Simulating dynamical features of escape panic

Controlling colloidal flow through a microfluidic Y-junction

Microscopic particles flowing through narrow channels may accumulate near bifurcation points provoking flow reduction, clogging and ultimately chip breakage in a microfluidic device. Here we show that the full flow behavior of colloidal particles through a microfluidic Y-junction can be controlled by tuning the pair interactions and the degree of confinement. By combining experiments with numerical simulations, we investigate the dynamic states emerging when magnetizable colloids flow through a symmetric Y-junction such that a single particle can pass through both gates with the same probability. We show that clogging, induced by the inevitable presence of a stagnation point, can be avoided by repulsive interactions. Moreover we tune the pair interactions to steer branching into the two channels: attractive particles are flowing through the same gate, while repulsive colloids alternate between the two gates. Even details of the particle assembly such as buckling at the exit gate are tunable by the interactions and the channel geometry.

Simulating human behavior under earthquake early warning

Earthquakes are a rapid-onset hazard where advance planning and learning plays a key role in mitigating injuries and death to individuals. Recent advances in earthquake detection have resulted in the development of earthquake early warning (EEW) systems. These systems can provide advance warning to predetermined geographic regions that an earthquake is in progress, which may result in individuals receiving warning seconds before significant shaking is felt at their location. This additional time could allow individuals to take more effective protective actions during the immediate disaster. To demonstrate this, we created an agent-based simulation of a basic apartment that allowed us to randomly and repeatedly simulate an individual receiving and responding to an EEW message. The results of our preliminary simulation show that, in our study environment, earthquake early warning alerts have the potential to allow for sufficient time for individuals to take protective actions.

Efficient crowd simulation in complex environment using deep reinforcement learning

Simulating virtual crowds can bring significant economic benefits to various applications, such as film and television special effects, evacuation planning, and rescue operations. However, the key challenge in crowd simulation is ensuring efficient and reliable autonomous navigation for numerous agents within virtual environments. In recent years, deep reinforcement learning has been used to model agents' steering strategies, including marching and obstacle avoidance. However, most studies have focused on simple, homogeneous scenarios (e.g., intersections, corridors with basic obstacles), making it difficult to generalize the results to more complex settings. In this study, we introduce a new crowd simulation approach that combines deep reinforcement learning with anisotropic fields. This method gives agents global prior knowledge of the high complexity of their environment, allowing them to achieve impressive motion navigation results in complex scenarios without the need to repeatedly compute global path information. Additionally, we propose a novel parameterized method for constructing crowd simulation environments and evaluating simulation performance. Through evaluations across three different scenario levels, our proposed method exhibits significantly enhanced efficiency and efficacy compared to the latest methodologies. Our code is available at https://github.com/tomblack2014/DRL_Crowd_Simulation.

Stochastic dynamics of granular hopper flows: A configurational mode controls the stability of clogs

Granular flows in small-outlet hoppers exhibit several characteristic but poorly understood behaviors: temporary clogs (pauses) where flow stops before later spontaneously restarting, permanent clogs that last indefinitely, and non-Gaussian, nonmonotonic flow-rate statistics. These aspects have been studied independently, but a model of hopper flow that explains all three has not been formulated. Here, we introduce a phenomenological model that provides a unifying dynamical mechanism for all three behaviors: coupling between the flow rate and a hidden mode that controls the stability of clogs. In the theory, flow rate evolves according to Langevin dynamics with multiplicative noise and an absorbing state at zero flow, conditional on the hidden mode. The model fully reproduces the statistics of pause and clog events of a large (>40000 flows) experimental dataset, including nonexponentially distributed clogging times and non-Gaussian flow rate distribution, and explains the stretched-exponential growth of the average clogging time with outlet size. Further, we identify the physical nature of the hidden mode in microscopic configurational features, including size and smoothness of the static arch structure formed during pauses and clogs. Our work provides a unifying framework for several poorly understood clogging phenomena, and suggests numerous new paths toward further understanding of this complex system.

Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies

The collective dynamics of self-organised systems emerge from the decision rules agents use to respond to each other and to external forces. This is evident in groups of animals under attack from predators, where understanding collective escape patterns requires evaluating the risks and rewards associated with particular social rules, prey escape behaviour, and predator attack strategies. Here, we find that the emergence of the 'fountain effect', a common collective pattern observed when animal groups evade predators, is the outcome of rules designed to maximise individual survival chances given predator hunting decisions. Using drone-based empirical observations of schooling sardine prey (Sardinops sagax caerulea) attacked by striped marlin (Kajikia audax), we first find the majority of attacks produce fountain effects, with the dynamics of these escapes dependent on the predator's attack direction. Then, using a spatially-explicit agent-based model of predator-prey dynamics, we show that fountain manoeuvres can emerge from combining an optimal individual prey escape angle with social interactions. The escape rule appears to prioritise maximising the distance to the predator and creates conflict in the effectiveness of predators' attacks and the prey's avoidance, explaining the empirically observed predators' attack strategies and the fountain evasions produced by prey. Overall, we identify the proximate and ultimate explanations for fountain effects and more generally highlight that the collective patterns of self-organised predatory-prey systems can be understood by considering both social escape rules and attack strategies.

Dynamics of switching processes: general results and applications in intermittent active motion

Systems switching between different dynamical phases is a ubiquitous phenomenon. The general understanding of such a process is limited. To this end, we present a general expression that captures fluctuations of a system exhibiting a switching mechanism. Specifically, we obtain an exact expression of the Laplace-transformed characteristic function of the particle's position. Then, the characteristic function is used to compute the effective diffusion coefficient of a system performing intermittent dynamics. Furthermore, we employ two examples: (1) generalized run-and-tumble active particle, and (2) an active particle switching its dynamics between generalized active run-and-tumble motion and passive Brownian motion. In each case, explicit computations of the spatial cumulants are presented. Our findings reveal that the particle's position probability density function exhibit rich behaviours due to intermittent activity. Numerical simulations confirm our findings.

