Crowd Evacuation Research Articles

A high-resolution meshfree particle method for numerical investigation of second-order macroscopic pedestrian flow models

Recent advancements in empirical observations of human psychological responses have revealed that the governing rules of human behavioral aspects can not only be predicted with adequate deterministic precision but also forged into mathematical formulations. These modeling studies enable crisis managers with reliable simulation tools to gain insights into critical facets of crowd disasters and enhance their decision-making abilities to facilitate safe egress in emergency situations. The macroscopic scale of representation in this context is often invoked to comprehend large-scale collective pattern formation in vast built environments, owing to its computational viability. The associated governing equations are typically constructed in a system of hyperbolic conservation law form, and conducting simulations in real-life complex geometric configurations can pose computational challenges. This article puts forward a high-resolution, shock-capturing meshfree particle method in an Eulerian framework for the numerical approximation of several widely adopted macroscopic pedestrian flow models. It serves as a superior alternative to traditional mesh-based methods, eliminating the need for specific mesh structures and connectivity between them. A classical Generalized Finite Difference Method (GFDM) in conjunction with several consistency conditions on spatial derivative coefficients is adopted to obtain a non-oscillatory and fairly conservative Godunov-type discretization of the governing system. The intercell flux is evaluated using a suitable Riemann solver, and a slope-limited reconstruction procedure of the corresponding Riemann states, adapted to arbitrary point distribution, is employed for improved accuracy. Moreover, the preferred direction of human motion is determined from a local density-dependent eikonal-type equation, which is solved using a generalized version of the Fast Marching Method. A thorough numerical investigation of three well-known second-order macroscopic models, namely Payne-Whitham (PW), Aw-Rascle (AR), and Aw-Rascle-Zhang (ARZ) is carried out through two hypothetical scenarios of crowd evacuation, not only to demonstrate the accuracy and applicability of the proposed approach but also to deepen understanding of their distinctive characteristics. A comparison of the flow flux with the fundamental diagram suggests that the density profile obtained with the ARZ model is equivalent to Hughes' first-order model. In contrast, the PW and AR models have proven to effectively replicate complex, unstable pedestrian phenomena such as stop-and-go waves. Finally, Helbing's counter-intuitive idea that strategically positioned barriers upstream of a bottleneck can essentially expedite evacuation is numerically investigated across various parameter choices.

Adaptive Formation by Pedestrian Small Groups During Egresses

Human crowd behavior is an intriguing example of collective behavior where global dynamics emerge from interindividual interactions. Although most studies of human crowds assume that all individuals are independent, the presence and impact of social groups, such as friends or families, have recently been explored. Spatial structures resulting from group behavior and decision-making processes are key aspects of social groups, and how such structures are formed and the impact of these are largely unknown, especially in the scenario of crowd evacuations. Here we experimentally show that in the vicinity of exits, dyads and triads form a representative formation, which would attenuate a disadvantage of small groups that are typically regarded as moving obstacles toward the evacuation dynamics. In laboratory experiments on crowd evacuations regarding small group behavior, we first observed no noticeable differences of egress time between monads and groups, which is contrary to a naïve intuitiveness but in agreement with recent experimental observations. To resolve this contradiction, a detailed analysis focusing on behavior in front of exits suggests that group members constitute a straight-line structure vertical to the exit. Our findings offer a new perspective on how adaptive small group behavior emerges during crowd evacuation.

The study on mechanical model considering optimal self-adaption in the bottleneck area

