Modified social force model considering emotional contagion for crowd evacuation simulation

The competitions of pedestrians evacuating from indoor easily cause congestion, which reduces efficiency of evacuation. By applying Game theory in dynamics of pedestrian evacuation process, the appropriate ratio of competitive pedestrians helps to improve the efficiency of evacuation. Based on the indoor pedestrian evacuation micro-model, we carry out the simulation for pedestrians evacuating from indoor. The results indicate that the evacuation time is greatly impacted by the ratio of the competitive pedestrians. The efficiency of indoor pedestrian evacuation is enhanced by optimizing the ratio of competitive pedestrians.

Pedestrian evacuation dynamics considering information guidance

Modeling the dynamics of pedestrian evacuation in a complex environment

The emergency sign system provides evacuation information and route guidance for people and plays an important role in crowd evacuation in case of emergency. This paper investigates crowd dynamics with emergency signs and mainly focuses on the influence of signs on crowd evacuation dynamics under the visual field-limited environment. A modified social force model is proposed by taking into account the evacuation guidance of signs and interaction force between the pedestrians and signs. The effects of quantity and locations of emergency signs and self-organization behavior on evacuation efficiency are explored by simulations. According to the simulation results, the crowd evacuation with emergency signs shows an orderly evacuation process and the evacuation time is reduced significantly compared with that without signs in a smoke-filled room scenario. Moreover, emergency signs with appropriate locations and quantity are more conducive to crowd evacuation. Besides, the herding behavior can also improve the crowd evacuation efficiency.

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.

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.

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.

<p>The dissertation explores the influence of emerging technologies on the dynamics of urban areas from a pedestrian-oriented perspective. To evaluate this influence, novel data sources and modern data-driven approaches replace traditional data collection methods and modelling techniques. Part of the dissertation establishes new tools and techniques to predict and explain pedestrian behaviour that concerns the future dynamics in urban areas. Specifically, this dissertation considers: (i) what are the novel data sources that can capture detailed information on pedestrian behaviour and how can they be used efficiently? (ii) how to extract valuable information from these new high- dimensional data sources? (iii) how to utilize the data and tools to predict pedestrian behaviour in the future urban environment? and (iv) how to interpret and explain pedestrian behaviour in the context of smart cities?</p> <p>This thesis is based on four articles introduced in Chapters 3 to 6. Chapter 3 introduces a semi-supervised residual network for transportation mode detection using passively collected labelled and unlabelled Wi-Fi signal data. Chapter 4 provides a survival analysis to model the wait time behaviour of a crossing pedestrian and analyze the effect of smartphone distraction on pedestrian behaviour. Virtual reality is used in this chapter as a means of data collection in a controlled environment. Chapter 5 highlights pedestrian crossing behaviour in the presence of automated vehicles. A large virtual reality data collection campaign is conducted to understand pedestrian behaviour in futuristic scenarios. Data-driven survival analysis is developed to analyze pedestrian wait time before mid-block unsignalized crossings. By using a post-hoc model interpretation, the contributing factors to pedestrian wait time are assessed. In Chapter 6, a neural network architecture is developed to incorporate sequential time-series data and contextual information to predict pedestrian trajectory. The proposed framework is applied to the virtual reality dataset.</p> <p>Methodological and data collection frameworks explored in this dissertation provide solutions for detecting, modelling and predicting pedestrian behaviour in a futuristic context. This dissertation con- tributes to the field of transportation by proposing alternative data collection methods, developing novel data-driven methodologies and analyzing pedestrian behaviour in the context of automated vehicles.</p>

