Continuous-space automaton model for pedestrian dynamics

Simulating pedestrian avoidance: The human-zombie game

This study introduces a simulated active matter system, applying the pedestrian collision avoidance paradigm, which involves dynamically adjusting the desired velocity. We present a human-zombie game set within a closed geometry, combining predator-prey behavior with a one-way contagion process that transforms prey into predators. The system demonstrates varied responses in our implemented model: with agents having the same maximum speeds, a single zombie always captures a human, whereas two zombies never capture a single human agent. As the number of human agents increases, observables such as the final fraction of zombie agents and total conversion times exhibit significant changes in the system's behavior at intermediate density values. Most notably, there is evidence of a first-order phase transition when analyzing the mean population speed as an order parameter.

Physical distance characterization using pedestrian dynamics simulation

In the present work we study how the number of simulated customers (occupancy) affects social distance in an ideal supermarket, considering realistic typical dimensions and processing times (product selection and checkout). From the simulated trajectories we measure social distance events of less than 2 m, and their duration. Among other observables, we define a physical distance coefficient that informs how many events (of a given duration) each agent experiences.

Pedestrian dynamics at the running of the bulls evidence an inaccessible region in the fundamental diagram

We characterize the dynamics of runners in the famous "Running of the Bulls" Festival by computing the individual and global velocities and densities, as well as the crowd pressure. In contrast with all previously studied pedestrian systems, we unveil a unique regime in which speed increases with density that can be understood in terms of a time-dependent desired velocity of the runners. Also, we discover the existence of an inaccessible region in the speed-density state diagram that is explained by falls of runners. With all these ingredients, we propose a generalization of the pedestrian fundamental diagram for a scenario in which people with different desired speeds coexist.

Collective dynamics of pedestrians in a corridor: An approach combining social force and Vicsek models

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.

Sparse Game Changers Restore Collective Motion in Panicked Human Crowds

Using a dynamic variant of the Vicsek model, we show that the emergence of disorder from an orderly moving human crowd is a nonequilibrium first-order phase transition. We also show that this transition can be reversed by modifying the dynamics of a few agents, deemed as game changers. Surprisingly, the optimal placement of these game changers is found to be in regions of maximum local crowd speed. The presence of such game changers is effective owing to the discontinuous nature of the underlying phase transition. Thus our generic approach provides strategies to (i) delay crowd crush and (ii) design safe evacuation procedures, two aspects that are of paramount importance in maintaining safety of mass gatherings of people.

Simulation of counterflow pedestrian dynamics using spheropolygons

Pedestrian dynamic models are typically designed for comfortable walking or slightly congested conditions and typically use a single disk or combination of three disks for the shape of a pedestrian. Under crowd conditions, a more accurate pedestrian shape has advantages over the traditional single or three-disks model. We developed a method for simulating pedestrian dynamics in a large dense crowd of spheropolygons adapted to the cross section of the chest and arms of a pedestrian. Our numerical model calculates pedestrian motion from Newton's second law, taking into account viscoelastic contact forces, contact friction, and ground-reaction forces. Ground-reaction torque was taken to arise solely from the pedestrians' orientation toward their preferred destination. Simulations of counterflow pedestrians dynamics in corridors were used to gain insight into a tragic incident at the Madrid Arena pavilion in Spain, where five girls were crushed to death. The incident took place at a Halloween Celebration in 2012, in a long, densely crowded hallway used as entrance and exit at the same time. Our simulations reconstruct the mechanism of clogging in the hallway. The hypothetical case of a total evacuation order was also investigated. The results highlights the importance of the pedestrians' density and the effect of counterflow in the onset of avalanches and clogging and provides an estimation of the number of injuries based on a calculation of the contact-force network between the pedestrians.

Game theory in models of pedestrian room evacuation

We analyze the pedestrian evacuation of a rectangular room with a single door considering a lattice gas scheme with the addition of behavioral aspects of the pedestrians. The movement of the individuals is based on random and rational choices and is affected by conflicts between two or more agents that want to advance to the same position. Such conflicts are solved according to certain rules closely related to the concept of strategies in game theory, cooperation and defection. We consider game rules analogous to those from the Prisoner's Dilemma and Stag Hunt games, with payoffs associated to the probabilities of the individuals to advance to the selected site. We find that, even when defecting is the rational choice for any agent, under certain conditions, cooperators can take advantage from mutual cooperation and leave the room more rapidly than defectors.

Evacuation of pedestrians from a single room by using snowdrift game theories

Game theory is introduced to simulate the complicated interaction relations among the conflicting pedestrians in a pedestrian flow system, which is defined on a square lattice with the parallel update rule. Modified on the traditional lattice gas model, each pedestrian can move to not only an empty site, but also an occupied site. It is found that each individual chooses its neighbor randomly and occupies the site with the probability W(x→y)=1/1+exp[-(P(x)-U(x))/κ], where P(x) is the x's payoff representing his personal energy, and U(x) is the average payoff of its neighborhood indicating the potential well energy if he stays. Two types of pedestrians are considered, and they interact with their neighbors following the payoff matrix of snowdrift game theory. The cost-to-benefit ratio r=c/(2b-c) (where b is the perfect payoff and c is the labor cost) represents the fear index of the pedestrians in this model. It is found that there exists a moderate value of r leading to the shortest escape time, and the situation for large values of r is better than that for small ones in general. In addition, the pedestrian flow system always arrives at a consistent state in which the two types of walkers have the same number and evolve by the same law irrespectively of the parameters, which can be interpreted as the self-organization effect of pedestrian flow. It is also proven that the time point of the onset of the steady state is unrelated to the scale of the pedestrians and the square lattice. Meanwhile, the system exhibits different dynamics before reaching the consistent state: the number of the two types of walkers oscillates when P(C)>0.5 (i.e., probability to change the present strategy), while no oscillation happens for P(C)≤0.5. Finally, it is shown that a smaller density of pedestrians ρ induces a shorter average escape time.

Natural discretization of pedestrian movement in continuous space

Is there a way to describe pedestrian movement with simple rules, as in a cellular automaton, but without being restricted to a cellular grid? Inspired by the natural stepwise movement of humans, we develop a model that uses local discretization on a circle around virtual pedestrians. This allows for movement in arbitrary directions, only limited by the chosen optimization algorithm and numerical resolution. The radii of the circles correspond to the step lengths of pedestrians and thus are model parameters, which must be derived from empirical observation. Therefore, we conducted a controlled experiment, collected empirical data for step lengths in relation with different speeds, and used the findings in our model. We complement the model with a simple calibration algorithm that allows reproducing known density-velocity relations, which constitutes a proof of concept. Further validation of the model is achieved by reenacting an evacuation scenario from experimental research. The simulated egress times match the values reported for the experiment very well. A new normalized measure for space occupancy serves to visualize the results.

Particle-based model for skiing traffic

We develop and investigate a particle-based model for ski slope traffic. Skiers are modeled as particles with a mass that are exposed to social and physical forces, which define the riding behavior of skiers during their descents on ski slopes. We also report position and speed data of 21 skiers recorded with GPS-equipped cell phones on two ski slopes. A comparison of these data with the trajectories resulting from computer simulations of our model shows a good correspondence. A study of the relationship among the density, speed, and flow of skiers reveals that congestion does not occur even with arrival rates of skiers exceeding the maximum ski lift capacity. In a sensitivity analysis, we identify the kinetic friction coefficient of skis on snow, the skier mass, the range of repelling social forces, and the arrival rate of skiers as the crucial parameters influencing the simulation results. Our model allows for the prediction of speed zones and skier densities on ski slopes, which is important in the prevention of skiing accidents.