It aims to solve the problem that the evacuation state of pedestrians depicted by the traditional social force model in a crowded multiexit scenario has a relatively large difference with the actual state, especially the 'optimal path' considered by the self-driving force is the problem of shortest path, and the multiexit evacuation mode depicted by the 'herd behavior' is the local optimum problem. Through in-depth analysis of actual evacuation data of pedestrians and causes of problem, a new crowd evacuation optimization model is established in order to effectively improve the simulation accuracy of crowd evacuation in a multi-exit environment. The model obtains the direction of motion of pedestrians using a field model, fully considers the factors such as exit distance, distribution of pedestrians and regional crowding degree, makes a global optimization for the self-driving force in the social force model using a centralized and distributed network model, and makes a local optimization for it using an elephant herding algorithm, so as to establish a new evacuation optimization method for optimal self-adaption in the bottleneck area. The performance status is compared between the improved social force model and the new model by experiments, and the key factors that affect the new model are analyzed in an in-depth manner. The results show that the new model can optimize the optimal path choice at the early stage of evacuation and improve the evacuation efficiency of pedestrians at the late stage, so as to ensure relatively even distribution of pedestrians at each exit, and also make the simulated evacuation process be more real; and the improvement in overall evacuation efficiency is greater when the number of pedestrians to be evacuated is larger. Therefore, the new model provides a method to solve the phenomenon of disorder in overall pedestrian evacuation due to excessive crowd density during the process of multi-exit evacuation.

Modeling heterogenous crowd evacuation on stairs in high-rise buildings using a fine discrete floor field cellular automaton model: Accounting for speed and boundary layer variations

The article investigates the impact of crowd heterogeneities on stair evacuation dynamics in high-rise building. Using a fine discrete floor field cellular automaton (FFCA) model, the study explores the often-overlooked influence of crowd variations in mobility and behaviors. The simulation, set in a 21-story staircase, incorporates variations in speeds and boundary layer widths to replicate the heterogenous crowd dynamics. Key findings reveal that the heterogenous group, considering speed variations, exhibit prolonged stair evacuation time, with children and elderly individuals finishing last. Interactions among individuals with different mobilities lead to a 24.1% blocking effect on young adults' evacuation, while young adults enhance the evacuation of children and elderly by 19.0% and 23.8%, respectively. Children on the inner side of stairs lag behind due to the boundary layer, and the entire heterogeneous crowd's evacuation time decreases by 7.0%, highlighting the efficiency boost from boundary layers. A robust linear correlation is identified between specific outflow and the average desired speed for the heterogeneous group, with a consistent density of 0.46ped/m2 near the exit. The proposed model's validity is supported by trajectories consistent with prior observations and empirical data from homogenous crowd evacuation. The study advances our understanding of evacuation behaviors in heterogenous crowds within high-rise buildings during emergencies.

A Coupled SFM-ASCRIBE Model To Investigate the Influence of Emotions and Collective Behavior in Homogeneous and Heterogeneous Crowds

The understanding of crowd behavior dynamics holds immense significance in ensuring public safety across a range of situations, including emergency evacuations and large-scale events. Our research focuses on two primary objectives: investigating the impact of emotions on crowd movement and gaining valuable insights into collective behavior within crowds. To achieve this, we present a coupled model, incorporating an enhanced ASCRIBE model with an agent displacement model. We introduce heterogeneity into our model by incorporating specific mobility laws for different categories of panicked crowds, considering the influence of emotions on both speed and direction. Through numerical simulations, we analyze the model's parameters, observe the behavior of uniform crowds, and explore the collective dynamics within diverse crowds. By conducting comprehensive simulations and analyses, the findings from this study can contribute to the development of more effective crowd management strategies and emergency evacuation protocols.

An enhanced multi-attribute crowd evacuation model incorporating the effects of environmental impact factors

We propose an enhanced floor field model (FFM) to analyze the behavioral characteristics of crowds with varying attributes proportions during evacuation. This model governs pedestrian movement through the Dynamic Floor Field (DFF) and the Static Floor Field (SFF). The DFF takes into account individual factors such as the gender, familiarity with the environment, and social relationships of evacuees, which influence safe evacuation. Concurrently, the SFF encapsulates the impact of environmental factors like obstacles, exits, and guidance effects. Subsequently, this refined FFM was applied and validated using a sports center evacuation scenario. The results demonstrated that the enhanced FFM accurately replicated evacuees' asymmetric behavior and queuing, and aligned well with other models when the number of evacuees fluctuated over time. In the absence of guidance, both environmental familiarity and gender emerged as primary factors influencing partial evacuation. Additionally, the gender of pedestrians significantly affected the overall evacuation. Notably, compared to pre-existing environmental information available to evacuees, the implementation of guidance to augment pedestrians' environmental familiarity resulted in a more efficient evacuation. The FFM model and these findings could be instrumental in simulating personnel evacuation and formulating emergency management strategies in crowded areas.

