Pedestrian Crowds Research Articles

Unveiling the Hidden Dimension of Pedestrian Crowds: Introducing Personal Space and Crowding into Simulations

Models for the automated analysis and simulation of the complex phenomena observable in built environment crowded by pedestrians have been studied for over thirty years. Nonetheless, one of the commonly agreed upon rules guiding regulation of distance among pedestrian, i.e. proxemics, was defined a nd discussed in static settings, whereas scenarios of interest generally deal with individual and collective movements in crowds. The present paper presents a systemic perspective on the research aimed at defining a dynamic form of proxemics. The paper firstly reports the results of an experiment focused on proxemics and pedestrians personal space, as the hidden dimension of human spatial behavior in crowded environments. We propose a representation of personal space through discrete potentials and an innovative crowding estimation method (i.e. Cumulative Mean Crowding), going beyond simple perceived density evaluation. The experimental setting is introduced and applied to appraise the potential impact of this novel pedestrian perception mechanism on innovative simulation models.

Hierarchical Agent-Based Modeling for Improved Traffic Routing

Agent-based model (ABM) simulation is a bottom–up approach that can describe the phenomena generated from actions and interactions within a multiagent system. An ABM is an improvement over model simulations which only describe the global behavior of a system. Therefore, it is an appropriate technology to analyze emergent phenomena in social sciences and complex adaptive systems such as vehicular traffic and pedestrian crowds. In this paper, a hybrid agent-based modeling framework designed to automate decision-making processes during traffic congestion is proposed. The model provides drivers with real-time alternative routes, computed via a decentralized multi-agent model, that tries to achieve a system-optimal traffic distribution within an entire system, thus reducing the total travel time of all the drivers. The presented work explores a decentralized ABM technique on an autonomous microgrid that is represented through cellular automata (CA). The proposed model was applied to high-density traffic congestion events such as car accidents or lane closures, and its effectiveness was analyzed. The experimental results confirm the efficiency of the proposed model in not only accurately simulating the driver behaviors and improving vehicular traffic flows during congestion but also by suggesting changes to traffic dynamics during the simulations, such as avoiding obstacles and high-density areas and then selecting the best alternative routes. The simulation results validate the ability of the proposed model and the included decision-making sub-models to both predict and improve the behaviors and intended actions of the agents.

Influence of different merging angles of pedestrian flows on evacuation time

The growing number of fires and other types of catastrophes occurring at large events highlights the need to rethink safety concepts and also to include new ways to optimize buildings and venues where events are held. Although there have been some attempts to model and simulate the movement of pedestrian crowds, little knowledge has been gathered to better understand the impact of the built environment and its geometric characteristics on the crowd dynamics. This paper presents computer simulations about pedestrians’ crowd dynamics that were conducted based on the Social Force Model. The influence of different configurations of pedestrian flows merging during emergency evacuations was investigated. In this study, 12 designs with different merging angles were examined, simulating the evacuation of 400 people in each scenario. The Planung Transport Verkehr (PTV, German for Planning Transport Traffic) Viswalk module of the PTV Vissim software (PTV Group, Karlsruhe, Germany) program was adopted, which allows the employment of the Social Force approach. The results demonstrate that both symmetric and asymmetric scenarios are sensitive to the angles of convergence between pedestrian flows.

An Intelligence-Based Approach for Prediction of Microscopic Pedestrian Walking Behavior

This paper focuses on locally microscopic pedestrian walking behavior and proposes an intelligent behavioral learning approach for its prediction. In this approach, pedestrian walking behavior was modeled as a special artificial neural network whose input and output layers were used to accommodate a pedestrian’s perceived environmental information (e.g., information on the destination, obstacles, and neighbors) and his or her walking behavioral response, respectively. The developed neural network was trained based on a large volume of data samples of real-life pedestrian walking behavior (3813 training samples) to acquire knowledge of pedestrian walking behavior and to develop the ability to predict microscopic pedestrian walking behavior. A quantitative evaluation index, R-squared, was calculated to evaluate the learning performance; the mean was calculated as 0.900, which indicates that the neural network can well capture the underlying decision-making mechanism behind pedestrian walking behavior. The approach was verified by the prediction of microscopic pedestrian walking behavior details in two real-life scenarios. The vector displacement error and the speed error were calculated to evaluate the quality of the prediction; the mean of the vector displacement error (the speed error) in two scenarios were, respectively, calculated as 0.192 (0.116) and 0.226 (0.096), which indicates that the prediction results were acceptable from an engineering perspective. Based on these results, we consider the developed approach to be capable of predicting microscopic pedestrian walking behavior. Moreover, extended application also shows that the proposed approach has promise for simulation of the short-term flow of a pedestrian crowd.