A literature review of contacting force measurement methods for pedestrian crowds

This article reviewed state-of-the-art achievements in pedestrian contacting force measurement as a hotspot survey closer to ground truth supporting pedestrian dynamics in mass-gathering environments. It analyzed different forces acting on pedestrian bodies, including normal external forces, self-driven forces, abnormal external forces, and pedestrian motion constraint forces from other obstacles, besides the crowding posture on the force distribution. This review covered main methodologies: sophisticated pressure sensors, modern technology for pedestrian motion-capturing systems, and advanced numerical simulations. Further, this paper summarized key findings from recent studies related to pedestrian contacting or crowding forces. It was found that despite significant advances, study achievements are mainly limited to different crowding postures, such as experiments regarding controlled environments in flat areas, indoor corridors, staircases, and competitive evacuation drills. Lack of sufficient sensor-based body measurements and contact force measurements on slop roads was analyzed. Finally, future research outlook was outlined, including planned experiments in highly crowded environments.

Experimental study on the synchronization mechanism and trigger characteristic density of vertical evacuation in crowds

Due to simultaneous horizontal and vertical displacement during vertical evacuation, the consequences of stampede congestion accidents can be more severe. Generally, pedestrians trigger a synchronization mechanism at some point during the vertical evacuation process. This synchronization behavior helps prevent stampede congestion and improves evacuation efficiency. This paper designs a well-controlled single-file vertical evacuation experiment. After the experiment, the video footage is imported into the TRACKER system to extract the coordinates of pedestrian step movements, after which the experimental data undergo calculations and visual analysis. The research findings indicate the following: Firstly, when the crowd coordinates trigger the synchronization mechanism, this behavior remains stable as long as pedestrian speed and direction are unchanged; Secondly, the variation in footstep speed over time is not directly related to the footstep synchronization rate of the crowd; Lastly, this study calculated the characteristic density value most likely to trigger the synchronization mechanism during vertical evacuation. This research deepens our understanding of crowd dynamics, reveals the characteristics of pedestrian movement during vertical evacuation, and proposes evacuation guidance strategies based on these features.

Activity-induced phase transition and coarsening dynamics in dry apolar active nematics

Using the Lebwohl-Lasher interaction for reciprocal local alignment, we present a comprehensive phase diagram for a dry, apolar, active nematic system using its stochastic off-lattice dynamics. The nematic-isotropic transition in this system is first-order and occurs alongside a fluctuation-dominated phase separation. Our phase diagram identifies three distinct regions based on activity and orientational noise relative to alignment strength: a homogeneous isotropic phase, a nematic phase with giant density fluctuations, and a coexistence region. Using mean-field analysis and hydrodynamic theory, we demonstrate that reciprocal interactions lead to a density fluctuation-induced first-order transition and derive a phase boundary consistent with numerical results. Quenching from the isotropic to nematic phase reveals coarsening dynamics where nematic ordering precedes particle clustering. Both the nematic and density fields exhibit similar scaling behaviors, exhibiting dynamic exponents zS ≈ 2.5 and zρ ≈ 2.34, consistently falling within the range of 2 and 3.

Emergence of social phases in human movement

Recent empirical studies have found different thermodynamic phases for collective motion in animals. However, such a thermodynamic description of human movement remains unclear. Existing studies of traffic and pedestrian flows have primarily focused on relatively high-speed mobility data, revealing only a fluidlike phase. This focus is partly because the parameter space of low-speed movement, which is governed predominantly by pairwise social interaction, remains largely uncharted. Here, we used ultrawideband radio frequency identification (UWB-RFID) technology to collect high-resolution spatiotemporal data on movements in four different classroom and playground settings. We observed two unique social phases in children's movements: a gaslike phase of free movement and a liquid-vapor coexistence phase characterized by the formation of small social groups. We also developed a simple statistical physics model that can reproduce different empirically observed phases. The proposed UWB-RFID technology can also be used to study the dynamics of active matter systems, including animal behavior, coordinating robotic swarms, and monitoring human interactions within complex systems, potentially benefiting future research in social physics.

Defect Solutions of the Nonreciprocal Cahn-Hilliard Model: Spirals and Targets

We study the defect solutions of the nonreciprocal Cahn-Hilliard model. We find two kinds of defects, spirals with unit magnitude topological charge, and topologically neutral targets. These defects generate radially outward traveling waves and thus break the parity and time-reversal symmetry. For a given strength of nonreciprocity, spirals and targets with unique asymptotic wave number and amplitude are selected. We use large-scale simulations to show that at low nonreciprocity α, disordered states evolve into quasistationary spiral networks. With increasing α, we observe networks composed primarily of targets. Beyond a critical threshold α_{c}, a disorder-order transition from defect networks to traveling waves emerges. The transition is marked by a sharp rise in the global polar order.

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.

TraInterSim: Adaptive and Planning-Aware Hybrid-Driven Traffic Intersection Simulation

Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent's movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others' motion plannings and relative distances to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method.