A model for aircraft cabin evacuation considering passenger type

Pedestrian safety evacuation in aircraft cabins has been a challenging problem because of the aircraft's unique characteristics, such as the diversity of passengers and the restricted evacuation environment. It is difficult to reproduce evacuation activities in aircraft cabin due to safety concerns and cost constraints. To fill this gap, an improved cellular automaton model of crowd evacuation for aircraft cabin is established by incorporating the characteristics of cabin space structures and passenger attributes. Passengers are divided into healthy individual passengers and disabled-healthy group passengers, whose movement mechanisms are quantified. Based on the constructed model, simulation experiments are conducted using the configuration cabin layout of B737-800 as an example. The results show that the evacuation time is prolonged with increased passenger density and the number of disabled passengers. Moreover, the overall evacuation time is insignificantly affected by whether disabled-healthy group passengers' seats are close to the aisle or window, and the evacuation efficiency is best when their seats are evenly distributed in the cabin. The evacuation time is the shortest when all cabin doors are open, and pedestrians are evacuated the slowest when the central emergency doors are closed. This study provides valuable insights into effective strategies for pedestrian evacuation and crowd emergency management of civil aircraft.

Crowd evacuation with human-level intelligence via neuro-symbolic approach

Understanding human response to crowd emergencies is extremely complex, and it plays a significant role in engineering construction designs and crowd safety. Individual choices, reasoning, and behaviours cannot be fully described by equations or rule-based methods. Accordingly, this research proposes a neuro-symbolic approach for modelling agents with human-level capabilities of reasoning and performance in an emergency evacuation. The proposed neuro-symbolic approach combines deep reinforcement learning with evaluative fuzzy logic to address the challenges of large amounts of required data, time, and trials-and-errors for policy optimization and to handle the assumption of reward function that may not be practical in real scenarios. This neuro-symbolic model has the potential to deal with the complexity of the environment and decision-making process via deep reinforcement learning and enhances the cognitive and visual intelligence via an evaluative fuzzy function, which continuously evaluates agent actions during the training process to boost pedestrian active response to their surroundings, with full awareness of time, thereby, the human-level capacity of reasoning. Moreover, this proposed model optimizes the computational demands of deep reinforcement learning and enables faster learning of new situations. The findings indicate that the proposed model can produce behavioural patterns that align with real observations of crowd evacuation, such as laminar flow, stop-and-go flow, and crowd turbulence. On top of that, a new evacuation behaviour is observed, as some pedestrians avoid congestion at the exit until the density reduces which reflects a level of human reasoning. The proposed model illustrates a higher accuracy and much faster converge than the pure proximal policy optimization model with substantially minimal training timesteps of as little percentage as 2 to 8. Meanwhile, the reliability study records an increase of the mean and standard deviation of evacuation time from 39.7 s, 1.06 to 155.09 s, 7.39 as crowd size increases from 15 to 200 pedestrians, which implies a rise of uncertainty. Therefore, we perceive that this work can provide crowd authorities and construction engineers with insights into complex behaviour and critical conditions to make better evacuation plans and sustainable designs to ensure crowd safety. It also provides a promising alternative to the evident lack of data on critical crowd conditions.

Experimental study on the movement characteristics of adversarial people in the corridor

With the continuous development of pedestrian dynamics, the movement characteristics of multiple types of people (elderly, children, disabled people, etc.) under various scenarios (flood, fire, special building structure, etc.) have been extensively and deeply studied. The relevant conclusions and movement rules are of great significance for guiding the design of building structures and crowd evacuation instructions. However, there is a lack of research on the movement of adversarial crowds with different movement purposes and strong interactions. The analysis of its movement characteristics and the movement modelling of interaction rules need to be further explored through controlled experiments. Therefore, this paper focuses on the study of the movement characteristics of the adversarial crowd and carries out a realistic experiment in which pedestrians in the corridor escape from attackers to complete the evacuation. The purpose was to address the following questions: the impact of attackers and corridor width on pedestrian movement. It was found that the attacker's movement pause would increase the desired velocity of the crowd, and thus the velocity-density relationship was different from the traditional fundamental diagram. The width of the corridor has a great impact on the movement speed and the collision probability of the crowd. Based on the results of the experiment, a model was established and simulation experiments were carried out by changing the width of the corridor. It is hoped that the results of this paper can provide help for emergency evacuation guidance for pedestrians in the future.

