An improved social force model for pedestrian dynamics in shipwrecks

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 emergency scenarios of passenger ships are reproduced unrealistically by evacuation drills due to cost and safety limitations, and model simulation is an effective alternative method. To study the evacuation efficiency of an inclined ship, a deck of a Ro-Ro ship with 1065 passengers was modeled on a simulation platform developed based on a social force model (SFM). A series of inclined scenarios were produced by adding the walking speed attenuation factor to the individual parameters. Furthermore, the influence of crew’s guidance was considered in this model, and the effects on evacuation were also analyzed for the entire deck. The simulation results show that the larger the inclination angle is, the more evacuation time that is needed. When the largest inclination angle was 20°, the evacuation time increased by 32.6%. In addition, the participation of crew’s guidance in all of these scenarios significantly accelerated the evacuation efficiency, and this benefit was achieved by the evaluation of the exit flow and congestion level by crew. However, organizing orderly evacuations as soon as possible when the inclination angle is less than 15° and making maximum efforts to ensure evacuation success are still recommended. The findings provide useful insights into a large group of Ro-Ro passenger vessels under inclined emergency evacuation.

Pedestrian Evacuation Modeling and Simulation on Metro Platforms Considering Panic Impacts

Modified social force model considering emotional contagion for crowd evacuation simulation

Human motion models are finding an increasing number of novel applications in many different fields, such as building design, computer graphics and robot motion planning. The Social Force Model is one of the most popular alternatives to describe the motion of pedestrians. By resorting to a physical analogy, individuals are assimilated to point-wise particles subject to social forces which drive their dynamics. Such a model implicitly assumes that humans move isotropically. On the contrary, empirical evidence shows that people do have a preferred direction of motion, walking forward most of the time. Lateral motions are observed only in specific circumstances, such as when navigating in overcrowded environments or avoiding unexpected obstacles. In this paper, the Headed Social Force Model is introduced in order to improve the realism of the trajectories generated by the classical Social Force Model. The key feature of the proposed approach is the inclusion of the pedestrians’ heading into the dynamic model used to describe the motion of each individual. The force and torque representing the model inputs are computed as suitable functions of the force terms resulting from the traditional Social Force Model. Moreover, a new force contribution is introduced in order to model the behavior of people walking together as a single group. The proposed model features high versatility, being able to reproduce both the unicycle-like trajectories typical of people moving in open spaces and the point-wise motion patterns occurring in high density scenarios. Extensive numerical simulations show an increased regularity of the resulting trajectories and confirm a general improvement of the model realism.

Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to simulate high-density crowd movement realistically due to the model having only three basic crowd characteristics which are goal, attraction, and repulsion. Therefore, it does not satisfy the high-density crowd condition which is complex yet unique, due to its capacity, density, and various demographic backgrounds of the agents. Thus, this research proposes a model that improves the social force model by introducing four new characteristics which are gender, walking speed, intention outlook, and grouping to make simulations more realistic. Besides, the high-density crowd introduces irregular behaviours in the crowd flow, which is stopping motion within the crowd. To handle these scenarios, another model has been proposed that controls each agent with two different states: walking and stopping. Furthermore, the stopping behaviour was categorized into a slow stop and sudden stop. Both of these proposed models were integrated to form a high-density crowd simulation framework. The framework has been validated by using the comparison method and fundamental diagram method. Based on the simulation of 45,000 agents, it shows that the proposed framework has a more accurate average walking speed (0.36 m/s) compared to the conventional social force model (0.61 m/s). Both of these results are compared to the real-world data which is 0.3267 m/s. The findings of this research will contribute to the simulation activities of pedestrians in a highly dense population.

Study on the propagation characteristics of shockwave in dense crowd in corner passage

Simulating pedestrian flow through narrow exits

The ability of robots to understand human characteristics and make themselves socially accepted by humans are important issues if smooth collision avoidance between humans and robots is to be achieved. When discussing smooth collision avoidance, robot should understand not only physical components such as human position, but also social components such as body pose, face orientation and proxemics (personal space during motion). We integrated these components in a modified social force model (MSFM) which allows robots to predict human motion and perform smooth collision avoidance. In the modified model, short-term intended direction is described by body pose, and a supplementary force related face orientation is added for intention estimation. Face orientation is also the best indication of the direction of personal space during motion, which was verified in preliminary experiments. Our approach was implemented and tested on a real humanoid robot in a situation in which a human is confronted with the robot in an indoor environment. Experimental results showed that better human motion tracking was achieved with body pose and face orientation tracking. Being provided with the face orientation as an indication of the intended direction, and observing the laws of proxemics in a human-like manner, the robot was able to perform avoidance motions that were more human-like when compared to the original social force model (SFM) in a face-to-face confrontation.

Abstract. Crowded centers always cause personnel casualties in evacuation operations. Stampede events often occur by hit, squeeze and crush due to panic. It is of vital important to alleviate such situation. With the deepening of personnel evacuation research, more and more researchers are committed to study individual behaviors and self-organization phenomenon in evacuation process. The study mainly includes: 1, enrich the social force model from different facets such as visual, psychological, external force to descript more realistic evacuation; 2, research on causes and effects of self – organization phenomenon. In this paper, we focus on disorder motion that occurs in the crowded indoor publics, especially the narrow channel and safety exits and other special arteries. We put forward the improved social force model to depict pedestrians’ behaviors, an orderly speed-stratification evacuation method to solve disorder problem, and shape-changed export to alleviate congestion. The result of this work shows an improvement of evacuation efficiency by 19.5 %. Guiding pedestrians’ direction to slow down the influence of social forces has a guidance function in improving the efficiency of indoor emergency evacuation.

