Crowd Evacuation Modeling and Simulation Using Care HPS

Emergency signage systems provide effective route guidance and evacuation information for pedestrians in case of emergencies such as fire, blackout, and explosion. This paper proposes a modified social force (SF) model to investigate crowd evacuation dynamics taking into account the influence of emergency signs. The perceiving probability model is formulated for the quantitative description of the probability that pedestrians can successfully notice the sign and clearly perceive the guidance information. Simulation experiments and controlled experiments are designed to calibrate the parameters of the proposed model. The effectiveness of the modified SF model is preliminarily verified by comparing the simulation results with experimental data and/or empirical results such as fundamental diagrams and self-organization phenomena. A case study of crowd evacuation simulations at a typical Beijing subway station is conducted to evaluate evacuation performance of three signage distribution schemes, i.e., Maximal Covering (MaxCover), Uniform, and Random, which are proposed by the maximal covering location and empirical approaches, as well as contrasted scheme without emergency signs. The effects of the quantity and distribution of emergency signs on crowd evacuation efficiency are studied quantitatively by simulations. The results show that installing emergency signs can improve evacuation efficiency no matter what distribution scheme is adopted. By choosing an appropriate distribution scheme i.e., MaxCover, the evacuation performance can be further improved and the evacuation time can be significantly reduced.

With the continuous expansion of the city scale and urbanization, urban road networks are becoming increasingly complex. Moreover, severe and extreme weather events, earthquakes, and other natural disasters occur frequently. Therefore, how to effectively and quickly evacuate urban crowd in dynamic environments is an urgent issue. To carry out the above objective, an unmanned system-guided crowd evacuation method is proposed in the current study. In the proposed method, the robot can perceive the environment in a timely and accurate manner to generate the evacuation map via advanced information technologies such as the Internet of Things or urban brain. Subsequently, an improved elliptic tangent graph approach based on global and local information (ETG-GLI) is utilized to plan a feasible and short evacuation path in large-scale scenarios. Finally, a novel crowd evacuation model based on the social force model is proposed to simulate the actual crowd evacuation process in complex and large-scale environments. To test the performance of the proposed path planning method, 25 different scenarios are proposed to simulate complex urban crowd evacuation environments. The experimental results show that the proposed algorithm outperforms other competitors in terms of path planning ability and computational time. Three actual evacuation cases with 324 pedestrians are modeled to further test the performance of the proposed algorithm. The simulation results demonstrate that the unmanned system-guided crowd evacuation method can find a shorter evacuation path for reducing the evacuation time in three complex and large-scale environments when compared with three other methods. Therefore, the proposed algorithm is a highly effective and promising approach to provide useful decision support and guidance for actual urban planning and urban emergence management. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In modern cities, the population density is high and the road network is complex. To evacuate the crowd in a timely and safe manner, planning feasible and short paths in large-scale and complex environments is a critical and challenging task. Therefore, the present study aims to provide a novel method to plan high-quality evacuation routes to guide the pedestrian flow. The performance of the proposed approach is validated in 25 test scenarios and 3 real-world instances. Experimental results demonstrate that the proposed algorithm performs well in terms of path length and computation time. Moreover, the proposed crowd evacuation model can simulate the actual process of crowd evacuation.

Modified social force model considering emotional contagion for crowd evacuation simulation

Crowd simulation has its greatest utility in the study of safety measures for crowded events. However, total evacuation time, which is the most important feature in crowd evacuation, can vary depending on the population, the environment, the adopted strategies to decide routes, but also because people can be panicked or not. This paper presents a model to parametrize crowd simulation allowing to increase or decrease the agents stress. We use BioCrowds model and proposed an extension to consider new parameters to deal with crowd relaxing and compression, as comfort and stress. These two new parameters impact the will to go to the goal and the individual panic in trying to save itself. Indeed, our model could be integrated in other crowd simulators. This work discusses some obtained results and also presents a case study regarding a real scenario. We simulate the Hillsborough Disaster happened in 1989 in order to discuss the reliability of our method. Results indicate that our method can simulate in a coherent way the densities observed in the real life event.

