Pedestrian Crowds Research Articles

Social groups in pedestrian crowds: review of their influence on the dynamics and their modelling

Pedestrians are often encountered walking in the company of some social relations, rather than alone. The social groups thus formed, in variable proportions depending on the context, are not randomly organised but exhibit distinct features, such as the well-known tendency of 3-member groups to be arranged in a V-shape. The existence of group structures might thus impact the collective dynamics of the crowd, possibly in a critical way when emergency situations are considered. After turning a blind eye to these group aspects for years, endeavours to model groups in crowd simulation software have thrived in the past decades. This fairly short review opens on a description of their empirical characteristics and their (more controversial) impact on the global flow. Then, it aims to offer a pedagogical discussion of the main strategies to model such groups, within different types of models, in order to provide guidance for prospective modellers.

Model-based assessment of the risks of viral transmission in non-confined crowds

This work assesses the risks of Covid-19 spread in diverse daily-life situations involving crowds of maskless pedestrians, mostly outdoors. More concretely, we develop a method to infer the global number of new infections from patchy observations, by coupling ad hoc spatial models for disease transmission via respiratory droplets to detailed field-data about pedestrian trajectories and head orientations. This allows us to rank the investigated situations by the infection risks that they present; importantly, the obtained hierarchy of risks is very largely conserved across transmission models: Street cafés present the largest average rate of new infections caused by an attendant, followed by busy outdoor markets, and then metro and train stations, whereas the risks incurred while walking on fairly busy streets are comparatively quite low. While our models only approximate the actual transmission risks, their converging predictions lend credence to these findings. In situations with a moving crowd, density is the main factor influencing the estimated infection rate. Finally, our study explores the efficiency of street and venue redesigns in mitigating the viral spread: While the benefits of enforcing one-way foot traffic in (wide) walkways are unclear, changing the geometry of queues substantially affects disease transmission risks.

Investigating the influence of a cyclist on crowd behaviors on a shared road

This study investigated the influence of a cyclist on crowd behaviors on a shared road. A series of mixed traffic experiments of a cyclist and a crowd of pedestrians were conducted in a circular wide track under different densities and moving directions. The influence of the cyclist on the fundamental diagram of pedestrians showed a clear difference under unidirectional and bidirectional scenarios. The cyclist restricted the pedestrian flow evidently in the unidirectional flow, while in bidirectional flow, a critical density around 0.9 ped m−2 was observed, where the restriction on the motion of the crowd was not noticeable unless the critical density was reached. The speed of the cyclist also depended on the relative moving direction, but the discrepancy became negligible when the following effect was eliminated. The lane formation was quantitatively analyzed with Otsu’s method. The results showed that the formed lanes were more concentrated in bidirectional flow than unidirectional flow. When two lanes were formed, the distances between the lanes were larger in unidirectional flow. These differences could be explained by the cognition difference of pedestrians in mixed traffic under unidirectional and bidirectional scenarios. It was observed that a major effect of the cyclist on the crowd behavioral mechanism was the generation of backward propagated density waves. A Y-shaped pattern for the formation of high-density clusters was discovered, which implied that stable high-density clusters were likely to form as the forward propagated and backward propagated density waves merged. Moreover, in bidirectional flow, the generated backward propagated density wave could also promote the fading of stable high-density clusters by reducing the heterogeneity of density distribution. Our study helps to achieve a better understanding and modeling of the dynamics of pedestrian–cyclist in mixed traffic.

Parameter Calibration of a Social Force Model for the Crowd-Induced Vibrations of Footbridges

