Pedestrian Crowds Research Articles

Effects of Small Talk With a Crowd of Virtual Humans on Users' Emotional and Behavioral Responses.

In this contribution, we empirically investigated the effect of small talk on the users' non-verbal behaviors and emotions when users interacted with a crowd of virtual humans (VHs) with positive behavioral dispositions. Users were tasked with collecting items in a virtual marketplace via natural speech-based dialogue with a crowd of virtual pedestrians and vendors. The users were able to engage in natural speech-based conversation in a predefined corpus of small talk content that covered various commonplace small talk topics such as conversations about the weather, general concerns, and entertainment based on similar real-life situations. For instance, the VHs with the small talk ability would ask the users some simple questions to make small talk or remind the users of their belongings. We conducted a between-subjects empirical evaluation to investigate whether the user behaviors and emotions were different between a small talk condition and a non-small talk condition, and examined gender effects of the participants. We collected objective and subjective measures of the users to analyze users' emotions and social interaction behaviors, when in conversation with VHs that either possessed small-talk capability or not, besides task or goal oriented dialogue capabilities. Our result revealed that the VHs with small talk capability could alter the emotions and non-verbal behaviors of the users. Furthermore, the non-verbal behaviors between female and male participants differed greatly in the presence or absence of small talk.

Model considering panic emotion and personality traits for crowd evacuation

Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies and quantitative analysis of evacuation panic, such as panic behaviors, panic evolution, and the stress responses of pedestrians with different personality traits to panic emotion are still rare. Here, combined with the theories of OCEAN (openness, conscientiousness, extroversion, agreeableness, neuroticism) model and SIS (susceptible, infected, susceptible) model, an extended cellular automata model is established by the floor field method in order to investigate the dynamics of panic emotion in the crowd and dynamics of pedestrians affected by emotion. In the model, pedestrians are divided into stable pedestrians and sensitive pedestrians according to their different personality traits in response to emotion, and their emotional state can be normal or panic. Besides, emotion contagion, emotion decay, and the influence of emotion on pedestrian movement decision-making are also considered. The simulation results show that evacuation efficiency will be reduced, for panic pedestrians may act maladaptive behaviors, thereby making the crowd more chaotic. The results further suggest that improving pedestrian psychological ability and raising the standard of management can effectively increase evacuation efficiency. And it is necessary to reduce the panic level of group as soon as possible at the beginning of evacuation. We hope this research could provide a new method to analyze crowd evacuation in panic situations.

Data driven analysis of the desired speed in ordinary differential equation based pedestrian simulation models

In this paper, the desired speed is figured out as an important parameter for ordinary differential equation (ODE) based pedestrian simulation models. However, there lacks a thorough study on the optimal way of assigning the desired speed for ODE based pedestrian simulation models to achieve reliable performance. To gain a deep understanding of the role of the desired speed and its influence on the performances of the ODE based pedestrian simulation models, a total of nine assigning strategies of the desired speed are conducted a comparison study. Specifically, the performances of the ODE based models equipped with such desired speed assigning strategies are compared with regard to reference data from both the uni- and bi-directional pedestrian flow scenarios using a fair model comparison framework. Then, the performances are compared across several dimensions, including different assigning strategies of the desired speed, the different pedestrian simulation models, different experiments for the same motion base, and different motion base scenarios. Based on these results, four findings are summarized. Firstly, the desired speed is quantitatively verified to be an important role in the performance of ODE based pedestrian simulation models. Secondly, pedestrian density level is verified to be an effective indicator for the complexity of pedestrian crowd dynamics. Thirdly, the performance of the ODE based models is dependent on the extent to which the heterogeneity of desired speed is considered. Lastly, the performance of the social force model (SF) is suggested to be more predictable than optimal reciprocal collision avoidance model (ORCA) regarding the stability and optimality. Finally, based on the above findings, recommendations on assigning the desired speed for ODE based pedestrian simulation models are given.