Congestion Evaluation of Pedestrians in Metro Stations Based on Normal-Cloud Theory

This study aims at evaluating the congestion level of pedestrians in metro stations. Twelve hours (4 h × three facilities) of video data were collected in the channel, stairway, and platform in a metro station in the city of Ningbo, China. The indicator of GPC (grade of pedestrian crowd) was proposed to quantify the congestion level of pedestrians. Four levels of congestion (level I, level II, level III, and level IV) were determined based on the GPC. A normal-cloud (NC) model was proposed and calibrated for the evaluation of three facilities including channel, stairway, and platform. The evaluation results showed that the GPC of L1-L2 and L2-L1 in channel are level II and level I, respectively. The GPC of upward and downward of stairway are level III and level I. The GPC of platform is level IV. Crowd management countermeasures were proposed for the management of pedestrians in metro station.

Experimental study of architectural adjustments on pedestrian flow features at bottlenecks

Crowd dynamics has been attracting more and more attention in the past decades because of the serious accidents occurred within pedestrian crowds, especially near bottlenecks. In this paper, pedestrian flow features at bottlenecks, i.e. room exits, are investigated with human-experiments considering varying door sizes and locations. The measure of evacuation time all indicated that lager door widths showed higher egress efficiency. Our analyses showed that the time lapses between two successive pedestrians displayed heavy-tailed distribution in all the scenarios studied. Meanwhile, regardless of the physical setups, faster flow rate and greater burst sizes with the increasing door width were found. Moreover, the influence of the boundary layer, as well as the effective width on pedestrian crowd dynamics were clearly observed. Our results also demonstrated that the door position also has an effect on the pedestrian flow at bottlenecks. In narrow door scenarios, the specific capacity was continuously decreased from the middle exit scenarios to the corner exit scenarios. As the door width increased, this gap gradually disappeared.

Congestion detection in pedestrian crowds using oscillation in motion trajectories

Identifying crowd congestion in high density pedestrian crowds is a challenging problem, with substantial interest for safety and security applications. We present a method to identify and localize congestion in high density crowd videos, based on the characteristic motion of individuals stuck in congestion. Pedestrians who cannot move freely due to congestion tend to undergo lateral oscillations. Using only motion features, extracted from optical flow and particle advection, we generate sampled trajectories and compute areas which show a pattern of increasing trajectory oscillation. A new and first ever data set including 15 different crowd scenes is introduced to better evaluate the accuracy of our method. Showing results on a diversity of challenging scenarios, we both qualitatively and quantitatively show that this approach provides accurate detection and excellent localization of congestion events.

Traffic flow in a crowd of pedestrians walking at different speeds.

This study investigates motion in a crowd of pedestrians walking at different speeds. Three pedestrian groups are considered (slow walkers, normal walkers, and fast walkers), and we design the experimental condition by mixing the normal walkers with either the slow or the fast walkers to create flows with different speed compositions. All the walkers in this experiment were instructed to walk along a circular course unidirectionally. Fundamental diagrams and multiple regression analysis show that the speed at which a particular pedestrian walks is determined by both the local density and the speed at which the surrounding pedestrians are walking. We also find that the spontaneous lane formation, that occurs in bidirectional flow, does not occur in flow in which the speed is heterogeneous, thereby resulting in a spatial density distribution with large variance. This corresponds to pedestrian clustering, which reduces both the mean speed and the flow rate.

Insights toward efficient angle design of pedestrian crowd egress point bottlenecks

Understanding the impact of geometrical characteristics of bottlenecks in pedestrian egress behaviour is ubiquitous in safe and effective crowd management. This paper studies how funnel-shaped bottlenecks with different angles affect the microscopic and macroscopic properties of pedestrian egress flow. The minimum width of the bottleneck in all experiments was one metre and the egress point was exactly in the middle. Two scenarios were designed and experimented. First, the large width of the bottleneck was constant and the length, and subsequently the slope, was varied. Second, the length of the bottleneck was constant and the large width, and subsequently the slope, was varied. As a result, the impact of length, large width, and slope were examined. The results suggest that the best-performing bottleneck with regard to angle follows a certain trend, with 26.6° being the most efficient. Further, it was observed that upon increasing the large width the flow increases.

Simulating pedestrians’ spatio-temporal distribution in underground spaces

Many pedestrians gather in underground spaces that connect railway stations. Determining pedestrians’ movements is important for evaluating and improving the actual underground space. Using pedestrian traffic survey data, we propose a model for simulating pedestrians’ spatio-temporal distribution (SD) in an underground space. Through simulation, we obtain pedestrians’ time-specific positions in the network, enabling us to study the interactions between people and the underground space with regard to crowds. We define several crowd condition variables as indicators of the degree of relative congestion in terms of safety, efficiency, and comfort, and we examine some environmental and individual factors’ influence on crowd variations. We then construct a method for assessing the degree of relative congestion in underground spaces with dynamic pedestrian crowds and perform two case studies to examine the effects of proposed flow control plans on actual underground pedestrian systems.