A reliable prediction of the human-induced vibrations of footbridges relies on an accurate representation of the pedestrian excitation for different loading scenario. Particularly, the modeling of crowd-induced dynamic loading is a critical issue for the serviceability assessment of footbridges. At the design stage, the modeling of crowd loading is often derived from single pedestrian models, neglecting the effect of the structural vibrations as well as the interactions among pedestrians. A detailed description of the crowd behavior can be achieved employing a social force model that describes the different influences affecting individual pedestrian motion. These models are widely adopted to describe the crowd behavior especially in the field of evacuation of public buildings, public safety and transport station management while applications in the serviceability assessment of footbridges are less common. To simulate unidirectional pedestrian flows on footbridges, this paper proposes a parameter calibration of the Helbing’s social force model performed adopting the response surface methodology. Parameters of the social force model are calibrated so as to represent the fundamental relation between mean walking speed and density of the pedestrian crowd. The crowd-induced vibrations are then simulated by modeling each pedestrian in the crowd as a vertical load that crosses the footbridge with time varying trajectory and velocity estimated from the calibrated social force model. Finally, results are compared to those obtained from a multiplication factor approach proposed in literature. This considers the crowd as a uniform distribution of pedestrians with constant speed and given synchronization level and the footbridge response is evaluated as the response to a single pedestrian scaled by a proper enhancement factor.

Disease contagion models coupled to crowd motion and mesh-free simulation

Modeling and simulation of disease spreading in pedestrian crowds have recently become a topic of increasing relevance. In this paper, we consider the influence of the crowd motion in a complex dynamical environment on the course of infection of the pedestrians. To model the pedestrian dynamics, we consider a kinetic equation for multi-group pedestrian flow based on a social force model coupled with an Eikonal equation. This model is coupled with a non-local SEIS contagion model for disease spread, where besides the description of local contacts, the influence of contact times has also been modeled. Hydrodynamic approximations of the coupled system are derived. Finally, simulations of the hydrodynamic model are carried out using a mesh-free particle method. Different numerical test cases are investigated, including uni- and bi-directional flow in a passage with and without obstacles.

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications.

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

Social groups barely change the speed-density relationship in unidirectional pedestrian flow, but affect operational behaviours

Pedestrian crowds are not homogeneous. One common distinguishing characteristic is the membership in social groups based on friendship or families, for example. It is important to understand how these social groups affect pedestrian dynamics to inform their consideration in safety planning. Despite previous work on social groups, there is a paucity of controlled experiments on their effect under normal conditions which is crucial to delineate relative risks to pedestrians in non-emergency situations. To address this, we present results from controlled experiments on the influence of dyadic social groups in unidirectional pedestrian flow. We consider crowds comprised of 100%, 67%, or 0% social groups. Our experimental setting results in crowd densities between 1.25 and 4.0 pedestrians per m2. We find that while the relationship between pedestrian speed and density is affected by the presence of social groups, the size of this effect is smaller than the overall variability of observations. In contrast, we find clear differences in movement dynamics between individuals and social groups. Social groups move at slower speeds and overtake others less frequently. Regardless of pedestrian density, social groups are further away from the next-closest pedestrian than individuals are, suggesting there is a larger empty space around social groups. These findings suggest when social groups need to be considered in safety management and modelling under normal conditions. At a macroscopic level, described by speed-density relationships, social groups need not be considered explicitly but when considering a microscopic level, including overtaking and personal space, they should be considered explicitly.

Electric bike navigation comfort in pedestrian crowds

The emergence of electric bikes (e-bikes) has brought a paradigm shift in shared mobility with a promise to move towards the mission of sustainable cities. Whereas an in-depth understanding of e-bike riding characteristics is crucial to effectively design the infrastructure for active mobility, it remains an open area of research. We take the first step towards modelling the e-bike navigation comfort in pedestrian crowds. Through a laboratory-controlled field experiment, we collect trajectories of e-bike riders under different pedestrian crowding levels in both opposite- (meeting) and same-direction (passing) encounters. For each trajectory, we obtain e-bike speed, e-bike lateral distance, and pedestrian crowding after processing the data obtained from four stationary cameras. Considering the riding comfort as a latent variable, we adopt a Bayesian network to represent the relationship between observed and the latent variables. Subsequently, we use fundamental principles of conditional probability to identify the causal effect of pedestrian crowding on e-bike riding comfort. Controlling for the demographic heterogeneity, we also estimate the relationship between the comfort of an e-bike rider, pedestrian crowding, and her riding characteristics (e.g., speed and lateral distance). The results of this study would guide policymakers in ex-ante evaluations of the infrastructure decisions for active mobility.