Research on emergency evacuation behavior rules of pedestrians under fire and explosion accidents in chemical parks considering social relationships

Chemical parks present a high risk of sudden accidents such as fires and explosions, given the significant number of hazardous sources present. This is why emergency evacuation after accidents is so essential. The behavior of crowds during evacuation is complex, and social relationships among pedestrians can have differentiated effects on the evacuation process. However, most of the current crowd evacuation research has focused on life scenarios and natural disaster contexts. Less attention has been paid to the emergency evacuation of crowds affected by social relations in the context of chemical accident disasters. A comprehensive evacuation model that considers the dynamic evolution of crowd transfer both within and outside the factory area is proposed in this paper. Using the Anylogic software based on the social force model, this study constructs emergency evacuation scenarios for crowd in the context of fire and explosion accidents, and toxic gas leakage incidents. Specifically, this study analyzes the impact of three factors, namely inter-personal distance, group size, and proportion of different group sizes in the total population, on the evacuation efficiency. It has been found that when large groups of pedestrians are split into smaller ones for evacuation, the evacuation efficiency can be effectively improved. The study includes calculations of the lethal range of a toxic gas and simulated the process of relocating large crowds of people to nearby shelters. In addition, a strategy for deploying pedestrian groups based on social relationships was developed for the context of mass evacuation. These measures have the potential to optimize the evacuation efficiency of pedestrian groups in chemical parks, including expanding and modifying shelters, diverting dense crowds from residential plots, using interpersonal spacing of 0.1 m, and splitting large groups of pedestrians into smaller ones. The research results can provide a reference for decision making for large-scale group evacuation in chemical parks, and improve the emergency evacuation capability and local risk response capability in chemical parks.

A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

An Unmanned System-Guided Crowd Evacuation Method in Complex and Large-Scale Evacuation Environments

An Unmanned System-Guided Crowd Evacuation Method in Complex and Large-Scale Evacuation Environments

Simulation of Crowd Evacuation in Asymmetrical Exit Layout based on Improved Dynamic Parameters Model

Simulation of Crowd Evacuation in Asymmetrical Exit Layout based on Improved Dynamic Parameters Model

包含不同类型残疾人群的混合人群疏散实验与模拟研究

包含不同类型残疾人群的混合人群疏散实验与模拟研究

Multirobot-Guided Crowd Evacuation: Two-Scale Modeling and Control

Multirobot-Guided Crowd Evacuation: Two-Scale Modeling and Control

Simulation of emergency evacuation with crowd-gathering risk and evaluation methods: a case study of the mass rapid transit system in Taipei

ABSTRACT Mass rapid transit (MRT) systems are the backbone of transportation infrastructure in many cities. During emergencies, indoor subway stations become particularly susceptible to panic and overcrowding, making evacuation difficult. Therefore, simulating the evacuation and sheltering processes of a large number of individuals during emergencies is key in urban disaster response. In this study, a spatial evacuation risk model was used to enhance the safety of evacuation strategies in an MRT system. Data were collected on spatial architectural floor plans, and a database for communication and pedestrian flow was established. The behavioral patterns and distribution of individuals within stations and the characteristics of refuge spaces were also investigated. To simulate the processes of evacuation and sheltering, the Pathfinder evacuation simulation tool was used to construct a three-dimensional spatial model of a subway station. Specific scenarios were then constructed to simulate the procedures of evacuation and sheltering. Subsequently, the factors contributing to overcrowding-related hazards were analyzed to examine the spatial risks associated with crowd aggregation and evacuation and provide recommendations for improvement. Given its numerous advantages, this approach can serve as a valuable reference for conducting risk assessments in other large-scale crowd aggregation spaces.