Dynamic Modeling and Simulation of Electric Scooter Interactions With a Pedestrian Crowd Using a Social Force Model

In recent years, micro-mobility transport vehicles have become very popular. As a result, the challenge of developing new strategies and tools for the safety of users and pedestrians in shared travelways has attracted a lot of attention. Previous studies have generally used a social force model as the main tool for the prediction of pedestrian movements. However, existing models cannot be directly applied to wheeled vehicles such as electrical scooters. This study presents a modified social force model to predict the interactions of an electric scooter with a pedestrian crowd by considering the scooter’s kinematics constraints and geometry and the velocity-dependent behaviors of the rider. Moreover, experiments are performed to calibrate the parameters of the proposed model and compare it to an existing social force model. The experimental results demonstrate that the scooter social force model is superior to the original model due to its higher prediction accuracy. Using the scooter model with experimentally calibrated parameters, numerical simulations illustrate the behavior of an e-scooter rider in a pedestrian crowd. Acceleration and force metrics are introduced to evaluate the pedestrian comfort and safety. Monte Carlo simulations provide insights for urban planners about how travelway width, pedestrian density, and the e-scooter rider’s desired velocity affect pedestrian actual and perceived safety.

Modelling group dynamics for crowd simulations

This paper investigates a new method to simulate pedestrian crowd movement in a large and complex virtual environment, representing a public space such as a shopping mall. To demonstrate pedestrian dynamics, we consider groups of pedestrians of different size, sharing a crowded environment. A pedestrian has its own characteristics, such as gender, age, position, velocity, and energy. The proposed method uses a multi-group microscopic model to generate real-time trajectories for all people moving in the defined virtual environment. Additionally, a dynamic model is introduced for modelling group behaviour. Based on the proposed method, all pedestrians in each group can continuously adjust their attributes and optimize their path towards the desired visiting targets, while avoiding obstacles and other pedestrians. Simulation results show that the proposed method can describe a realistic simulation of dynamic behaviour.

Constrained Gaussian Processes With Integrated Kernels for Long-Horizon Prediction of Dense Pedestrian Crowd Flows

In this paper, we present a novel approach for predicting pedestrian crowd dynamics over longer time horizons (30s). In dense environments over long time horizons, the number of pedestrian interactions is high, leading to the degradation of traditional pedestrian trajectory estimation techniques. Alternatively, we consider the macroscopic properties of the crowd as a whole, focusing on the flow of density. This approach benefits from not considering pedestrians individually, and can probabilistically estimate the existence of previously unobserved individuals. We propose a novel approach to imposing a physical constraint on the crowd density flow. Initially, a coarse resolution prediction is generated by a Convolutional Recurrent Neural Network (ConvRNN), and subsequently smoothly interpolated by a Gaussian Process (GP). Using the linearity properties of GPs, a continuous representation of the crowd is produced that complies with both the ConvRNN's prediction and a conservation of density constraint. The approach is trained and analysed on the dense ATC dataset, where we show the advantages of the approach and the improvements from our contributions.

Experimental Investigation on Pedestrian Walking Load in Steel Footbridges

Accurate simulation of walking load is of great significance in conducting human-induced vibration analyses. However, accurate pedestrian walking load data obtained from long-span footbridges is scarce and data reliability depends on the sensor used for measurement. In the current work, Yanluo Footbridge with 102 m span was adopted as test site and Xnode high-precision acceleration wireless sensor was applied for measurements. An experimental investigation was performed on walking loads according to bipedal walking force model. In experimental studies, single-person and multi-person walking tests were performed at Yanluo Footbridge to measure corresponding stride frequency and dynamic load factor. The acceleration time-histories of walking pedestrians were accurately recorded using three-axis wireless acceleration sensor Xnode. Furthermore, the equation of dynamic load factor was derived by analyzing time-histories and power spectra and the design models of pedestrian walking load and crowd load were developed based on a great number of experimental data. Time histories of pedestrian walking loads showed regular periodic changes and dynamic load factor increased by increasing stride frequency. Using the walking load model developed in this work, the reliable structure response of human-induced vibration analysis can be obtained.