Application of a multi-objective multi traveling salesperson problem with time windows

The pilgrimage to Mecca, which is called Hajj, is the largest annual pedestrian crowd management problem in the world. During the Hajj, the pilgrims are accommodated in camps. For safety reasons, exact times and directions are given to the pilgrims who are moving between holy sites. Despite the importance of complying with those schedules, violations can often be conjectured. Directing a small workforce between the camps to monitor the pilgrims’ compliance with the schedule is an important matter, which will be dealt with in this paper. A type of multi-objective multiple traveling salesperson optimization problem with time windows is introduced to generate the tours for the employees monitoring the flow of pilgrims at the campsite. Four objectives are being pursued: As many pilgrims as possible (1), should be visited with a preferably small workforce (2), the tours of the employees should be short (3) and employees should have short waiting times between visits (4). A goal programming, an enumeration, Augmecon2 and an interactive approach are developed. The topic of supported and non-supported efficient solutions is addressed by determining all efficient solutions with the enumeration approach. The suitability of the approaches is analyzed in a computational study, while using an actual data set of the Hajj season in 2015. For this application, the interactive approach has been identified as the most suitable approach to support the generation of an offer for the project.

Spontaneous synchronization of motion in pedestrian crowds of different densities.

Interacting pedestrians in a crowd spontaneously adjust their footsteps and align their respective stepping phases. This self-organization phenomenon is known as synchronization. However, it is unclear why and how synchronization forms spontaneously under different density conditions, or what functional benefit synchronization offers for the collective motion of humans. Here, we conducted a single-file crowd motion experiment that directly tracked the alternating movement of both legs of interacting pedestrians. We show that synchronization is most likely to be triggered at the same density at which the flow rate of pedestrians reaches a maximum value. We demonstrate that synchronization is established in response to an insufficient safety distance between pedestrians, and that it enables pedestrians to realize efficient collective stepping motion without the occurrence of inter-person collisions. These findings provide insights into the collective motion behaviour of humans and may have implications for understanding pedestrian synchronization-induced wobbling, for example, of bridges.

Propagation Measurement of a Pedestrian Tunnel at 24 GHz for 5G Communications

In this paper, we report the results of a field measurement campaign carried out inside a pedestrian tunnel at 24 GHz in two conditions, namely, empty tunnel scenario and busy tunnel scenario with pedestrian movement. The experiment measures the fading effects of various groups of pedestrian crowds using directional antennas at the transmitter and receiver for millimeter-wave radio communications. Having presented and analyzed the measurement data in several diverse scenarios, we have further investigated human scattering effects in the crowded pedestrian tunnel and performed ray-tracing simulation for an empty pedestrian tunnel condition. Because tunnel is an enveloped scenario that is not bound by any geographic areas, the results of this study can be applied to a wider scenario like other pedestrian tunnels across the globe. Above all, these findings contribute towards ensuring wireless connectivity for everyone even in a remote scenario like underground passages.

The value pedestrians attribute to environmental information diminishes in route choice sequences

Suppose someone wants to walk from one location to another location. In doing so, they either follow a pre-planned route or decide which way to go each time they reach a junction. Research into pedestrian dynamics has carefully studied route planning and single instances of decisions on routes. However, little is known about the decision-making processes in sequences of consecutive pedestrian route choices. Here, we propose the first mathematical model for this situation. Its key hypothesis is that the sensitivity of pedestrians to environmental information, such as signs or the movement of others, diminishes, the more decisions they make in sequence. To validate our model, we conduct a virtual reality experiment with over 200 participants. Our findings suggest that sensitivity to information diminishes for the experimental case when only information on the movement of others is available to pedestrians. Comparison of our model to situations when signs are present and to data from previously published work suggests that this effect cannot be detected in short sequences of route decisions that contain three or fewer decision points. We demonstrate the implications of this diminishing sensitivity to environmental information at the example of egress from a metro-station. While the proportion of pedestrians using a signposted shortest route may decrease, following behaviour is also suppressed which may result in more predictable route choice dynamics across pedestrian crowds. We advocate further study on this topic in real-world settings, to investigate the need for appropriate pedestrian route choice management strategies. Our experiment also provides preliminary insights into how different personality traits affect following behaviour and we suggest this opens another avenue for future investigation.