The combination of multi-agent technology and reinforcement learning methods has been recognized as an effective way which is used in path planning-based crowd simulation. However, the existing solution is still not satisfactory due to the problem in the mutual influence of agents. Therefore, an improved multi-agent reinforcement learning method (IMARL algorithm) is introduced. In this method, the intersection of the pedestrian trajectory extracted from the real video is first used as the state space for reinforcement learning. The crowd is grouped and the leader is selected. A bulletin board is added to the reinforcement learning algorithm of multi-agent to store the empirical knowledge of the learning process, and the navigation agent passes information between the leader and the bulletin board. The original social force model was improved, and the cohesive force of visual factors was added to the force formula. The IMARL algorithm is combined with the improved social force model for crowd evacuation simulation. Using a two-layer control mechanism, the leader in the upper layer uses the decision process based on the IMARL algorithm to select the path, and the individuals in the bottom group use the improved social force model to evacuate. The method of this paper not only solves the dimensionality disaster problem of reinforcement learning but also improves the convergence speed. The evacuation efficiency is effectively improved in crowd evacuation simulation experiments. In addition, it can also provide specific guidance scheme for crowd evacuation improvement and assistant decision support for the prevention and management of large-scale group trampling incidents.

The crowd evacuation simulation is essential to provide important results for occupants, especially in the large capacity building compared to the human fire drill exercise. The strategy of evacuation such as the use of obstacles may need to be adapted by many organizations as an aid to help in visualizing and estimating the evacuation time during an emergency. During certain crowd events, they may consider the various setting of the object to ensure smoothness and effective crowd evacuation flow. In this paper, it aims to provide the simulation with 100-1000 agents and testing with obstacle using Anylogic tool and analysis of evacuation time validated using SPSS. The results show that the placement of obstacles near the exit way indeed can reduce the evacuation time and complies with the anti-arching phenomenon during evacuation.

Existing crowd evacuation simulation methods commonly face challenges of low efficiency in path planning and insufficient realism in pedestrian movement during the evacuation process. In this study, we propose a novel crowd evacuation path planning approach based on the learning curve–deep deterministic policy gradient (LC‐DDPG) algorithm. The algorithm incorporates dynamic experience pool and a priority experience sampling strategy, enhancing convergence speed and achieving higher average rewards, thus efficiently enabling global path planning. Building upon this foundation, we introduce a double‐layer method for crowd evacuation using deep reinforcement learning. Specifically, within each group, individuals are categorized into leaders and followers. At the top layer, we employ the LC‐DDPG algorithm to perform global path planning for the leaders. Simultaneously, at the bottom layer, an enhanced social force model guides the followers to avoid obstacles and follow the leaders during evacuation. We implemented a crowd evacuation simulation platform. Experimental results show that our proposed method has high path planning efficiency and can generate more realistic pedestrian trajectories in different scenarios and crowd sizes.

PurposeIt has been witnessed that many incidents of crowd evacuation have resulted in catastrophic results, claiming lives of hundreds of people. Most of these incidents were a result of localized herding that eventually turned into global panic. Many crowd evacuation models have been proposed with different aspects of interests. The purpose of this paper is to attempt to bring together many of these aspects to study evacuation dynamics.Design/methodology/approachThe proposed agent-based model, in a hypothetical physical environment, uses perception maps for routing decisions which are constructed from agents’ personal observations of the surroundings as well as information gathered through distant communication. Communication is governed by a trust model which measures the authenticity of the information being shared. Agents are of two types; emotional and rational. The trust model is combined with a game-theoretic model to resolve conflict of agents’ own type with that of types of agents in the neighborhood.FindingsEvacuation dynamics in different environmental and exit strategies are evaluated on the basis of reduced herding and evacuation time. Using this integrated information sharing model, agents gain an overall view of the environment, sufficient to select the optimal path towards exits with respect to reduced herding and evacuation time.Originality/valueThe proposed model has been formulated and established using an agent-based simulation integrating important modeling aspects. The paper helps in understanding the interplay between technological and humanistic aspects in smart and pervasive environments.

