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Early crowd dynamics forecasting, such as one week in advance, plays an important role in risk-aware decision-making in urban regions such as congestion mitigation or crowd control for public safety. Although previous approaches have addressed crowd dynamics prediction, they have failed to deal with the scarcity of anomalous events, which results in a large model bias and could not quantify the number of visitors in anomalous crowd gathering. To provide an elaborate early forecast, we focus on the successive properties of importance weighting (IW) to penalize the anomalous data in terms of model bias; however, leveraging the concept of IW is challenging because dividing dataset into normal and abnormal sets is difficult. Motivated by these challenges, we propose CityOutlook, a novel forecasting model based on unbiased regression with importance-based reweighting. To make IW applicable to our approach, we design an anomaly-aware data annotation scheme by utilizing the heterogeneous property of mobility data to determine the data anomaly. We evaluate CityOutlook using the datasets of large-scale mobility and transit search logs. The experimental results show that CityOutlook outperforms the state-of-the-art models on crowd anomaly forecast, providing the same level accuracy in forecasting normal dynamics.

The local crowd density and the crowd distribution estimation tasks are useful but challenging. There are two main problems which limit the performance of existing algorithms. The first one is that there are not enough labeled training samples to build the high-performance estimation model. Another one is that existing methods lack the supports of physical theories of the crowd. To remedy them, a novel crowd density field model is proposed, which is deduced by jointing crowd dynamics theory and social force model. A crowd counting method based on the proposed crowd density field model is introduced to measure the proposed crowd density field model. Extensive experiments confirm the effectiveness of the proposed plan.

Predicting evacuation patterns is useful in emergency management situations such as an earthquake. To find out how pre-trained individuals interact with one another to achieve their own goal to reach the exit as fast as possible firstly, we investigated urban people’s evacuation behavior under earthquake disaster coditions, established crowd response rules in emergencies, and described the drill strategy and exit familiarity quantitatively through a cellular automata model. By setting different exit familiarity ratios, simulation experiments under different strategies were conducted to predict people’s reactions before an emergency. The corresponding simulation results indicated that the evacuees’ training level could affect a multi-exit zone’s evacuation pattern and clearance time. Their exit choice preferences may disrupt the exit options’ balance, leading to congestion in some of the exits. Secondly, due to people’s rejection of long distances, congestion, and unfamiliar exits, some people would hesitant about the evacuation direction during the evacuation process. This hesitation would also significantly reduce the overall evacuation efficiency. Finally, taking a community in Zhuhai City, China, as an example, put forward the best urban evacuation drill strategy. The quantitative relation between exit familiar level and evacuation efficiency was obtained. The final results showed that the optimized evacuation plan could improve evacuation’s overall efficiency through the self-organization effect. These studies may have some impact on predicting crowd behavior during evacuation and designing the evacuation plan.

This paper outlines a crowd egress simulation system that models the interactions of police and large crowds. Police are often deployed during large events for crowd management and to facilitate the safe and efficient departure of the crowd. Police and crowd members are represented in the system using agent-based modelling and simulation. Crowd members in the simulation either attempt to meet up with members of their social group or attempt to exit by public transit, while the police promote crowd movement by directing the crowd towards egress routes. This simulation system was used to model a crowd leaving the Honda Celebration of Light in Vancouver, BC, Canada. Several recommendations for crowd management were ascertained from comparing the results of different crowd and police management scenarios.

Undoubtedly, Location-based Social Networks (LBSNs) provide an interesting source of geo-located data that we have previously used to obtain patterns of the dynamics of crowds throughout urban areas. According to our previous results, activity in LBSNs reflects the real activity in the city. Therefore, unexpected behaviors in the social media activity are a trustful evidence of unexpected changes of the activity in the city. In this paper we introduce a hybrid solution to early detect these changes based on applying a combination of two approaches, the use of entropy analysis and clustering techniques, on the data gathered from LBSNs. In particular, we have performed our experiments over a data set collected from Instagram for seven months in New York City, obtaining promising results. This work is funded by: the European Regional Development Fund (ERDF) and the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC), Spain, the Spanish Ministry of Economy and Competitiveness under the National Science Program (TEC2014-54335-C4-3-R, TEC2014-54335-C4-2-R, TEC2017-84197-C4-3-R and TEC2017-84197-C4-2-R), and by the Madrid Regional Government eMadrid Excellence Network, Spain (S2013/ICE-2715).

The number of confirmed COVID-19 cases admitted in hospitals is continuously increasing in the Philippines. Frontline health care workers are faced with imminent risks of getting infected. In this study, we formulate a theoretical model to calculate the risk of being infected in health care facilities considering the following factors: the average number of encounters with a suspected COVID-19 patient per hour; interaction time for each encounter; work shift duration or exposure time; crowd density, which may depend on the amount of space available in a given location; and availability and effectiveness of protective gears and facilities provided for the frontline health care workers. Based on the simulation results, a set of risk assessment criteria is proposed to classify risks as ‘low’, ‘moderate’, or ‘high’. We recommend the following: (i) decrease the rate of patient encounter per frontline health care worker, e.g., maximum of three encounters per hour in a 12-hour work shift duration; (ii) decrease the interaction time between the frontline health care worker and the patients, e.g., less than 40 minutes for the whole day; (iii) increase the clean and safe space for social distancing, e.g., maximum of 10% crowd density, and if possible, implement compartmentalization of patients; and/or (iv) provide effective protective gears and facilities, e.g., 95% effective, that the frontline health care workers can use during their shift. Moreover, the formulated model can be used for other similar scenarios, such as identifying infection risk in public transportation, school classroom settings, offices, and mass gatherings.

