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Review on Microscopic Model of Continuous Pedestrian Flow
Abstract
Continuous pedestrian flow micro model is an important method to study pedestrian flow at present. Compared with macro model and discrete model, continuous pedestrian flow micro model can better simulate pedestrian flow phenomenon. This paper summarizes the research significance and achievements of the continuous pedestrian flow micro model. The research contents and corresponding modeling methods of relevant models are mainly introduced, and the future development of pedestrian flow micro models is prospected.
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Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner—that is, robots have to navigate in such a way that people feel safe and comfortable. In this work, we present two navigation tasks, social robot navigation and robot accompaniment, which combine machine learning techniques with the Social Force Model (SFM) allowing human-aware social navigation. The robots in both approaches use data from different sensors to capture the environment knowledge as well as information from pedestrian motion. The two navigation tasks make use of the SFM, which is a general framework in which human motion behaviors can be expressed through a set of functions depending on the pedestrians’ relative and absolute positions and velocities. Additionally, in both social navigation tasks, the robot’s motion behavior is learned using machine learning techniques: in the first case using supervised deep learning techniques and, in the second case, using Reinforcement Learning (RL). The machine learning techniques are combined with the SFM to create navigation models that behave in a social manner when the robot is navigating in an environment with pedestrians or accompanying a person. The validation of the systems was performed with a large set of simulations and real-life experiments with a new humanoid robot denominated IVO and with an aerial robot. The experiments show that the combination of SFM and machine learning can solve human-aware robot navigation in complex dynamic environments.
Simulation modeling is an important tool for simulating crowd behavior and studying the law of crowd evacuation. It is of great significance for exploring evacuation management methods in emergency situations. The real-time change of evacuation is the main challenge of simulation modeling. In the evacuation simulation, it is difficult for people to choose a suitable route according to the change of evacuation dynamics. This paper proposes a new evacuation simulation method which combines an improved artificial bee colony algorithm for dynamic path planning and SFM (Social Force Model) for simulating the movement of pedestrians, to providing pedestrians with timely route selection. In the path planning layer, we developed a MABCM (Multiple-subpopulations Artificial Bee Colony with Memory) algorithm and proposed a new exit evaluation strategy. These methods can plan a route with the shortest evacuation time for pedestrians according to the dynamic changes of evacuation and improve evacuation efficiency. In the simulated motion layer, we use the SFM to avoid collisions and achieve the reproduction of the evacuation scene. We verified the performance of the proposed MABCM on the CEC 2014 benchmark suite, and the results show that it is superior to the four existing artificial bee colony algorithms in most cases. The proposed crowd evacuation method is verified on an existing SFM platform. The experimental results indicate that the proposed method can efficiently evacuate a dense crowd in multiple scenes and can effectively shorten evacuation time.
Modeling pedestrian decision making activities represents a serious challenge: different decisions are taken at distinct levels of abstraction, employing heterogeneous information and knowledge about the environment, from path planning to the regulation of distance from other pedestrians and obstacles present in the environment. Pedestrians, moreover, are not robots: although empirical observations show that they consider congestion when planning, there are also evidences that their decisions are not always optimal, even in normal situations. We present a model integrating and improving consolidated results mitigating the optimization effects of congestion aware path planning. In particular, we employ commonsense estimations of the effects of perceivable congestion instead of exact values, also embedding an imitation mechanism stimulating changes in planned decisions whenever another nearby pedestrian did the same. The model leads to improvements in quantitatively reproducing observed phenomena, both in a validation scenario as well as in a real-world situation: an interesting counterintuitive result, in which reducing available choices and exits actually reduces overall egress time, is also presented and discussed.
The original social force model of pedestrians and its variants have been extensively studied in the past. However, little attention has been given to their parameter estimation and validation specifically for the environments with high density and complex phenomena such as self-organization and lane formation. This paper presents a macroscopic framework for calibration and validation of a modified social force model for bidirectional pedestrian streams. We use empirical three-dimensional trajectories from a set of controlled experiments to quantify macroscopic traffic flow characteristics based on the concept of pedestrian area-wide fundamental diagram, also known as pedestrian Macroscopic Fundamental Diagram (p-MFD), measured over a 28.8 m² area. The calibration framework is formulated as a non-linear optimization problem minimizing the discrepancy between the observed and simulated p-MFD. A metaheuristic solution method based on genetic algorithm is implemented to solve the problem. The calibrated model is then applied to simulate a multi-platform train station as a complex environment with multi-directional pedestrian movements. Results demonstrate that the proposed calibration process and the modified social force model can reproduce the empirically observed p-MFD with features such as hysteresis while consistently generating microscopic emergent self-organization and lane formation phenomena. Overall, validation and application results suggest that the calibrated model successfully replicates pedestrian traffic patterns and support the future applications of the model for various operations and planning purposes.