Unraveling the causes of the Seoul Halloween crowd-crush disaster

As the world steadily recovers from the COVID-19 pandemic, managing large gatherings becomes a critical concern for ensuring crowd safety. The crowd-crush disaster in Seoul in 2022 highlights the need for effective predictive crowd management techniques. In this study, an empirical analysis of this incident is conducted using data from various sources, and model-based simulations are created to replicate hazardous crowd conditions in high-risk areas. In the empirical analysis, mobile device data indicates a significant increase in population above normal levels in the disaster area just hours before the incident occurred. In the simulations, a hydrodynamic model is employed to simulate a bidirectional collision, which quantitatively demonstrates that the average density during the crush reached 7.57 ped/m2 (with a maximum of (9.95)ped/m2). Additionally, the average crowd pressure peaked at 1,063 N/m (with a maximum of 1,961 N/m), and the maximum velocity entropy was 10.99. Based on these findings, it can be concluded that the primary causes of the disaster were the substantial population, bidirectional collision, and escalating panic. The results of controlled simulations under various management strategies are then presented. By implementing effective crowd management techniques, crowd safety can be enhanced through quantitative comparisons of these key indicators.

The combination operation of grouping and ensemble coding for structured biological motion crowds in working memory

Massive studies have explored biological motion (BM) crowds processing for their remarkable social significance, primarily focused on uniformly distributed ones. However, real-world BM crowds often exhibit hierarchical structures rather than uniform arrangements. How such structured BM crowds are processed remains a subject of inquiry. This study investigates the representation of structured BM crowds in working memory (WM), recognizing the pivotal role WM plays in our social interactions involving BM. We propose the group-based ensemble hypothesis and test it through a member identification task. Participants were required to discern whether a presented BM belonged to a prior memory display of eight BM, each with distinct walking directions. Drawing on prominent Gestalt principles as organizational cues, we constructed structured groups within BM crowds by applying proximity and similarity cues in Experiments 1 and 2, respectively. In Experiment 3, we deliberately weakened the visibility of stimuli structures by increasing the similarity between subsets, probing the robustness of results. Consistently, our findings indicate that BM aligned with the mean direction of the subsets was more likely to be recognized as part of the memory stimuli. This suggests that WM inherently organizes structured BM crowds into separate ensembles based on organizational cues. In essence, our results illuminate the simultaneous operation of grouping and ensemble encoding mechanisms for BM crowds within WM.

Behavioral transition of a fish school in a crowded environment

In open water, social fish gather to form schools, in which fish generally align with each other. In this work, we study how this social behavior evolves when perturbed by artificial obstacles. We measure the behavior of a group of zebrafish in the presence of a periodic array of pillars. When the pillar density is low, the fish regroup with a typical interdistance and a well-polarized state with parallel orientations, similarly to their behavior in open-water conditions. Above a critical density of pillars, their social interactions, which are mostly based on vision, are screened and the fish spread randomly through the aquarium, orienting themselves along the free axes of the pillar lattice. The abrupt transition from natural to artificial orientation happens when the pillar interdistance is comparable to the social distance of the fish, i.e., their most probable interdistance. We develop a stochastic model of the relative orientation between fish pairs, taking into account alignment, antialignment, and tumbling, from a distribution biased by the environment. This model provides a good description of the experimental probability distribution of the relative orientation between the fish and captures the behavioral transition. Using the model to fit the experimental data provides qualitative information on the evolution of cognitive parameters, such as the alignment or the tumbling rates, as the pillar density increases. At high pillar density, we find that the artificial environment imposes its geometrical constraints to the fish school, drastically increasing the tumbling rate.

Effects of Sadness and Fear on Moral Judgments in Public Emergency Events

With the rapid development of society and the deteriorating natural environment, there has been an increase in public emergencies. This study aimed to explore how sadness and fear in the context of public emergencies influence moral judgments. This research first induced feelings of sadness and fear by using videos about public emergencies and music, and then used moral scenarios from the CNI model (C parameter: sensitivity to consequences; N parameter: sensitivity to norms; I parameter: general preference for inaction) to assess participants' moral thinking. In Study 1, participants were divided into a sadness group and a neutral group, while in Study 2, participants were divided into a fear group and a neutral group. During the experiment, participants were exposed to different videos related to public emergencies to induce the corresponding emotions, and emotional music was continuously played throughout the entire experiment. Participants were then asked to answer questions requiring moral judgments. The results showed that based on the CNI model, sadness induced in the context of public emergencies significantly increased the C parameter, without affecting the N or I parameters. Fear increased the I parameter, without affecting the C or I parameters. That is, sadness and fear induced in the context of a public emergency can influence moral judgments. Specifically, sadness increases individuals' sensitivity to consequences and fear increases the general preference for inaction in moral judgments.

Revealing the mechanism and function underlying pairwise temporal coupling in collective motion

Coordinated motion in animal groups has predominantly been studied with a focus on spatial interactions, such as how individuals position and orient themselves relative to one another. Temporal aspects have, by contrast, received much less attention. Here, by studying pairwise interactions in juvenile zebrafish (Danio rerio)-including using immersive volumetric virtual reality (VR) with which we can directly test models of social interactions in situ-we reveal that there exists a rhythmic out-of-phase (i.e., an alternating) temporal coordination dynamic. We find that reciprocal (bi-directional) feedback is both necessary and sufficient to explain this emergent coupling. Beyond a mechanistic understanding, we find, both from VR experiments and analysis of freely swimming pairs, that temporal coordination considerably improves spatial responsiveness, such as to changes in the direction of motion of a partner. Our findings highlight the synergistic role of spatial and temporal coupling in facilitating effective communication between individuals on the move.

