual evacuation drills.

Passenger pushing behavior during emergency evacuations on roll‐on/roll‐off (Ro‐Ro) passenger ships is a critical yet overlooked factor in evacuation modeling. This study investigates the impact of pushing behavior on evacuation dynamics by employing an improved social force model (SFM) that integrates pushing forces and the ship’s inclination angle. Four evacuation scenarios are simulated to evaluate the impacts of pushing behavior and falling incidents. Results show that (1) moderate pushing can slightly shorten evacuation time without significantly increasing the risk of falling; (2) excessive pushing induces localized congestion, elevates the probability of falls, and ultimately prolongs evacuation time—under severe pushing conditions, total evacuation time increased by 45.4% compared with the no‐pushing baseline; and (3) ship inclination significantly affects passenger stability, particularly near exit bottlenecks and in narrow passages. The findings enhance the realism of evacuation simulations and provide practical insights for optimizing crowd management strategies on Ro‐Ro passenger ships.

Current static evacuation path planning methods are unable to meet the demands of real-time changes in fire scenarios. Therefore, a bi-objective dynamic planning (BODP) method for fire evacuation paths is proposed not only to minimize evacuation time but also reduce the thermal radiation dose received by personnel during the evacuation process. The BODP method includes both an equivalent rule proposed for the first time based on the linear weighting method, and an improved Dijkstra algorithm designed in this paper. Because of them, the BODP method can normalize evacuation time and thermal radiation dose to the same degree and perform multi-sink dynamic planning on a hexagonal grid map, in order to find the optimal evacuation path with the lowest equivalent cost in the event of a tank farm fire. This method accounts for changing fire scenarios and avoids potentially high-risk paths. Finally, a case study of a chemical plant tank farm is conducted to demonstrate the effectiveness of the method. The results indicate that the BODP method is better suited for evacuation path planning in complex and dynamic fire scenarios. The BODP method resolves the issue found in static path planning: the first-degree burn probabilities derived from estimated thermal radiation doses are 34% lower than those derived from actual thermal radiation doses, potentially resulting in unnecessary casualties. Additionally, the BODP method is also applicable to scenarios involving domino effects triggered by fire incidents. This method provides scientific and reliable technical support for emergency evacuation in tank farm fires, greatly contributing to the safety and protection of personnel.

Routes planning is one of the most important aspects in emergency evacuation planning. A multi-objective emergency evacuation model using ant colony optimization was proposed for routes planning in complex multi-exit evacuation environment. The two objectives of the model are to minimize the total evacuation time of all evacuees and to minimize the total path crowding degree respectively. A multi-objective ant colony optimization algorithm was presented to solve the proposed model. The results of the study show that the proposed model can make rapid, efficient and reasonable plans for complex evacuation routing problems.

Evacuation modeling and analysis are concerned primarily with identifying the types of traffic movements associated with a disaster evacuation, as well as the estimation of evacuation and clearance times. Thus, an efficient evacuation planning model is important in determining evacuation times, identifying critical locations in the transportation network, and assessing traffic operations strategies and evacuation policies. In this paper various scenarios, including a hurricane, a toxic chemical leak, dirty bombs, and a nuclear event, are studied to understand the evacuation and highway network effects of the evacuating population. Unlike corridor studies or bottleneck studies found in the literature, a network model with equilibrium assignment is used. The scenarios are tested with a case study of Northern New Jersey, modeled with the North Jersey Regional Transportation Model–Enhanced, a large-scale travel demand model of the region. The results presented in this paper focus on the effect of several assumptions and input data on the evacuation estimates, giving planners an idea of the necessary considerations for evacuation planning with a modeling context. The experience with this study shows that regional planning models are suitable tools to model evacuation; however, the modeler must be careful in their use. Multiple methodologies can be used, and assumptions, such as time of day, notice or no-notice, passengers per car, and background traffic in the network, have wide-ranging effects.