Lévy walk process in self-organization of pedestrian crowds.

Similar to other animal groups, human crowds exhibit various collective patterns that emerge from self-organization. Recent studies have emphasized that individuals anticipate their neighbours' motions to seek their paths in dynamical pedestrian flow. This path-seeking behaviour results in deviation of pedestrians from their desired directions (i.e. the direct path to their destination). However, the strategies that individuals adopt for the behaviour and how the deviation of individual movements impact the emergent organization are poorly understood. We here show that the path-seeking behaviour is performed through a scale-free movement strategy called a Lévy walk, which might facilitate transition to the group-level behaviour. In an experiment of lane formation, a striking example of self-organized patterning in human crowds, we observed how flows of oppositely moving pedestrians spontaneously separate into several unidirectional lanes. We found that before (but not after) lane formation, pedestrians deviate from the desired direction by Lévy walk process, which is considered optimal when searching unpredictably distributed resources. Pedestrians balance a trade-off between seeking their direct paths and reaching their goals as quickly as possible; they may achieve their optimal paths through Lévy walk process, facilitating the emergent lane formation.

Characterization of Collision Avoidance in Pedestrian Crowds

The avoidance behavior of pedestrians was characterized in the present paper by simulating the movement of crowds in both unidirectional and bidirectional pedestrian flow. A phase change of alternative lane formation observed in real bidirectional pedestrian flows has been studied, where pedestrians tended to evade individuals in counterflow and simultaneously keep a certain distance from each other in the uniform pedestrian flow when the counterflow disappeared. What is more, the comparison between the effect of evading and pushing behavior on evacuation has been investigated in the room egress scenario. Additionally, the evading and overtaking behavior of fast pedestrians have also been simulated in heterogeneous crowds. The performance of the proposed model was compared to the experimental data and the results obtained using other evacuation models. Numerical results showed that both the phase change of alternative lane formation in bidirectional pedestrian flow and the effective evading behavior in unidirectional pedestrian flow were conductive to reduce the evacuation time of pedestrian crowds. Even though pushing behavior of fast pedestrians seemed to improve the flow through the wide exit, it might lead to the panic and other negative effect on the crowds, such as crowds trample. The proposed model in this paper could provide a theoretical basis for the pedestrian crowd management during emergency evacuation.

Optimizing optimization: accurate detection of hidden interactions in active body systems from noisy data

Given deficient and noisy movement data from a pedestrian crowd—a class of active body systems, is it possible to uncover the hidden group interaction patterns or connections? Yes, it is possible. Here, we develop a general framework based on an optimal combination of the conventional compressive sensing ( $$L_1$$ minimization) and $$L_2$$ optimization procedure to achieve optimal detection of the contact network embedded in pedestrian crowd under the data shortage conditions. Different from previous publications, in our framework, the optimal weights of the $$L_1$$ and $$L_2$$ components in the combination can be determined specifically from the noisy data, which can obtain more accurate detection for the corresponding system. To detect hidden interaction patterns from spatiotemporal data has broader applications, and our optimized compressive sensing-based framework provides a practically viable solution. In addition, we provide a relative entropy perspective to facilitate more general theoretical and technological extensions of the framework.

Stress estimation in pedestrian crowds: Experimental data and simulations results

Stress estimation in pedestrian crowds: Experimental data and simulations results

Pedestrian crowd flows in shared spaces: Investigating the impact of geometry based on micro and macro scale measures

The motion dynamics of humans in crowded built environments during evacuations are impacted by individuals’ interactions with the physical environment particularly when the geometric space of the movement is restrictive. Controlled laboratory experiments have recently been employed as a reliable data provision approach in this field. Using this approach and based on data obtained from 54 experimental scenarios, this study investigates a range of questions in relation to the collective motion of pedestrian crowds in restricted shares spaces. This includes the role of the layout of the spatial constraints as well as the influence of the movement speed level in the evacuation performance of pedestrian facilities. We explored these questions particularly for dense crowds and based on both micro-scale and macro-scale measures of pedestrian traffic. Our analyses showed that traffic efficiency is demonstrably sensitive to the physical features of the merging passages as well as the desired speed level of the crowd. The patterns of influence of the geometric constraints on traffic efficiency were by and large common across all three speed regimes that we examined. Our results also showed that under specific geometric constraints, the formation of blockages (i.e., longer queue or longer time interval between the passage of two consecutive pedestrians) is more likely. An inspection of the trajectory patterns revealed that this could be attributable to the extent to which the two flows had to mix up with each other in order to be able to merge and share the space. Certain layouts, particularly symmetric layouts, allowed the two flows to keep their separate ways after the entered the shared space, whereas other setups engendered a more noticeable mixture of movement trajectories in order for the two flows to be able to travel through the shared area. The findings have applications for the design of safe passageways when dense crowd flows enter restrictive areas.