Analyzing factors causing deadlock events of bi-directional pedestrian flow when moving on stairs using a personal space model

Comprehending crowd dynamics on staircases is imperative for preventing accidents, particularly in emergency scenarios. In this context, our study delves into bi-directional pedestrian flow. When confronted with limited staircase space, the occurrence of two distinct lanes-one for upstairs and another for downstairs-is a common observation. However, there has been no quantitative investigation conducted to understand this phenomenon. To facilitate such an analysis, we employ a velocity-based personal space model that accurately reproduces the formation of upstairs and downstairs lanes in bi-directional staircases. The study of lane formation mechanisms and the exploration of factors influencing deadlock are essentially two sides of the same coin. This is due to the fact that, the occurrence of deadlock signifies a disruption in the anticipated lane behavior during counter flow. As a result, we have devised various scenarios to meticulously analyze the factors contributing to both deadlock formation and its subsequent performance. This involves manipulating parameters such as speed, speed dispersion, pedestrian count, initial density, right-hand preference weight, minimum personal space size, same-direction following intensity, and time step. The findings hold the potential to enhance the overall quality of service in stairway movement and offer significant contributions to the understanding and management of pedestrian behavior in such settings.

Pedestrian evacuations with imitation of cooperative behavior

We analyze the dynamics of room evacuation for mixed populations that include both competitive and cooperative individuals through numerical simulations using the social force model. Cooperative agents represent well-trained individuals who know how to behave in order to reduce risks within high-density crowds. We consider that competitive agents can imitate cooperative behavior when they are in close proximity to cooperators. We study the effects of the imitation of cooperative behavior on the duration and safety of evacuations, analyzing evacuation time and other quantities of interest for varying parameters such as the proportions of mixing, the aspect ratio of the room, and the parameters characterizing individual behaviors. Our main findings reveal that the addition of a relatively small number of cooperative agents into a crowd can reduce evacuation time and the density near the exit door, making the evacuation faster and safer despite an increase in the total number of agents. In particular, for long spaces such as corridors, a small number of added cooperative agents can significantly facilitate the evacuation process. We compare our results with those of systems without imitation and also study the general role of cooperation, providing further analysis for homogeneous populations. Our main conclusions emphasize the potential relevance of training people how to behave in high-density crowds.

Robust spatial self-organization in crowds of asynchronous pedestrians

Human crowds display various self-organized collective behaviours, such as the spontaneous formation of unidirectional lanes in bidirectional pedestrian flows. In addition, parts of pedestrians' footsteps are known to be spontaneously synchronized in one-dimensional, single-file crowds. However, footstep synchronization in crowds with more freedom of movement remains unclear. We conducted experiments on bidirectional pedestrian flows (24 pedestrians in each group) and examined the relationship between collective footsteps and self-organized lane formation. Unlike in previous studies, pedestrians did not spontaneously synchronize their footsteps unless following external auditory cues. Moreover, footstep synchronization generated by external cues disturbed the flexibility of pedestrians' lateral movements and increased the structural instability of spatial organization. These results imply that, without external cues, pedestrians marching out of step with each other can efficiently self-organize into robust structures. Understanding how asynchronous individuals contribute to ordered collective behaviour might bring innovative perspectives to research fields concerned with self-organizing systems.

Can self-propelled objects escape from compression stimulation?

We studied circular papers impregnated with camphor (CPs) and CPs with magnets (MCPs) as self-propelled objects floating on water under the compression of the water surface as an inanimate system for evacuation in an emergency. Two water chambers-Cin and Cout-were connected via a plastic gate, and eight CPs or eight MCPs were placed on Cin. We monitored the movement of the CPs or MCPs from Cin to Cout when the gate was opened and the area of Cin (Ain) was decreased using a barrier. When Ain was large, CPs moved stochastically from Cin to Cout while exhibiting random motion. The escape probability from Cin to Cout (P) at time t = 20 s increased with a decrease in Ain, and the rate of increase in P increased depending on the width of the gate (Wg). By contrast, clustering was observed for MCPs. Consequently, P of MCPs was lower than that of CPs. The difference in the surface tension between Cin and Cout (Δγ) increased with a decrease in Ain. P is discussed in relation to Δγ as the driving force for emergencies and the repulsive forces between CPs or attractive forces between MCPs. These results suggest that the repulsive force enhances the self-propulsion of objects towards the gate, that is, as a result, higher values of P are obtained.

Dimensionless numbers reveal distinct regimes in the structure and dynamics of pedestrian crowds

In fluid mechanics, dimensionless numbers like the Reynolds number help classify flows. We argue that such a classification is also relevant for crowd flows by putting forward the dimensionless Intrusion and Avoidance numbers, which quantify the intrusions into the pedestrians' personal spaces and the imminency of the collisions that they face, respectively. Using an extensive dataset, we show that these numbers delineate regimes where distinct variables characterize the crowd's arrangement, namely, Euclidean distances at low Avoidance number and times-to-collision at low Intrusion number. On the basis of these findings, a perturbative expansion of the individual pedestrian dynamics is carried out around the noninteracting state, in quite general terms. Simulations confirm that this expansion performs well in its expected regime of applicability.

Morocco's population contact matrices: A crowd dynamics-based approach using aggregated literature data

Estimation of contact patterns is often based on questionnaires and time-use data. The results obtained using these methods have been used extensively over the years and recently to predict the spread of the COVID-19 pandemic. They have also been used to test the effectiveness of non-pharmaceutical measures such as social distance. The latter is integrated into epidemiological models by multiplying contact matrices by control functions. We present a novel method that allows the integration of social distancing and other scenarios such as panic. Our method is based on a modified social force model. The model is calibrated using data relating to the movements of individuals and their interactions such as desired walking velocities and interpersonal distances as well as demographic data. We used the framework to assess contact patterns in different social contexts in Morocco. The estimated matrices are extremely assortative and exhibit patterns similar to those observed in other studies including the POLYMOD project. Our findings suggest social distancing would reduce the numbers of contacts by 95%. Further, we estimated the effect of panic on contact patterns, which indicated an increase in the number of contacts of 11%. This approach could be an alternative to questionnaire-based methods in the study of non-pharmaceutical measures and other specific scenarios such as rush hours. It also provides a substitute for estimating children's contact patterns which are typically assessed through parental proxy reporting in surveys.