Exploring the Impacts of Exit Structures on Evacuation Efficiency

In the context of a fire emergency, safe and efficient exits are of paramount importance for pedestrian evacuation. The recent rapid development in the construction industry has rendered exit structures more diverse and complex. However, little attention has been paid to the influence of exit structures on the efficiency of crowd evacuation processes. In this paper, a tentative experiment was designed to preliminarily reveal the effects of five exit structures (Exit 1, Exit 2, Exit 4, Exit 5, and Exit 3 as examples for comparison) on crowd evacuation. Exit 1 has door leaves opening outward. Exit 2 has door leaves opening inward. Exit 3 has no leaves. Exit 4 consists of double-layer exit doors with the doors opening outward. Exit 5 comprises double-layer exit doors with the doors opening both sides outwards Subsequently, according to the properties of this experiment, a social force-based simulation model was established using the AnyLogic software 8.8.4. By changing the exit width and the crowd density, data such as evacuation time, flow rate, crowd density, and time delay were investigated in detail. The results revealed a notable variation in the evacuation efficiency depending on the deign of the exit. The respective flow rates for Exits 1, 2, 3, 4, and 5 were 0.66 people/(m·s), 0.77 people/(m·s), 0.80 people/(m·s), 0.71 people/(m·s), and 0.66 people/(m·s). Although Exit 3 excelled in terms of evacuation efficiency, it is not directly applicable to real architectural structures. Therefore, Exit 2 emerged as a highly promising solution in terms of flow rate and population control in the exit area, underscoring the effectiveness and practicality of its structural design. It is prospective that the results of this study can offer engineering and technical professionals valuable references and guidance concerning the design of exit structures.

Heterogeneous decision-making dynamics of threshold-switching agents on complex networks.

In the classical two-player decision-making scenario, individuals may have different tendencies to take a certain action, given that there exists a sufficient number of neighbors adopting a particular option. This is ubiquitous in many real-life contexts including traffic congestion, crowd evacuation, and minimal vertex cover problem. Under best-response dynamics, we investigate the decision-making behaviors of heterogeneous agents on complex networks. Results of the networked games are twofold: for networks of uniform degree distribution (e.g., the lattice) and fraction of the strategy is of a linear function of the threshold setting. Moreover, the equilibrium analysis is provided and the relationship between the equilibrium dynamics and the change of the threshold value is given quantitatively. Next, if the games are played on networks with non-uniform degree distribution (e.g., random regular and scale-free networks), influence of the threshold-switching will be weakened. Robust experiments indicate that it is not the value of the average degree, but the degree distribution that influences how the strategy evolves affected by the threshold settings. Our result shows that the decision-making behaviors can be effectively manipulated by tuning the parameters in the utility function (i.e., thresholds) of some agents for more regular network structures.

Public Place Crowd Transaction Monitoring System

Currently, the phenomenon of abnormal movement in public spaces by groups is becoming increasingly prominent, leading to issues concerning public flow and safety. The escalating problems of high crowd density, the presence of controlled dangerous items, and unexpected group activities highlight the necessity for timely detection in public settings. Timely identification of such scenarios will facilitate prompt responses and assistance from relevant government departments. Exploring how artificial intelligence technology can aid urban management personnel in effectively detecting abnormal group behaviors is crucial. Having the ability to swiftly and efficiently evacuate crowds in emergency situations holds significant practical importance. This paper employs deep learning methodologies to assist urban management personnel in efficiently monitoring crowd density and detecting abnormal behaviors. The aim is to maintain crowd density within reasonable limits and enable rapid and effective crowd evacuation in emergency situations. Detection of abnormal group behaviors typically involves methods based on global features, extracting feature patterns like optical flow from entire video segments and constructing corresponding histograms. Given that automatic classification of crowd patterns involves sudden and abnormal changes, a novel method is proposed to extract motion "textures" from dynamic STV (Space-Time Volume) blocks formed from real-time video streams.

Simulation of Crowd Evacuation Behaviours at Subway Stations under Panic Emotion

Simulation of Crowd Evacuation Behaviours at Subway Stations under Panic Emotion