Modeling tagged pedestrian motion: A mean-field type game approach

This paper suggests a model for the motion of tagged pedestrians: Pedestrians moving towards a specified targeted destination, which they are forced to reach. It aims to be a decision-making tool for the positioning of fire fighters, security personnel and other services in a pedestrian environment. Taking interaction with the surrounding crowd into account leads to a differential nonzero-sum game model where the tagged pedestrians compete with the surrounding crowd of ordinary pedestrians. When deciding how to act, pedestrians consider crowd distribution-dependent effects, like congestion and crowd aversion. Including such effects in the parameters of the game, makes it a mean-field type game. The equilibrium control is characterized, and special cases are discussed. Behavior in the model is studied by numerical simulations.

Examining effect of architectural adjustment on pedestrian crowd flow at bottleneck

Recent advances in bottleneck studies have highlighted that different architectural adjustments at the exit may reduce the probability of clogging at the exit thereby enhancing the outflow of the individuals. However, those studies are mostly limited to the controlled experiments with non-human organisms or predictions from simulation models. Complementary data with human subjects to test the model’s prediction is limited in literature.This study aims to examine the effect of different geometrical layouts at the exit towards the pedestrian flow via controlled laboratory experiments with human participants. The experimental setups involve pedestrian flow through 14 different geometrical configurations that include different exit locations and obstacles near exit under normal and slow running conditions.It was found that corner exit performed better than middle exit under same obstacle condition. Further, it was observed that the effectiveness of obstacle is sensitive to its size and distance from the exit. Thus, with careful architectural adjustment within a standard escape area, a substantial increase in outflow under normal and slow running conditions could be achieved. However, it was also observed that placing the obstacle too close to the exit can reduce outflow under both normal and slow running conditions. Moreover, we could not observe “faster-is-slower” effect under slow running condition and instead noticed “faster-is-faster” effect. In addition, the power law fitted headway distribution demonstrated that any architectural configurations that enhanced the outflow have higher exponent value compared to the other configuration that negates the outflow.The findings from this paper demonstrate that there is a scope to adjust the architectural elements to optimize the maximum outflow at the egress point. Further, the output from the experiments can be used to develop and verify mathematical models intended to simulate crowd evacuation.

LCrowdV: Generating labeled videos for pedestrian detectors training and crowd behavior learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework to generate crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior (personality), flow, lighting conditions, viewpoint, type of noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by augmenting a real dataset with it and improving the accuracy in pedestrian detection and crowd classification. Furthermore, we evaluate the impact of removing the variety in different LCrowdV parameters to show the importance of the diversity of data generated from our framework. LCrowdV has been made available as an online resource.

Arch formation-based congestion alleviation for crowd evacuation

Congestion in pedestrian crowds is a critical issue for evacuation management. This paper presents an approach based on arch formation to alleviate congestion formed by crowds gathering at a bottleneck. In this approach, analyzing a specific self-organized motion pattern, namely, arch formation, the position of the arch formation has a great influence on congestion alleviation. Specifically, an arch formation at an outer position is beneficial for congestion alleviation because it is unstable and collapses easily. The symmetrically placed obstacles, which have a similar role as anti-slide piles, are used to change the position of the arch formation in a crowd. The spacing between the symmetrically placed obstacles is an important parameter for adjusting an arch formation. We present a modified model that originates from the soil mechanics in civil engineering to calculate the proper spacing between the symmetrically placed obstacles. This model analyzes the static attributes of an arch formation and improves the spacing calculation. The proper spacing between symmetrically placed obstacles can alleviate congestion and increase evacuation efficiency. In this paper, the social force model is used to conduct an evacuation simulation, and the simulation results illustrate the effectiveness of the proposed approach.

Mechanical response of dense pedestrian crowds to the crossing of intruders

The increasing number of mass events involving large crowds calls for a better understanding of the dynamics of dense crowds. Inquiring into the possibility of a mechanical description of these dynamics, we experimentally study the crossing of dense static crowds by a cylindrical intruder, a mechanical test which is classical for granular matter. The analysis of our experiments reveals robust features in the crowds’ response, comprising both similarities and discrepancies with the response of granular media. Common features include the presence of a depleted region behind the intruder and the short-range character of the perturbation. On the other hand, unlike grains, pedestrians anticipate the intruder’s passage by moving much before contact and their displacements are mostly lateral, hence not aligned with the forces exerted by the intruder. Similar conclusions are reached when the intruder is not a cylinder, but a single crossing pedestrian. Thus, our work shows that pedestrian interactions even at high densities (3 to 6 ped/m2) do not reduce to mechanical ones. More generally, the avoidance strategies evidenced by our findings question the incautious use of force models for dense crowds.