As a highly dense area for passengers, the airport departure hall is particularly characterized by the easy gathering of passengers, irregularity, and difficulty in command, which has become a high incidence of emergencies. For the evacuation problem in case of emergencies, the vast majority of passengers randomly choose the shortest path from the exit to escape, which will lead to congestion at some nodes and prolonging of the evacuation time. Therefore, this study proposes a better evacuation method considering the dynamic change in the spatiotemporal congestion of passengers. First, this paper constructs an evacuation network diagram for the departure hall of the Shenzhen Bao’an International Airport terminal building, simplifies the evacuation process into point-to-point evacuation, and proposes a time-varying network algorithm to optimize the emergency evacuation of the airport. Second, on the premise of obtaining the number and dynamic distribution of passengers in each area in real time, a linear programming model with the shortest evacuation time as the optimization objective is established, and the current optimal evacuation path of passengers is dynamically calculated. Finally, the two evacuation methods, random evacuation and evacuation, based on a time-varying network algorithm are simulated and analyzed by AnyLogic software. The results show that the evacuation scheme based on the time-varying network algorithm can effectively relieve the passenger congestion of some evacuation paths and safety exits. The evacuation time is reduced by 74 s, the evacuation efficiency is improved by 14%, passenger congestion at some evacuation routes and safety exits can be effectively relieved, and the global optimization of the emergency evacuation of passengers is achieved.

The configuration of a room and the evacuee information are important factors that determine evacuation performance. While several studies have investigated the impact of these factors on evacuation behavior, little research has been done on the evacuation of a room with blocked exits and uninformed evacuees who are unaware about the position of open exit. In this paper, we propose an extended floor field model that accounts for these types of conditions and is validated by experiments. The simulation results of our model show that optimal total evacuation time is achieved when more than 40% of evacuees are informed evacuees (IEs) who know the position of the open exit if their initial positions are uniformly distributed in the crowd. Our model indicates that total evacuation time is reduced when IEs are near an open exit. In addition, our model indicates that the ratio of IEs to total evacuees, required for achieving optimal evacuation time, changes with the change in the initial position of IEs.

In this research, we formulate the emergency evacuation and rescue network reconstruction problem as a bi-level programming network design model. The upper level problem is a decision of minimized system cost under limited resources. The lower level problem is one of the constraints for the upper level problem that considers users equilibrium route choice behaviors. We also consider the multi-class users' route choice behaviors in the lower level problem. In solving the problem, the variational inequality sensitivity analysis method, generalized inverse matrix method, diagonalization method, and gradient projection method are adopted to develop the solution algorithms for the bi-level model. The numerical test results indicated that the Stackelberg equilibrium solution of the emergency evacuation and rescue network reconstruction for natural disasters bi-level programming model do exist. More significantly, the optimal network reconstruction planning, disaster victim emergency evacuation and rescue route planning under multi-class user route choice conditions can be achieved.

Strategic Evacuation Network Design (SEND) under cost and time considerations

During mass evacuations, many psychological and physical factors are responsible for stampedes and other life threatening situations. Quantitative and qualitative analyses of these factors are of high importance while devising optimal strategies for evacuations. In this paper, we present an agent-based model that considers psychological and physical factors that cause panic in such situations. We have also simulated some simple evacuation scenarios and presented a method to identify possible bottlenecks and shortcomings in the environments during emergency evacuations. Our method also helps in evaluation and analysis of different evacuation strategies. To enable this analysis, we have used a rule-based roadmap approach, where critical nodes in the environment are identified by the evacuation planner and each node has a special rule according to the strategy of the planner. We evaluate different strategies on parameters, such as evacuation time and physical discomfort caused to the agents.

Incorporating human factors in emergency evacuation – An overview of behavioral factors and models

Rail transit stations with multifloor structures have been built in many cities to intensively utilize land resources and facilitate lives of community. However, being overcrowded with passengers results in high risks during daily operation. In response, this study conducted an emergency evacuation simulation and optimization in the three-dimensional (3D) space of “complex rail transit stations” (CRTSs). The aim of the paper is to provide a methodology to determine effective emergency evacuation strategies for CRTSs. The Lianglukou Rail Transit Station in Chongqing, China, was used as a case study and the AnyLogic simulation platform employed for simulating emergency evacuations. An emergency evacuation theoretical framework was established. The emergency evacuation strategies, including evacuation routes and evacuation times, were determined based on the theoretical demonstration. Simulation and optimization of emergency evacuation in the Lianglukou station were conducted. Accordingly, four main simulation results were obtained: (1) Escalators/stairs and turnstiles are key facilities in the evacuation; (2) Effective guidance for the evacuation is necessary in the public space of the station; (3) Passenger aggregation nodes should be guided for balanced evacuation; (4) Removing metal barriers is a useful evacuation optimization measure. The proposed research method and framework can be used by other CRTSs in the establishment of emergency evacuation strategies and effective optimization strategies to promote safety of transportation system. The research findings are beneficial to passengers in helping them provide valuable emergency evacuation guidance.

Evacuation time prediction method for large cruise ships based on a deep extreme learning machine

Simulating large-scale pedestrian movement using CA and event driven model: Methodology and case study