An Improved Simulation Model for Pedestrian Crowd Evacuation

This paper works on one of the most recent pedestrian crowd evacuation models—i.e., “a simulation model for pedestrian crowd evacuation based on various AI techniques”—which was developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it into the model. This method enables the developed model to find a more appropriate evacuation area design regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model’s output—i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees’ evacuation times of each exit door location; then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs.

A study of group effects in pedestrian crowd evacuation: Experiments, modelling and simulation

The presence of social groups in pedestrian crowds can have a substantial effect on the overall evacuation process. However, crowds are mostly simplified as a collection of individuals, and the influence of grouping has not been widely considered in pedestrian crowd simulations. In this study, a novel model that accounts for social groups was implemented based on a social force framework to investigate evacuation dynamics in pedestrian crowds. The associated model parameters were calibrated via two group experiments to provide more credible and realistic simulation results. The effects of certain variables (e.g., group size, desired speed, exit width) on the overall evacuation performance and local group behaviour were investigated. The simulation results indicate that group effects facilitated overall crowd evacuation, especially when the exit was wide. The total evacuation time of a pedestrian crowd was shorter when the groups within the crowd were larger. The time intervals for groups of various sizes did not show a significant difference. The group shape was approximated as a rectangle, and the group features were therefore quantified by shape-related parameters (e.g., aspect ratio and area). The rectangle was shown to orient itself along the moving direction. An elongated configuration of the group became increasingly notable when the leader–follower relationship intensified, resulting in a queue-like formation that facilitated the overall evacuation. A certain trend was found that showed an increase in the aspect ratio and a decrease in the normalised area over time. The larger-sized group often seemed to have a smaller aspect ratio but required more space per person during movement. This study deepens the understanding of crowd evacuation dynamics featuring social groups.

Incentive-based experiments to characterize pedestrians’ evacuation behaviors under limited visibility

Pedestrian evacuation dynamics under near zero or limited visibility conditions are usually different from normal evacuation conditions. In previous studies, the competitiveness of evacuees under limited visibility condition is mainly controlled by stimulating their physical and psychological states during evacuation, and the monetary incentive effect has not been investigated. This study aims to investigate the use of monetary incentive to mimic different emergency levels and the resulting effects in evacuation process under limited visibility condition. Two connected areas (7 m × 5 m) enclosed by an iron fence are set up for the single-exit evacuation experiment. A total of 18 evacuation experiments with 30 participants with limited vision were conducted. By controlling different amounts of monetary incentives as different emergency levels, the position of the connecting door in the middle or the edge of two fences and the opening of the exit of one room in two rooms were randomly selected as control variables to reduce the experimental behavior. The results show that the emergency level controlled by monetary incentive has a significant influence on evacuation efficiency. With the increase in incentive level, evacuation time of the participants generally decreased. The emergency level not only improved pedestrian’s walking speed by improving pedestrian’s urgency for evacuation, but also had a significant influence on pedestrian’s route choice. These results suggest that improving the incentive level can reduce the randomness and hysteresis of participants’ path and route selection decision. Findings of this study are valuable resources for proactive pedestrian crowd management under adverse conditions.

Scale and density invariant head detection deep model for crowd counting in pedestrian crowds

Crowd counting in high density crowds has significant importance in crowd safety and crowd management. Existing state-of-the-art methods employ regression models to count the number of people in an image. However, regression models are blind and cannot localize the individuals in the scene. On the other hand, detection-based crowd counting in high density crowds is a challenging problem due to significant variations in scales, poses and appearances. The variations in poses and appearances can be handled through large capacity convolutional neural networks. However, the problem of scale lies in the heart of every detector and needs to be addressed for effective crowd counting. In this paper, we propose a end-to-end scale invariant head detection framework that can handle broad range of scales. We demonstrate that scale variations can be handled by modeling a set of specialized scale-specific convolutional neural networks with different receptive fields. These scale-specific detectors are combined into a single backbone network, where parameters of the network is optimized in end-to-end fashion. We evaluated our framework on challenging benchmark datasets, i.e., UCF-QNRF, UCSD. From experiment results, we demonstrate that proposed framework beats existing methods by a great margin.