As the existing crowd evacuation model seldom considers the influence of crowd confusion degree on the evacuation results. This paper proposes evacuation entropy particle swarms optimization (E-PSO) crowd evacuation model. This model considers the impact of obstacles and the surrounding people on evacuees' evacuation behavior in the evacuation process. And the concept of evacuation entropy is introduced to quantitatively describe the degree of chaos in crowd movement and is used to correct the individual speed direction in the model. The model further considers the influence of individual panic psychology and the presence of leaders in evacuation. The simulation results show that the panic of evacuees will aggravate the degree of chaos on the event, but the addition of leaders will reduce the degree of confusion and improve the evacuation efficiency. At the same time, compared with the traditional PSO model, the modified PSO model with evacuation entropy can reduce the degree of confusion on the evacuation process, effectively avoid the occurrence of crowded stampede accident and improve the evacuation efficiency.

Cellular Automata (CA) as bio-inspired parallel computational models of self-reproducing organisms can capture the essential features of systems where global behavior arises from the collective effect of simple components which interact locally. In this aspect, CAs have been considered as a fine candidate to model pedestrian behavior and crowd dynamics in a fine manner. In specific, for crowd modeling, the CA models show evidence of a macroscopic nature with microscopic extensions, i.e. they provide adequate details in the description of human behavior and interaction, whilst they retain the computational cost at low levels. In this paper several CA models for crowd evacuation taking into consideration different modeling principles, like potential fields techniques, obstacle avoidance, follow-the-leader principles, grouping theory, etc. will be presented in an attempt to accomplish efficient crowd evacuation simulation. Moreover, an integrated system based on CAs that operates as an anticipative crowd management tool in cases of medium density crowd evacuation for indoor and outdoor environments is also shown, and its results different real world cases and different environments prove its efficiency. Finally, robot guided evacuation with the help of CAs is also presented. Quite recently, an evacuation system was proposed, based on an accurate CA model capable of assessing the human behavior during emergency situations takes advantage of the simulation output to provide sufficient information to a mobile robotic guide, which in turn guides people towards a less congestive exit at a time.

The Whale-Bat Chaotic Algorithm (WOABCM) is a revolutionary tool for optimizing pathfinding navigation in crowd evacuation scenarios. Conventional evacuation plans rely on static routes, causing traffic jams and decreased safety. WOABCM uses the erratic behavior of whales and bats to optimize escape routes in real time, making it particularly useful in large crowd evacuations. The algorithm can adapt to different scenarios, ensuring its continued efficacy across real-world conditions. Iteratively iterating through the evacuation environment, WOABCM iteratively finds the most effective ways for agents to reach exits. In this experiment, the results show notable improvements in evacuation times, with WOABCM determining and directing agents through the shortest and least crowded paths in a room setting and assigning agents to exits in more complicated scenarios. The algorithm's flexibility also allows it to swiftly recompute pathways for agents, avoiding obstructions or congestion points. This groundbreaking approach to crowd evacuation simulation demonstrates how chaos theory-inspired algorithms can be used to solve practical problems

As the security accidents in public places frequently emerge, the research based on crowd evacuation gets more and more people’s attention. Now, the crowd evacuation research has shifted from the traditional live exercise to computer simulation. This paper chose five kinds of crowd evacuation simulation platform and summarized Cellular Automata, Agent-based model, network model they involved. Then, the thesis introduced the software, analyzed the performance of them and stated the respective advantages and disadvantage in order to help user choose proper platform to achieve fast and efficient results of crowd evacuation simulation.

The Metro is a significant part of a transit system as it provides service to millions of people every day. During peak hours, rail transit hub stations are packed with commuters; therefore it is extremely important to evacuate all occupants safely and rapidly when a catastrophe occurs. In this study, we focused on a typical urban rail transit hub in Shanghai: the Caobao Road Metro Station. Spatial data and population parameters are collected to set up a geometric model. We conducted dozens of evacuation simulations for different scenarios and established a ranking standard to assess the level of service of a station. The analysis combines evacuation time, pedestrian density, exit efficiency, and potential risks. The results show that the hub has an imbalance on efficiency of exits. This research can help understand the potential dangers and provide scientific suggestions for metro administration and supervision authorities.

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

Development of an evacuation model considering the impact of stress variation on evacuees under fire emergency