Synchronized rotations of active particles on chemical substrates

Many microorganisms use chemical 'signaling' - a quintessential self-organizing strategy in non-equilibrium - that can induce spontaneous aggregation and coordinated motion. Using synthetic signaling as a design principle, we construct a minimal model of active Brownian particles (ABPs) having soft repulsive interactions on a chemically quenched patterned substrate. The interplay between chemo-phoretic interactions and activity is numerically investigated for a proposed variant of the Keller-Segel model for chemotaxis. Such competition not only results in a chemo-motility-induced phase-separated state, but also results in a new cohesive clustering phase with synchronized rotations. Our results suggest that rotational order can emerge in systems by virtue of activity and repulsive interactions alone without an explicit alignment interaction. These rotations can also be exploited by designing mechanical devices that can generate reorienting torques using active particles.

HyPedSim: A Multi-Level Crowd-Simulation Framework-Methodology, Calibration, and Validation

Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based model to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data are extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimises the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimising computational performance compared to other models.

The Impact of Postures and Moving Directions in Fire Evacuation in a Low-Visibility Environment

Walking speed is a significant aspect of evacuation efficiency, and this speed varies during fire emergencies due to individual physical abilities. However, in evacuations, it is not always possible to keep an upright posture, hence atypical postures, such as stoop walking or crawling, may be required for survival. In this study, a novel 3D passive vision-aided inertial system (3D PVINS) for indoor positioning was used to track the movement of 20 volunteers during an evacuation in a low visibility environment. Participants' walking speeds using trunk flexion, trunk-knee flexion, and upright postures were measured. The investigations were carried out under emergency and non-emergency scenarios in vertical and horizontal directions, respectively. Results show that different moving directions led to a roughly 43.90% speed reduction, while posture accounted for over 17%. Gender, one of the key categories in evacuation models, accounted for less than 10% of the differences in speed. The speeds of participants under emergency scenarios when compared to non-emergency scenarios was also found to increase by 53.92-60% when moving in the horizontal direction, and by about 48.28-50% when moving in the vertical direction and descending downstairs. Our results also support the social force theory of the warming-up period, as well as the effect of panic on the facilitating occupants' moving speed.

How reciprocity impacts ordering and phase separation in active nematics?

Active nematics undergo spontaneous symmetry breaking and show phase separation instability. Within the prevailing notion that macroscopic properties depend only on symmetries and conservation laws, different microscopic models are used out of convenience. Here, we test this notion carefully by analyzing three different microscopic models of apolar active nematics. They share the same symmetry but differ in implementing reciprocal or non-reciprocal interactions, including a Vicsek-like implementation. We show how such subtle differences in microscopic realization determine if the ordering transition is continuous or first order. Despite the difference in the type of phase transition, all three models exhibit fluctuation-dominated phase separation and quasi-long-range order in the nematic phase.

Beyond 'stampedes': Towards a new psychology of crowd crush disasters

The Bethnal Green tube shelter disaster, in which 173 people died, is a significant event in both history and psychology. While notions of 'panic' and 'stampede' have been discredited in contemporary psychology and disaster research as explanations for crowd crushes, Bethnal Green has been put forward as the exception that proves the rule. Alternative explanations for crushing disasters focus on mismanagement and physical factors, and lack a psychology. We analysed 85 witness statements from the Bethnal Green tragedy to develop a new psychological account of crowd disasters. Contrary to the established view of the Bethnal Green disaster as caused by widespread public overreaction to the sound of rockets, our analysis suggests that public perceptions were contextually calibrated to a situation of genuine threat; that only a small minority misperceived the sound; and that therefore, this cannot account for the surge behaviour in the majority. We develop a new model, in which crowd flight behaviour in response to threat is normatively structured rather than uncontrolled, and in which crowd density combines with both limited information on obstruction and normatively expected ingress behaviour to create a crushing disaster.

Irreversibility across a Nonreciprocal PT-Symmetry-Breaking Phase Transition

Nonreciprocal interactions are commonplace in continuum-level descriptions of both biological and synthetic active matter, yet studies addressing their implications for time reversibility have so far been limited to microscopic models. Here, we derive a general expression for the average rate of informational entropy production in the most generic mixture of conserved phase fields with nonreciprocal couplings and additive conservative noise. For the particular case of a binary system with Cahn-Hilliard dynamics augmented by nonreciprocal cross-diffusion terms, we observe a nontrivial scaling of the entropy production rate across a parity-time symmetry breaking phase transition. We derive a closed-form analytic expression in the weak-noise regime for the entropy production rate due to the emergence of a macroscopic dynamic phase, showing it can be written in terms of the global polar order parameter, a measure of parity-time symmetry breaking.

Emergence and collapse of reciprocity in semiautomatic driving coordination experiments with humans

Forms of both simple and complex machine intelligence are increasingly acting within human groups in order to affect collective outcomes. Considering the nature of collective action problems, however, such involvement could paradoxically and unintentionally suppress existing beneficial social norms in humans, such as those involving cooperation. Here, we test theoretical predictions about such an effect using a unique cyber-physical lab experiment where online participants (N = 300 in 150 dyads) drive robotic vehicles remotely in a coordination game. We show that autobraking assistance increases human altruism, such as giving way to others, and that communication helps people to make mutual concessions. On the other hand, autosteering assistance completely inhibits the emergence of reciprocity between people in favor of self-interest maximization. The negative social repercussions persist even after the assistance system is deactivated. Furthermore, adding communication capabilities does not relieve this inhibition of reciprocity because people rarely communicate in the presence of autosteering assistance. Our findings suggest that active safety assistance (a form of simple AI support) can alter the dynamics of social coordination between people, including by affecting the trade-off between individual safety and social reciprocity. The difference between autobraking and autosteering assistance appears to relate to whether the assistive technology supports or replaces human agency in social coordination dilemmas. Humans have developed norms of reciprocity to address collective challenges, but such tacit understandings could break down in situations where machine intelligence is involved in human decision-making without having any normative commitments.