Virtual geographic environments (VGEs) are extensively used to explore the relationship between humans and environments. Crowd simulation provides a method for VGEs to represent crowd behaviors that are observed in the real world. The social force model (SFM) can simulate interactions among individuals, but it has not sufficiently accounted for inter-group and intra-group behaviors which are important components of crowd dynamics. We present the social group force model (SGFM), based on an extended SFM, to simulate group behaviors in VGEs with focuses on the avoiding behaviors among different social groups and the coordinate behaviors among subgroups that belong to one social group. In our model, psychological repulsions between social groups make them avoid with the whole group and group members can stick together as much as possible; while social groups are separated into several subgroups, the rear subgroups try to catch up and keep the whole group cohesive. We compare the simulation results of the SGFM with the extended SFM and the phenomena in videos. Then we discuss the function of Virtual Reality (VR) in crowd simulation visualization. The results indicate that the SGFM can enhance social group behaviors in crowd dynamics.
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.
The Social Force Model is one of the most prominent models of pedestrian
dynamics. As such naturally much discussion and criticism has spawned around
it, some of which concerns the existence of oscillations in the movement of
pedestrians. This contribution is investigating under which circumstances,
parameter choices, and model variants oscillations do occur and how this can be
prevented. It is shown that oscillations can be excluded if the model
parameters fulfill certain relations. The fact that with some parameter choices
oscillations occur and with some not is exploited to verify a specific computer
implementation of the model.
Based on suitable video recordings of interactive pedestrian motion and improved tracking software, we apply an evolutionary optimization algorithm to determine optimal parameter specifications for the social force model. The calibrated model is then used for large-scale pedestrian simulations of evacuation scenarios, pilgrimage, and urban environments.
One of the most disastrous forms of collective human behaviour is the kind of crowd stampede induced by panic, often leading to fatalities as people are crushed or trampled. Sometimes this behaviour is triggered in life-threatening situations such as fires in crowded buildings; at other times, stampedes can arise during the rush for seats or seemingly without cause. Although engineers are finding ways to alleviate the scale of such disasters, their frequency seems to be increasing with the number and size of mass events. But systematic studies of panic behaviour and quantitative theories capable of predicting such crowd dynamics are rare. Here we use a model of pedestrian behaviour to investigate the mechanisms of (and preconditions for) panic and jamming by uncoordinated motion in crowds. Our simulations suggest practical ways to prevent dangerous crowd pressures. Moreover, we find an optimal strategy for escape from a smoke-filled room, involving a mixture of individualistic behaviour and collective 'herding' instinct.
It is suggested that the motion of pedestrians can be described as if they would be subject to `social forces'. These `forces' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can be also applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: First, a term describing the acceleration towards the desired velocity of motion. Second, terms reflecting that a pedestrian keeps a certain distance to other pedestrians and borders. Third, a term modeling attractive effects. The resulting equations of motion are nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically. Comment: For related work see http://www.theo2.physik.uni-stuttgart.de/helbing.html
In closed rooms with limited convection human motion can considerably affect the airflow and thus the dispersion of pollutant. However, in Computational Fluid Dynamics (CFD) simulations on air quality and safety for human beings this effect is generally not considered, which is mainly due to a lack of a well-founded and detailed estimation of the human behavior and the high computational cost of taking into account moving objects in CFD meshes. This work addresses this issue by coupling multidisciplinary methods to allow for a more realistic simulation of pollutant dispersion by taking into account the influence of human movements. A Social Force Model predicts trajectory and speed of each person moving in a complex environment. A lattice Boltzmann-based CFD tool provides a Large Eddy Simulation of the unsteady turbulent airflow with pollutant dispersion and thermal effects. And an Actuator Line Model supplies the CFD tool with body forces that mimic the impact of moving objects on the airflow, thus, avoiding computationally expensive dynamic meshing. The capability of the coupled model is demonstrated on three realistic evacuation scenarios with various pollutant sources and a wide range of scales (dimension from 10 to 100 m, occupation from 10 to 6000 persons). The results allow to access instantaneous environmental parameters like pollutant concentration for each person during the course of the evacuation, enabling the assessment of associated health risks.
Guiding crowd evacuation through changes in crowd density is one of the hot spots in crowd simulation research. Crowd evacuation methods can intuitively guide crowds to avoid high-density areas and improve evacuation efficiency. However, these methods are limited in accounting for the overall situation and lack realism. Therefore, this paper proposes a crowd evacuation method based on a density navigation algorithm. In the proposed method, a density navigation field model based on an equipotential field is first established. The model uses changes in the amount of charge to respond to changes in the density of the crowd, forming a density field. Second, this paper proposes a density navigation algorithm that guides the target selection in the process of crowd movement by calculating the density factor and distance factor. Finally, the density evacuation algorithm is combined with the social force model (SFM) to perform crowd evacuation simulation using a two-layer mechanism. The upper layer uses the density navigation algorithm for path planning, and the bottom layer uses the social force model to guide the evacuation. The experimental results show that the method effectively improves the crowd evacuation efficiency and can provide auxiliary decision support for large-scale group gathering events.