Macro-level literature analysis on pedestrian safety: Bibliometric overview, conceptual frames, and trends

Due to the high volume of documents in the pedestrian safety field, the current study conducts a systematic bibliometric analysis on the researches published before October 3, 2021, based on the science-mapping approach. Science mapping enables us to present a broad picture and comprehensive review of a significant number of documents using co-citation, bibliographic coupling, collaboration, and co-word analysis. To this end, a dataset of 6311 pedestrian safety papers was collected from the Web of Science Core Collection database. First, a descriptive analysis was carried out, covering whole yearly publications, most-cited papers, and most-productive authors, as well as sources, affiliations, and countries. In the next steps, science mapping was implemented to clarify the social, intellectual, and conceptual structures of pedestrian-safety research using the VOSviewer and Bibliometrix R-package tools. Remarkably, based on intellectual structure, pedestrian safety demonstrated an association with seven research areas: “Pedestrian crash frequency models”, “Pedestrian injury severity crash models”, “Traffic engineering measures in pedestrians’ safety”, “Global reports around pedestrian accident epidemiology”, “Effect of age and gender on pedestrians’ behavior”, “Distraction of pedestrians”, and “Pedestrian crowd dynamics and evacuation”. Moreover, according to conceptual structure, five major research fronts were found to be relevant, namely “Collision avoidance and intelligent transportation systems (ITS)”, “Epidemiological studies of pedestrian injury and prevention”, “Pedestrian road crossing and behavioral factors”, “Pedestrian flow simulation”, and “Walkable environment and pedestrian safety”. Finally, “autonomous vehicle”, “pedestrian detection”, and “collision avoidance” themes were identified as having the greatest centrality and development degrees in recent years.

Disentangling intrinsic motion from neighborhood effects in heterogeneous collective motion.

Most real-world collectives, including animal groups, pedestrian crowds, active particles, and living cells, are heterogeneous. The differences among individuals in their intrinsic properties have emergent effects at the group level. It is often of interest to infer how the intrinsic properties differ among the individuals based on their observed movement patterns. However, the true individual properties may be masked by the nonlinear interactions in the collective. We investigate the inference problem in the context of a bidisperse collective with two types of agents, where the goal is to observe the motion of the collective and classify the agents according to their types. Since collective effects, such as jamming and clustering, affect individual motion, the information in an agent's own movement is insufficient for accurate classification. A simple observer algorithm, based only on individual velocities, cannot accurately estimate the level of heterogeneity of the system and often misclassifies agents. We propose a novel approach to the classification problem, where collective effects on an agent's motion are explicitly accounted for. We use insights about the phenomenology of collective motion to quantify the effect of the neighborhood on an agent's motion using a neighborhood parameter. Such an approach can distinguish between agents of two types, even when their observed motion is identical. This approach estimates the level of heterogeneity much more accurately and achieves significant improvements in classification. Our results demonstrate that explicitly accounting for neighborhood effects is often necessary to correctly infer intrinsic properties of individuals.

An attempt to distinguish physical and socio-psychological influences on pedestrian bottleneck.

It has been realized that the distinction between social-psychological effects and physical effects in pedestrian crowds is complex, and so the relevance of social psychology for the properties of pedestrian streams is still discussed controversially. Although physics-based models appear to capture many properties rather accurately, it was argued that simple systems of self-driven particles could not explain certain emergent phenomena. In particular, results from a recent empirical study of pedestrian flow at bottlenecks have been interpreted as indicating the relevance of social psychology even in relatively simple scenarios of crowd dynamics. The study showed a surprising dependence of the density near the bottleneck on the width of the corridor leading to it. The density increased with increasing corridor width, although a wider corridor provides more space for pedestrians. It has been argued that this observation is a consequence of social norms, which trigger the effect by a preference for queuing in such situations. However, convincing evidence for this hypothesis is still missing. Here, we reconsider this scenario from a physics perspective using computer simulations of a simple microscopic velocity-based model.

The Barcelona Attack as an Example of a New Way of Carrying Out a Terrorist Attempt in an Urban Space Using a Vehicle Ramming a Crowd of Pedestrians

Abstract In this article the author presents problems related to the use of a new way of carrying out attacks by terrorists and terrorist groups, which is the use of vehicles to ram people in crowded areas of urban space. This method is presented based on the 2017 attacks in Barcelona and Cambrils, Spain. The text presents the course of the attack, the actions of the services, as well as a detailed analysis of these attacks. The material on which the article was based was taken from Spanish press and from communications made available by the services responsible for state security. In the conclusion the author draws particular attention to the increasing use of the method of ramming people with vehicles by terrorists and the ways in which this should be counteracted.