Situational social influence leading to non-compliance with conservation rules

It is well established that the decisions that we make can be strongly influenced by the behaviour of others. However, testing how social influence can lead to non-compliance with conservation rules during an individual's decision-making process has received little research attention. We synthesise advances in understanding of conformity and rule-breaking in individuals and in groups, and take a situational approach to studying the social dynamics and ensuing social identity changes that can lead to non-compliant decision-making. We focus on situational social influence contagion that are copresent (i.e., same space and same time) or trace-based (i.e., behavioural traces in the same space). We then suggest approaches for testing how situational social influence can lead to certain behaviours in non-compliance with conservation rules.

Emergence of intelligent collective motion in a group of agents with memory

Intelligent agents collect and process information from their dynamically evolving neighborhood to efficiently navigate through it. However, agent-level intelligence does not guarantee that at the level of a collective; a common example is the jamming we observe in traffic flows. In this study, we ask: how and when do the interactions between intelligent agents translate to desirable or intelligent collective outcomes? To explore this question, we choose a collective consisting of two kinds of agents with opposing desired directions of movement. Agents in this collective are minimally intelligent: they possess only a single facet of intelligence, viz., memory, where the agents remember how well they were able to travel in their desired directions and make up for their non-optimal past. We find that dynamics due to the agent's memory influences the collective, giving rise to diverse outcomes at the level of the group: from those that are undesirable to those that can be called "intelligent." When memory is short term, local rearrangement of agents leads to the formation of symmetrically jammed arrangements that take longer to unjam. However, when agents remember across longer time-scales, their dynamics become sensitive to small differences in their movement history. This gives rise to heterogeneity in the movement that causes agents to unjam more readily and form lanes.

Brain network communication: concepts, models and applications

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.

Anchoring Effect of an Obstacle in the Silo Unclogging Process

Contrary to the proven beneficial role that placing an obstacle above a silo exit has in clogging prevention, we demonstrate that, when the system is gently shaken, this passive element has a twofold effect in the clogging destruction process. On one side, the obstacle eases the destruction of weak arches, a phenomenon that can be explained by the pressure screening that it causes in the outlet proximities. But on the other side, we discover that the obstacle presence leads to the development of a few very strong arches. These arches, which dominate in the heavy tailed distributions of unclogging times, correlate with configurations where the number of particles contacting the obstacle from below are higher than the average; hence suggesting that the obstacle acts as an anchoring point for the granular packing. This finding may help one to understand the ambiguous effect of obstacles in the bottleneck flow of other systems, such as pedestrians evacuating a room or active matter in general.

Symbiotic dynamics in living liquid crystals

An amalgam of nematic liquid crystals and active matter, referred to as living liquid crystals, is a promising self-healing material with futuristic applications for targeted delivery of information and microcargo. We provide a phenomenological model to study the symbiotic pattern dynamics in this contemporary system using the Toner-Tu model for active matter (AM), the Landau-de Gennes free energy for liquid crystals (LCs), and an experimentally motivated coupling term that favours coalignment of the active and nematic components. Our extensive theoretical studies unfold two novel steady states, chimeras and solitons, with sharp regions of distinct orientational order that sweep through the coupled system in synchrony. The induced dynamics in the passive nematic is unprecedented. We show that the symbiotic dynamics of the AM and LC components can be exploited to induce and manipulate order in an otherwise disordered system.

Parameter estimation from aggregate observations: a Wasserstein distance-based sequential Monte Carlo sampler

In this work, we study systems consisting of a group of moving particles. In such systems, often some important parameters are unknown and have to be estimated from observed data. Such parameter estimation problems can often be solved via a Bayesian inference framework. However, in many practical problems, only data at the aggregate level is available and as a result the likelihood function is not available, which poses a challenge for Bayesian methods. In particular, we consider the situation where the distributions of the particles are observed. We propose a Wasserstein distance (WD)-based sequential Monte Carlo sampler to solve the problem: the WD is used to measure the similarity between the observed and the simulated particle distributions and the sequential Monte Carlo samplers is used to deal with the sequentially available observations. Two real-world examples are provided to demonstrate the performance of the proposed method.

Characterization of superspreaders movement in a bidirectional corridor using a social force model

During infectious disease outbreaks, some infected individuals may spread the disease widely and amplify risks in the community. People whose daily activities bring them in close proximity to many others can unknowingly become superspreaders. The use of contact tracking based on social networks, GPS, or mobile tracking data can help to identify superspreaders and break the chain of transmission. We propose a model that aims at providing insight into risk factors of superspreading events. Here, we use a social force model to estimate the superspreading potential of individuals walking in a bidirectional corridor. First, we applied the model to identify parameters that favor exposure to an infectious person in scattered crowds. We find that low walking speed and high body mass both increase the expected number of close exposures. Panic events exacerbate the risks while social distancing reduces both the number and duration of close encounters. Further, in dense crowds, pedestrians interact more and cannot easily maintain the social distance between them. The number of exposures increases with the density of person in the corridor. The study of movements reveals that individuals walking toward the center of the corridor tend to rotate and zigzag more than those walking along the edges, and thus have higher risks of superspreading. The corridor model can be applied to designing risk reduction measures for specific high volume venues, including transit stations, stadiums, and schools.