Agent Based Modelling and Simulation of Pedestrian Crowds in Panic Situations

The increasing occurrence of panic stampedes during mass events has motivated studying the impact of panic on crowd dynamics. Understanding the collective behaviors of panic stampedes is essential to reducing the risk of deadly crowd disasters. In this work, we use an agent-based formulation to model the collective human behavior in such crowd dynamics. We investigate the impact of panic behavior on crowd dynamics, as a specific form of collective behavior, by introducing a contagious panic parameter. The proposed model describes the intensity and spread of panic through the crowd. The corresponding panic parameter impacts each individual to represent a different variety of behaviors that can be associated with panic situations such as escaping danger, clustering, and pushing. Simulation results show contagious panic and pushing behavior, resulting in a more realistic crowd dynamics model.

When diffusion faces drift: Consequences of exclusion processes for bi-directional pedestrian flows

Stochastic particle-based models are useful tools for describing the collective movement of large crowds of pedestrians in crowded confined environments. Using descriptions based on the simple exclusion process, two populations of particles, mimicking pedestrians walking in a built environment, enter a room from two opposite sides. One population is passive — being unaware of the local environment; particles belonging to this group perform a symmetric random walk. The other population has information on the local geometry in the sense that as soon as particles enter a visibility zone, a drift activates them. Their self-propulsion leads them towards the exit. This second type of species is referred here as active. The assumed crowdedness corresponds to a near-jammed scenario. The main question we ask in this paper is: Can we induce modifications of the dynamics of the active particles to improve the outgoing current of the passive particles? To address this question, we compute occupation number profiles and currents for both populations in selected parameter ranges. Besides observing the more classical faster-is-slower effect, new features appear as prominent like the non-monotonicity of currents, self-induced phase separation within the active population, as well as acceleration of passive particles for large-drift regimes of active particles.

Agent-based modelling of pedestrian responses during flood emergency: mobility behavioural rules and implications for flood risk analysis

AbstractAn agent-based model (ABM) for simulating the interactions between flooding and pedestrians is augmented to more realistic model responses of evacuees during floodwater flow. In this version of the ABM, the crowd of pedestrians have different body heights and weight, and extra behavioural rules are added to incorporate pedestrians’ states of stability and walking speed in floodwater. The augmented ABM is applied to replicate an evacuation scenario for a synthetic test case of a flooded shopping centre. Simulation runs are performed with increasingly sophisticated configuration modes for the pedestrians’ behavioural rules. Simulation results are analysed based on spatial and temporal indicators informing on the dynamic variations of the flood risk states of the flooded pedestrians, i.e. in terms of a commonly used flood Hazard Rating (HR) metric, variable walking speed, and instability due to toppling and/or sliding. Our analysis reveals significantly prolonged evacuation times and risk exposure levels as the stability and walking speed behavioural rules become more sophisticated. Also, it allows us to identify more conservative HR thresholds of pedestrian instability in floodwater, and a new formula relating walking speed states to the HR for stable pedestrians in floodwater. Accompanying details for software accessibility are provided.

Aversion of Pedestrians to Face-to-Face Situations Eases Crowding

We conducted numerical simulation for a crowd of pedestrians. Each pedestrian, modeled with three circles, has a shape whose long axis is perpendicular to the anteroposterior axis, and is designed to move fixed destination. The pedestrians have friction at the surface and soft repulsion. In this study, we newly introduced an active rotation which captures psychological effect to evade face-to-face situation. The numerical simulation revealed that active rotation induces fluidization of system leading to higher flux of pedestrian. We further confirmed that this fluidization is due to fragmentation of force chain induced by the active rotation.