How COVID-19 is Affecting Pedestrian Modeling and Simulation: The Case of Venice

The use of computer-based pedestrian crowd simulations has become crucial in transport planning considering the unprecedented effects of the COVID-19 pandemic on urban mobility. However, there is still a lack of knowledge in relation to the impact of social distancing on crowding, queuing, route choice, and other pedestrian crowd phenomena. In this context, the aim of the current study was to apply the Social Force Model of the pedestrian simulation platform PTV Viswalk to investigate the effects of disruption of social distancing on pedestrian dynamics. First, a descriptive set of metrics and parameters was applied for calibrating the dynamic regulation of interpersonal distances among pedestrians. The plausibility of the proposed social distancing model was then evaluated against the so-called fundamental diagram to calibrate pedestrian volume-delay functions. Finally, the proposed model was integrated into the PTV Visum simulation platform to evaluate the effects of social distancing on large-scale pedestrian route choice. To do so, a macroscopic static model of the city of Venice was developed to test the effectiveness of alternative crowd management strategies related to pedestrian dynamics in a predictive scheme.

An improved crowd detection system using text and image filtration methods for precautions from COVID-19

The text feature is an important descriptive feature of many image analysis applications. Objectives of this study aim to determine the different texture characteristics of them estimation and calculation of population density. In this paper, we have various reviews of the texture and their own have been extensively reviewed, a different combination that is possible to test their effectiveness in crowds of pedestrians. Divide into two categories and retreat. A framework has been proposed to evaluate performance of all aspects of the density coefficient of human density as well to count. According to the framework, the input images are categorized into blocks and blocks into cells of different sizes, having various levels of overcrowding. Because of a distorted view of people, people's visibility near the camera contributes greatly to this feature vector than distant people. Therefore, the features released are usually using a standard visual map of the scene. In the first stage, picture blocks are classified using multiple classes SVM has been at a different level of congestion. In the second phase Gaussian Process Deactivation is used to restore undoing low-level features for calculation. Various texture features and their possible combinations checked on publicly available dataset.

Pedestrian dynamics of single-file experiments with music considering different music and different instructions

Pedestrian crowds are common in public facilities. It is essential to manage pedestrian flow for the safety of pedestrians. The rhythm is a potential method for improving pedestrian flow. Here we studied pedestrian dynamics with music by a series of single-file experiments considering different kinds of music (motivational music and nonmotivational music) and different instructions (listen to the music while walking, follow the beat of the music). To observe the effect of music on pedestrian movement, the condition without any rhythm and that with music were connected without breaking in a same run. Trajectories of the pedestrians were extracted and further analyzed. It is found that the effect of music on pedestrian flow can be observed directly. Both different kinds of music and different instructions can influence pedestrian motion. Pedestrians walk more quickly with the motivational music at all densities when instructed to listen to the music while walking. While they will walk more slowly at low density and more quickly at high density with the nonmotivational music. But when the participants are asked to follow the music, the velocity of pedestrians is only influenced by the music at high density. Comparing fundamental diagrams, we found that the music can improve pedestrian flow at most conditions. Our study will be helpful for understanding the effect of music on pedestrian dynamics.

A Hierarchical Approach for Mobile Robot Exploration in Pedestrian Crowd

Autonomous exploration is a fundamental task for mobile robots. However, a major limitation is that in practical applications, robots usually face dynamic obstacles such as pedestrian crowds. These dynamic objects create significant challenges for both collision-free navigation and accurate localization/mapping, which can compromise the safety and exploration performance of the robot. In this work, a hierarchical approach is proposed for both effective exploration and collision-free navigation in crowded environments. The central idea of our approach is to combine local and global information to ensure the safety and efficiency of the exploration planner. Besides, our planning method utilizes a reinforcement learning (RL)-based obstacle avoidance algorithm that allows the robot to safely follow the exploration planner's path through the pedestrian crowd. The proposed system is thoroughly evaluated in simulation environments, and the results show that it outperforms existing methods in terms of not only the exploration efficiency but also the localization and mapping accuracy.

Social Groups in Pedestrian Crowds as Physical and Cognitive Entities: To What Extent Can We Model and Predict Their Behavior?

Social Groups in Pedestrian Crowds as Physical and Cognitive Entities: To What Extent Can We Model and Predict Their Behavior?