Fish evacuate smoothly respecting a social bubble

Crowd movements are observed among different species and on different scales, from insects to mammals, as well as in non-cognitive systems, such as motile cells. When forced to escape through a narrow opening, most terrestrial animals behave like granular materials and clogging events decrease the efficiency of the evacuation. Here, we explore the evacuation behavior of macroscopic, aquatic agents, neon fish, and challenge their gregarious behavior by forcing the school through a constricted passage. Using a statistical analysis method developed for granular matter and applied to crowd evacuation, our results clearly show that, unlike crowds of people or herds of sheep, no clogging occurs at the bottleneck. The fish do not collide and wait for a minimum waiting time between two successive exits, while respecting a social distance. When the constriction becomes similar to or smaller than their social distance, the individual domains defined by this cognitive distance are deformed and fish density increases. We show that the current of escaping fish behaves like a set of deformable 2D-bubbles, their 2D domain, passing through a constriction. Schools of fish show that, by respecting social rules, a crowd of individuals can evacuate without clogging, even in an emergency situation.

Exploring the criticality hypothesis using programmable swarm robots with Vicsek-like interactions

A widely mentioned but not experimentally confirmed view (known as the 'criticality hypothesis') argues that biological swarm systems gain optimal responsiveness to perturbations and information processing capabilities by operating near the critical state where an ordered-to-disordered state transition occurs. However, various factors can induce the ordered-disordered transition, and the explicit relationship between these factors and the criticality is still unclear. Here, we present an experimental validation for the criticality hypothesis by employing real programmable swarm-robotic systems (up to 50 robots) governed by Vicsek-like interactions, subject to time-varying stimulus-response and hazard avoidance. We find that (i) not all ordered-disordered motion transitions correspond to the functional advantages for groups; (ii) collective response of groups is maximized near the critical state induced by alignment weight or scale rather than noise and other non-alignment factors; and (iii) those non-alignment factors act to highlight the functional advantages of alignment-induced criticality. These results suggest that the adjustability of velocity or directional coupling between individuals plays an essential role in the acquisition of maximizing collective response by criticality. Our results contribute to understanding the adjustment strategies of animal interactions from a perspective of criticality and provide insights into the design and control of swarm robotics.

Flow rate resonance of actively deforming particles

Lymphoid organs are unusual multicellular tissues: they are densely packed, but the lymphocytes trafficking through them are actively moving. We hypothesize that the intriguing ability of lymphocytes to avoid jamming and clogging is in part attributable to the dynamic shape changes that cells undergo when they move. In this work, we test this hypothesis by investigating an idealized system, namely, the flow of self-propelled, oscillating particles passing through a narrow constriction in two dimensions (2D), using numerical simulations. We found that deformation allows particles with these properties to flow through a narrow constriction in conditions when non-deformable particles would not be able to do so. Such a flowing state requires the amplitude and frequency of oscillations to exceed threshold values. Moreover, a resonance leading to the maximum flow rate was found when the oscillation frequency matched the natural frequency of the particle related to its elastic stiffness. To our knowledge, this phenomenon has not been described previously. Our findings could have important implications for understanding and controlling flow in a variety of systems in addition to lymphoid organs, such as granular flows subjected to vibration.

The spatial effect of digital economy on public psychological resilience during the diffusive crisis

Purpose

To explore whether the digital economy has spatial effects and spatial heterogeneity on public psychological resilience during the diffusive crisis and to analyze the specific impact mechanisms.

Methods

This study is based on the Baidu Search Index from 2011 to 2020 and the provincial panel data of 30 provinces in China. It constructs measures of public psychological resilience and digital economy development level and employs the spatial Durbin model to empirically analyze the relationship between the two, revealing their spatial impact.

Results

(1) Public psychological resilience exhibits a spatial distribution characterized by high values in the west, medium values in the central region, and low values in the east, while the digital economy development level shows a "U"-shaped spatial structure with high levels in the eastern and western regions and low levels in the middle; (2) The digital economy development level in a local region has a negative effect on the public psychological resilience of that region, while the digital economy development level in surrounding regions has a positive spatial spillover effect on the local region's public psychological resilience.

Conclusion

It is essential to strengthen crisis management, focus on the coordinated development of the digital economy in different regions, share the benefits of digital society development more equitably and broadly, and further improve the psychological resilience of regions under the context of digital economy development.

Functional Objects in Urban Walking Environments and Pedestrian Trajectory Modelling

Functional objects are large and small physical entities installed in urban environments to offer specific functionalities to visitors, such as shops, escalators, and information kiosks. Instances of the novel notion are focal points of human activities and are significant in pedestrian movement. Pedestrian trajectory modelling in an urban scene is a challenging problem because of the complex patterns resulting from social interactions of the crowds and the diverse relation between pedestrians and functional objects. Many data-driven methods have been proposed to explain the complex movements in urban scenes. However, the methods considering functional objects in their formulation are rare. This study aims to reduce the knowledge gap by demonstrating the importance of pedestrian-object relations in the modelling task. The proposed modelling method, called pedestrian-object relation guided trajectory prediction (PORTP), uses a dual-layer architecture that includes a predictor of pedestrian-object relation and a series of relation-specific specialized pedestrian trajectory prediction models. The experiment findings indicate that the inclusion of pedestrian-object relation results in more accurate predictions. This study provides an empirical foundation for the novel notion and a strong baseline for future work on this topic.