Since the restaurant on cruise ships are filled with large numbers of passengers at mealtimes, the crowd in high-density may take the risk when they evacuate from the restaurant in emergent cases. The size of exits usually has a certain impact on the efficiency of passenger evacuation. It is necessary to study the correlation between the sizes of exits and evacuation efficiency, which could help to reasonably design the sizes of exits to ensure the safety of passengers. The AnyLogic software is used to establish the spatial model of the restaurant, and the sizes of restaurant exits are set as the independent variable. Then the evacuation performance of heterogeneous passengers under different exit sizes is simulated. The simulation results show that the evacuation time increases as the width of exits decreases, and the evacuation time of passengers is highly sensitive to the width of exits when the width is less than 3m.

We study the pedestrian motion along a corridor in a nonpanic regime, as usually happens in evacuation scenarios in, e.g., schools, hospitals, or airports, by means of Monte Carlo simulations. We present a model, a combination of the well-known social force model (SFM) and Vicsek model (VM), that takes into account both model interactions, based on the relative position (SFM) and based on the velocity of the particles with some randomness (modulated by an external control parameter, the noise η, VM), respectively. To clarify the influence of the model ingredients we have compared simulations using (a) the pure Vicsek model (VM) with two boundary conditions (periodic and bouncing back) and with or without desired direction of motion, (b) the social force model (SFM), and (c) the model (SFM+VM). The study of steady-state particle configurations in the VM with confined geometry shows the expected bands perpendicular to the motion direction, while in the SFM and SFM+VM particles order in stripes of a given width w along the direction of motion. The results in the SFM+VM case show that w(t)≃t^{α} has a diffusivelike behavior at low noise η (dynamic exponent α≈1/2), while it is subdiffusive at high values of external noise (α<1/2). We observe the well-known order-disorder transition in the VM with both boundary conditions, but the application of a desired direction condition inhibits the existence of disorder as expected. Similar behavior is observed in the SFM case. For the SFM+VM case we find a susceptibility maximum which slowly increases with system size as a function of noise strength. This might be indicative of a order-disorder transition in the range of densities (ρε[1/12,1/9]) and speeds (v_{0}ε[0.5,2]) studied.

In the present paper, we investigate arch-shaped equilibrium solutions in the social force model proposed by Helbing and Molnar (Phys Rev E 51:4282, 1995) and Helbing et al. (Nature 407:487, 2000). The social force model is a system of ordinary differential equations, which describe the motion of the pedestrians under a panic situation. In the simulation of the social force model, we observe an intermittent appearance of arch-shaped structures (i.e. the “Blocking clusters” Parisi and Dorso, Physica A 354:606, 2005; Physica A 385:343, 2007; Frank and Dorso, Physica A 390:2135, 2011) around an exit which block up the flow of pedestrians. To understand such a dynamic behavior, we study arch-shaped equilibrium solutions around an exit under the simplest configuration.

Pedestrian movement modeling and simulation is an important way to formulate an emergency evacuation plan. The performance of the pedestrian movement model directly determines the accuracy of the simulation experiment results. Pedestrian movement in the real world is a very complicated process, and this process is susceptible to a variety of external factors (e.g. road conditions, visibility, etc.) and internal factors (e.g. psychological states, etc.) which making it difficult to model pedestrian movement. The social force model draws on the principles of mechanics and proposes a modeling scheme from the perspective of social force which greatly simplifies the process of pedestrian movement modeling. However, the traditional social force model considers the general situation of pedestrian movement and does not target the emergency evacuation scene, which has certain limitations. In this paper we start with the social force model and introduces the concepts of external guides and internal panic emotions to improves the model. In the scene, the influence of the attraction of the guide and the psychological state of the pedestrian on the movement process of the pedestrian are considered. So that the model is more suitable for the emergency evacuation scene with limited field of vision. It also provides a reference for emergency evacuation plans in public places. We take the subway station as the research object and uses Anylogic software to build simulation experiments. The experimental results show that the improved model we proposed in this paper is less than the traditional model in terms of evacuation time by 8.4%, and is better than the traditional model in terms of exit pedestrian density by 21.5%.

The unprecedented growth of passenger throughput in large airport terminals highlights the importance of analyzing passengers’ movement to achieve airport terminal’s elaborate management. Based on the theory of original social force model, video data from a departure hall of a large airport terminal in China were analyzed to summarize passengers’ path planning characteristics. Then, a double‐level model was established to describe passengers’ path planning behaviors. At the decision level of the proposed model, the avoiding force model including common avoiding force and additional horizontal avoiding force was established on the basis of setting time and space limitations for taking avoiding action and was used to describe passengers’ path planning in close‐range space. At the tactical level of the proposed model, the route and node choice models were established to describe passengers’ path planning in long‐range space. In the route choice model, a distribution of intermediate destination areas was proposed, with detouring distance, pedestrian density, speed difference, and pedestrian distribution considered in choosing an intermediate destination area. In the node choice model, the walking distance, the quantity of people waiting, and luggage were considered in choosing a check‐in counter or security check channel. The main parameters of the proposed model were confirmed according to video data. Simulation results show that the proposed model can simulate departure passengers’ path planning behaviors at an acceptable accuracy level.