Analyzing pedestrian individual and interaction collision avoidance dynamics in traditional scenarios

Revealing the behavioral mechanism of crowd dynamics is crucial for explaining the self-organized phenomena and making a quick emergent response for crowd management. Various approaches have been proposed to analyze self-organized phenomena of crowd dynamics, but the essential mechanisms of complicated and heterogeneous collision avoidance behaviors are still not clear. To deeply investigate the microscopic behaviors, the geometrical characteristics of Voronoi diagrams and velocity obstacles are explored and a unified spatial tessellation and velocity obstacle approach is proposed. The personal space is divided into four regions, and a velocity graph that is a subgraph of Delaunay triangulation will be established to describe the relationship between pedestrians. In a velocity graph, a link represents that a pedestrian’s speed is towards a Voronoi edge of its neighbor. According to the experimental pedestrian trajectory data, the collision avoidance probability is relevant to some parameters, such as spatial conflict time, and minimal velocity difference. Results show that the probability is related to the distance to velocity obstacle boundary and relaxation time by Sigmoid function. Besides, the behavior dynamics in each region is analyzed, and it is determined by the deviation angles between Voronoi nodes and neighbor locations. This approach can be potentially applied to pedestrian behavioral analysis and crowd management.

Simulating crowd behaviour combining both microscopic and macroscopic rules

We propose a new method for simulating pedestrian crowd movement in a virtual environment. A crowd consists of groups of different number of people with different attributes such as gender, age, position, velocity, and energy. Each group has its own intention used to generate a trajectory for each pedestrian navigating in the virtual environment. Additionally, an agent-based model is introduced to simulate pedestrian behaviours in the groups, where various steering behaviours are introduced and combined into a single steering force to allow pedestrians in each group to walk toward their destination point. Based on the proposed method, every single pedestrian in each group can continuously adjust their attributes. Moreover, pedestrians optimize their path independently toward the desired goals, while avoiding obstacles and other pedestrians in the scene. This method takes into account the safety-space around each pedestrian to avoid collisions among pedestrians. The proposed method was implemented for several simulation scenarios under various conditions for a wide range of different parameters. Statistical analysis is carried out to evaluate the performance of the proposed method for simulating the crowd movement in the virtual environment. Results indicate that our method can generate each pedestrian’s trajectories in each group independently to reach several goal points within a reasonable computational time. Moreover, the obtained results reveal that the mean value of the computational time is not increased significantly with the increasing of the number of pedestrians in the crowd.

A systematic review and meta-analysis on the effect social groups have on the egress times of pedestrian crowds

Conflicting results in the literature raise the question of how reliable findings from single experiments on pedestrian crowd movements are. An important example is the effect social groups have on crowd egress times from confined spaces where both increases and decreases were reported. We identify only six comparable studies and conduct the first analysis on this topic that integrates evidence from multiple studies quantitatively, accounting for different sample sizes. The aggregated findings suggest social groups increase average egress times but there is insufficient evidence to reject the null hypothesis of no effect. The conflicting results across published studies are thus likely to have arisen by chance, as experiments are statistically underpowered for determining a small effect. We find no evidence for publication bias in terms of findings or statistical power. Our work presents a quantitative basis for discussing the statistical reliability of experiments considering the high context-dependency of pedestrian dynamics.

Experimental study of pedestrian flows including crutch users through a bottleneck with different angles

There is a great demand for the transportation safety of pedestrian crowd including crutch users considering the rapidly aging and physically disabled population as well as the increased potential safety hazard in areas with a dense population. To investigate the pedestrian dynamics of lower-body disabled or injured people, we performed pedestrian experiments in bottlenecks by considering different angles of the bottleneck in heterogeneous crowd with different numbers of crutch users. It is shown that the average velocities of crutch users are 7.1%–22.2% smaller than those of able-bodied participants. With the increase of crutch numbers, the proxemic values of crutch users decrease as a linear function. Compared with the other three angles (0°, 15°, 30°), the flow rate in the 45° bottleneck is higher with less waiting and congestion. However, crutch users receive a larger repulsion effect at angle 45° considering their larger proxemic values. The results in this study are valuable for the facility design and evacuation guidance in the pedestrian flow including crutch users through bottlenecks.