Heterogeneous Crowd Simulation Using Parametric Reinforcement Learning

Agent-based synthetic crowd simulation affords the cost-effective large-scale simulation and animation of interacting digital humans. Model-based approaches have successfully generated a plethora of simulators with a variety of foundations. However, prior approaches have been based on statically defined models predicated on simplifying assumptions, limited video-based datasets, or homogeneous policies. Recent works have applied reinforcement learning to learn policies for navigation. However, these approaches may learn static homogeneous rules, are typically limited in their generalization to trained scenarios, and limited in their usability in synthetic crowd domains. In this article, we present a multi-agent reinforcement learning-based approach that learns a parametric predictive collision avoidance and steering policy. We show that training over a parameter space produces a flexible model across crowd configurations. That is, our goal-conditioned approach learns a parametric policy that affords heterogeneous synthetic crowds. We propose a model-free approach without centralization of internal agent information, control signals, or agent communication. The model is extensively evaluated. The results show policy generalization across unseen scenarios, agent parameters, and out-of-distribution parameterizations. The learned model has comparable computational performance to traditional methods. Qualitatively the model produces both expected (laminar flow, shuffling, bottleneck) and unexpected (side-stepping) emergent qualitative behaviours, and quantitatively the approach is performant across measures of movement quality.

Bio-Inspired Autonomous Navigation and Formation Controller for Differential Mobile Robots

This article proposes a decentralized controller for differential mobile robots, providing autonomous navigation and obstacle avoidance by enforcing a formation toward trajectory tracking. The control system relies on dynamic modeling, which integrates evasion forces from obstacles, formation forces, and path-following forces. The resulting control loop can be seen as a dynamic extension of the kinematic model for the differential mobile robot, producing linear and angular velocities fed to the mobile robot's kinematic model and thus passed to the low-level wheel controller. Using the Lyapunov method, the closed-loop stability is proven for the non-collision case. Experimental and simulated results that support the stability analysis and the performance of the proposed controller are shown.

How do classroom-turnover times depend on lecture-hall size?

Academic spaces in colleges and universities span classrooms for 10 students to lecture halls that hold over 600 people. During the break between consecutive classes, students from the first class must leave and the new class must find their desks, regardless of whether the room holds 10 or 600 people. Here we address the question of how the size of large lecture halls affects classroom-turnover times, focusing on non-emergency settings. By adapting the established social-force model, we treat students as individuals who interact and move through classrooms to reach their destinations. We find that social interactions and the separation time between consecutive classes strongly influence how long it takes entering students to reach their desks, and that these effects are more pronounced in larger lecture halls. While the median time that individual students must travel increases with decreased separation time, we find that shorter separation times lead to shorter classroom-turnover times overall. This suggests that the effects of scheduling gaps and lecture-hall size on classroom dynamics depends on the perspective-individual student or whole class-that one chooses to take.

Modelling physical contacts to evaluate the individual risk in a dense crowd

Tumble and stampede in a dense crowd may be caused by irrational behaviours of individuals and always troubles the safety management of crowd activities. Risk evaluation based on pedestrian dynamical models can be regarded as an effective method of preventing crowd disasters. Here, a method depending on a combination of collision impulses and pushing forces was used to model the physical contacts between individuals in a dense crowd, by which the acceleration error during physical contacts caused by a traditional dynamical equation can be avoided. The human domino effect in a dense crowd could be successfully reproduced, and the crushing and trampling risk of a microscopic individual in a crowd could be quantitatively evaluated separately. This method provides a more reliable and integral data foundation for evaluating individual risk that shows better portability and repeatability than macroscopic crowd risk evaluation methods and will also be conducive to preventing crowd disasters.

Agent-based modeling of mass shooting case with the counterforce of policemen

Mass shooting cases have caused large casualties worldwide. The counterforce, such as the policemen, is of great significance to reducing casualties, which is the core issue of social safety governance. Therefore, we model both the killing force and counterforce, to explore the crowd dynamics under the shooting. Taking the “Borderline” shooting in 2018 as the target case, the agent-based modeling is applied to back-calculate this dynamic process and explore key behavior rules of individuals. The real death tolls of three classes of agents (civilians, policemen, & killers) are as the real function, based on which we calculate the gaps between real target case and simulations. Eventually, we obtain three optimal solutions, which achieve the least gap or highest matching degree. Besides, we make counterfactual inferences under the optimal solutions, to explore the strategic interactions between policemen and killers. For strategies of killers, we explore different sizes, positions, and moving patterns of the killers. The optimal size of policemen is four to five, for each one killer. For strategies of policemen, we explore the size, locations, and the response time. It indicates that optimal response time of policemen is thirty to forty shots of the killer, and the death of civilians and policemen can be minimized, and the death probability of the killer can be maximized. These findings help to improve public safety governance for our cities. To effectively deal with sudden shooting terrorist cases, patrol routes, reasonable settings, and swift dispatches of the police (stations) should be considered.

Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation

Communication between gray matter regions underpins all facets of brain function. We study inter-areal communication in the human brain using intracranial EEG recordings, acquired following 29,055 single-pulse direct electrical stimulations in a total of 550 individuals across 20 medical centers (average of 87 ± 37 electrode contacts per subject). We found that network communication models-computed on structural connectivity inferred from diffusion MRI-can explain the causal propagation of focal stimuli, measured at millisecond timescales. Building on this finding, we show that a parsimonious statistical model comprising structural, functional, and spatial factors can accurately and robustly predict cortex-wide effects of brain stimulation (R²=46% in data from held-out medical centers). Our work contributes toward the biological validation of concepts in network neuroscience and provides insight into how connectome topology shapes polysynaptic inter-areal signaling. We anticipate that our findings will have implications for research on neural communication and the design of brain stimulation paradigms.