The escape panic version of the Social Force Model (SFM) is a suitable model for describing emergency evacuations. In this research, we analyze a real-life video, recorded at the opening of a store during a Black Friday event, which resembles an emergency evacuation (November 2017, South Africa). We measure the flow of pedestrians entering the store and found a higher value (〈J〉=6.7±0.8p/s) than the usually reported in “laboratory” conditions. We performed numerical simulations to recreate this event. The empirical measurements were compared against simulated evacuation curves corresponding to different sets of parameters currently in use in the literature. The results obtained suggest that the set of parameters corresponding to calibrations from laboratory experiments (involving pedestrians in which the safety of the participants is of major concern) or situations where the physical contact is negligible, produce simulations in which the agents evacuate faster than in the empirical scenario. To conclude the paper, we optimize two parameters of the model: the friction coefficient kt and the body force coefficient kn. The best fit we found could replicate the qualitative and quantitative behavior of the empirical evacuation curve. We also found that many different combinations in the parameter space can produce similar results in terms of the goodness of fit.
A social force model is developed in this paper to study the crowd evacuation when a terrorist attack occurs in the public place. The persons in the model are divided into two groups—terrorists and pedestrians. On one hand, each terrorist chooses the nearest pedestrian as the target. Once the distance between him and his target is small enough, the terrorist will launch attacks on his target and the target will be killed with a certain probability. On the other hand, each pedestrian tries to avoid the terrorists and reach the exits. An emergency exit choice strategy which emphasizes the security risk factor is developed for pedestrians. The main simulation results are summarized as follows. First, the number of deaths in the terrorist attack increases with the number of terrorists and with the density of pedestrians. If the number of exits decreases, the death toll will become more sensitive to the change of the density of pedestrians. Second, adding the number of exits can significantly reduce casualties. Third, more pedestrians will be killed and the evacuation speed will be reduced if terrorists start the attack from the positions of the exits. Fourth, the emergency exit choice strategy has an advantage over the ordinary exit choice strategy in daily life for reducing casualties. The more unbalanced the terrorists’ initial distribution around the exits is, the more noticeable this advantage will be. Fifth, the number of deaths will decrease obviously if at least half of the exits are available and safe. Our study is valuable for developing an effective evacuation scheme to reduce casualties in a terrorist attack.
Pedestrian dynamics is of great theoretical significance for strategy design of emergency evacuation. Modification of pedestrian dynamics based on the social force model is presented to better reflect pedestrians’ behavioral characteristics in emergency. Specifically, the modified model can be used for guided crowd dynamics in large-scale public places such as subway stations and stadiums. This guided crowd model is validated by explicitly comparing its density–speed and density–flow diagrams with fundamental diagrams. Some social phenomena such as gathering, balance and conflicts are clearly observed in simulation, which further illustrate the effectiveness of the proposed modeling method. Also, time delay for pedestrians with time-dependent desired velocities is observed and explained using the established model in this paper. Furthermore, this guided crowd model is applied to the simulation system of Beijing South Railway Station for predictive evacuation experiments.
Considerable research has been conducted on the topic of unidirectional evacuations from exits. However, few studies aim at simulating counter flow through a bottleneck with complex conflict. This paper proposes an agent-based model to investigate bidirectional flow evacuation. Pedestrian speed is determined by the speed of the leading agent and the surrounding agents. The moving direction of pedestrian originates from four forces, namely, gradient force, repulsive force, resistance force, and random force. These four forces dominate the main stream of the pedestrian moving trajectory, the interaction between pedestrians and their local environment, the resistance or disinclination to movement, and the random variations and chaotic nature of pedestrian dynamics. The novelty of this research is in the agent-based model that combines the agent and forces while providing insights for the simulation of the pedestrian dynamic on the cognitive level. The experiment results show that the behavior that arises from this model is consistent with the observations from Guangzhou Metro and that this model could help capture the essence of pedestrian behavior near egresses.
The Social Force Model presents some limitations when describing the experimental data of pedestrian flows in normal conditions — in particular the specific flow rates for different door widths and the fundamental diagram.In the present work we propose a modification of this model that consists of a self-stopping mechanism to prevent a simulated pedestrian from continuously pushing over other pedestrians.With this simple change, the modified model is able to reproduce the specific flow rates and fundamental diagram of pedestrian flows for normal conditions, as reported in the literature.
A model of crowd motion that considers each pedestrian as a Newtonian particle subject to both physical and social forces was reported by Helbing, Farkas, and Vicsek in 2000. Subsequent numerical simulations of this model, performed by its authors, showed that it exhibits realistic crowd behavior. In this article, the authors point out that numerical values of certain parameters in that model may produce counterintuitive results when applied to the motion of an isolated pedestrian or a small number of pedestrians. They have considered modifications of the original model, which allow them to use parameter values that, in the aforementioned sense, are more realistic. However, this is achieved by introducing more features and parameters into the original model. These features are described, and some results of the numerical simulations of the modified model are presented. Two major results of their study need to be mentioned. First, they developed an algorithm, based on an explicit numerical integration scheme, which prevents simulated pedestrians from overlapping with one another in physical space. Second, they demonstrated how the form of the social repulsive force between two pedestrians may be deduced from certain measured characteristics of pedestrian flows.
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