The unique single-entry, multi-exit layout of theme parks, low visitor familiarity with the environment, and small-group travel patterns make information signage and group information transmission crucial for emergency evacuations. Existing research has insufficiently examined the relationship between dynamic information dissemination and environmental chaos in large outdoor settings, the interplay between signage location and group information sharing, and the time delay in path reconstruction caused by visitor entrance dependence. This research constructs a small-group cellular automaton model that considers the synergistic effects of information transmission and signage. The small-group information transmission mechanism is refined into a two-layer model of "internal complete sharing with external trust dependence," and chaos entropy is introduced to quantify the environmental constraints on information dissemination. The research shows that group trust has an inverted U-shaped effect on evacuation efficiency, with an optimal range of 0.4-0.6. Spatial optimization of signage placement is superior to stacking signage, and a centrally clustered layout can significantly reduce costs while maintaining efficient evacuation. The presence of a geometrically central emergency exit can improve evacuation efficiency by 40%-60%. This improvement magnitude is associated with the degree of matching among site scale, exit capacity, and the capacity of connecting passages. This research emphasizes the collaborative improvement of evacuation efficiency and safety through information regulation, spatial optimization, and network resilience design, providing a behavioral basis for the safety design of large-scale theme parks.

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.

Emergency exercises are an efficient approach for preventing serious damage and harm, including loss of life and property and a wide range of adverse social effects, during various public emergencies. Among various factors affecting the value of emergency exercises, including their design, development, conduct, evaluation, and improvement planning, this paper emphasizes the focal role of evacuees and their behavior. We address two concerns: What are the intrinsic reasons behind human behavior? How do we model and exhibit human behavior? We review studies investigating the mechanisms of psychological behavior and crowd evacuation animation. A comprehensive analysis of logical patterns of behavior and crowd evacuation is presented first. The interactive effects of information (objective and subjective), psychology (panic, small groups, and conflicting roles), and six kinds of behavior contribute to a more effective understanding of an emergency scene and assist in making scientific decisions. Based on these studies, a wide range of perspectives on crowd formation and evacuation animation models is summarized. Collision avoidance is underlined as a special topic. Finally, this paper highlights some of the technical challenges and key questions to be addressed by future developments in this rapidly developing field.

Effect of uncertain information on pedestrian dynamics under adverse sight conditions

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.

The modeling of a new generation of agent-based simulation systems supporting pedestrian and crowd management taking into account affective states represents a new research frontier. Pedestrian behaviour involves human perception processes, based on subjective and psychological aspects. Following the concept of pedestrian environmental awareness, each walker adapts his/her crossing behaviour according to environmental conditions and his/her perception of safety. Different pedestrian behaviours can be related to subjective mobility and readiness to respond, and these factors are strongly dependent on the subjective interaction with the environment. Having additional inputs about pedestrian behaviour related to their perception processes could be useful in order to develop a more representative pedestrian dynamic model. In particular, the subjective perception of the safeness of crossing should be taken into consideration. In order to focus on the pedestrians’ perception of safe road crossing and walking, an experiment in an uncontrolled urban scenario has been carried out. Besides more conventional self-assessment questionnaires, physiological responses have been considered to evaluate the affective state of pedestrians during the interaction with the urban environment. Results from the analysis of the collected data show that physiological responses are reliable indicators of safety perception while road crossing and interacting with real urban environment, suggesting the design of agent-based models for pedestrian dynamics simulations taking in account the representation of affective states.

Staircase design is critical to the evacuation of children. Through an agent-based simulation, this study focused on the relationship between staircase design factors and evacuation efficiency in a multi-story kindergarten. A quantitative study was conducted on three critical architectural design factors: stair flight width, positional relationship, and design pattern of the juncture between the staircase and the corridor. The findings were as follows. (1) When the stair flight width ranges from 0.7 to 1.0 m, an increase in this width can improve evacuation efficiency significantly; when the width ranges from 1.1 to 1.4 m, evacuation efficiency is improved continuously, but an increase in this width range has a diminishing effect on evacuation efficiency; when the width is greater than 1.7 m, a further increase has an adverse effect on evacuation efficiency, because such a staircase space allows overtaking behaviors. (2) Under the same stair flight width conditions, evacuation efficiency is higher when the staircase and corridor are perpendicular to each other than when they are parallel, because the natural steering angle of the children was preserved during their evacuation. (3) The cut corner and rounded corner designs between the staircase and corridor improved evacuation efficiency and alleviated the congestion at bottleneck positions; the evacuation efficiency continued to rise with an increase in the cutting angle. These findings are expected to provide a useful reference for the evacuation design of kindergarten buildings and for emergency evacuation management.