In this study, pedestrian crowd dynamics at corner turns were investigated by analyzing pedestrian trajectories in a subject experiment for building more reliable, general-purpose, pedestrian simulation models. An experiment under laboratory conditions was conducted wherein a pedestrian crowd walked straight for a short distance before turning into a right-angled corner built with partition walls; the opposite sides were unwalled. Trials were performed with different widths and densities of initial participant positions. Finally, the trajectories of the pedestrians were extracted from a video through computer image analysis. The results demonstrated that pedestrian behavior at corner turns depends on lane position, lane distance (from the wall), and crowd density.

Present simulation and experimental research still have deficiency in depicting the evading behaviour of single pedestrians confronting with an obstacle, which is the basis for the study of crowd dynamics affected by obstacles in real life. Therefore, this study will conduct experiments with a bar-shaped obstacle in the middle of a corridor and explore the corresponding general and particular features of single pedestrians. Particularly, the variation of pedestrian velocity and trajectory under different-sized obstacles will be illustrated. By taking the average velocity and trajectories of the 32 participants, it could be concluded that pedestrians would walk at a velocity of about 1.5 m/s without being affected by the size of obstacle. Besides, pedestrians tend to pass a location about 0.4 meters away from the obstacle edge that is perpendicular to walking direction. Furthermore, pedestrians tend to begin and finish evading the obstacle at locations respectively about 4.40 meters and 4.85 meters away from the obstacle. We also found a heterogeneity in the evading behaviour and pedestrians could be classified into four types accordingly. Results of this study are expected to provide reliable evidence for agent-based modelling in the future.

Large people gatherings in public places exhibit crowd dynamics that are quite complex. Such mass events with high densities are fraught with potentially serious consequences if not understood and managed properly. In mass religious processions, the participants in the crowd are emotional and mostly impulsive and often crowd disasters occur due to the behaviour of the crowd. These events have great potential to cause safety hazards to the people. This paper attempts to narrate the typical situations of crowd dynamics observed in the Kumbh Mela procession-2016 and to describe the characteristics of the crowd that have not been reported in literature so far but have a significant impact on the crowd. Extreme crowd pressures resulting in individual loss control due to psychological and physiological factors, heterogeneity in the crowd, group behaviour and their induced competitiveness, unexpected behaviour exhibited due to the motivation behind participating in such procession makes it a typical crowd concentrated event to study the potentially critical dynamics of crowd. Physical and psychological forces acting on the people and their resulting dynamics of crowd in the Kumbh Mela procession 2016 lead to serpentine behaviour, which can possibly lead to crowd crushes, or any such crowd risk situations. Therefore, the characteristics of crowd participating in the Kumbh Mela procession have to be clearly understood so that it helps in better planning and well-organized movement patterns.

A high fidelity model for the propagation of pathogens via aerosols in the presence of moving pedestrians is proposed. The key idea is the tight coupling of computational fluid dynamics and computational crowd dynamics in order to capture the emission, transport and inhalation of pathogen loads in space and time. An example simulating pathogen propagation in a narrow corridor with moving pedestrians clearly shows the considerable effect that pedestrian motion has on airflow, and hence on pathogen propagation and potential infectivity.

The study of the discharge of active or self-driven matter in narrow passages has become of the growing interest in a variety of fields. The question has particularly important practical applications for the safety of pedestrian human flows notably in emergency scenarios. It has been suggested predominantly through simulation in some theoretical studies as well as through few experimentations that under certain circumstances, an elevated vigour to escape may exacerbate the outflow and cause further delay although the experimental evidence is rather mixed. The dimensions of this complex phenomenon known as the “faster-is slower” effect are of crucial importance to be understood owing to its potential practical implications for the emergency management. The contextual requirements of observing this phenomenon are yet to be identified. It is not clear whether a “do not speed up” policy is universally beneficial and advisable in an evacuation scenario. Here for the first time we experimentally examine this phenomenon in relation to the pedestrian flows at merging sections as a common geometric feature of crowd egress. Various merging angles and three different speed regimes were examined in high-density laboratory experiments. The measurements of flow interruptions and egress efficiency all indicated that the pedestrians were discharged faster when moving at elevated speed levels. We also observed clear dependencies between the discharge rate and the physical layout of the merging with certain designs clearly outperforming others. But regardless of the design, we observed faster throughput and greater avalanche sizes when we instructed pedestrians to run. Our results give the suggestion that observation of the faster-is-slower effect may necessitate certain critical conditions including passages being overly narrow relative to the size of participles (pedestrians) to create long-lasting blockages. The faster-is-slower assumption may not be universal and there may be circumstances where faster is, in fact, faster for evacuees. In the light of these findings, we suggest that it is important to identify and formulate those conditions so they can be disentangled from one another in the models. Misguided overgeneralisations may have unintended adverse ramifications for the safe evacuation management, and this highlights the need for further exploration of this phenomenon.

It is well known that a large percentage of occupants in a building are evacuated together with their friends, families, and officemates, especially in China. Small group behaviors are therefore critical for crowd movement. This paper aims to study the crowd dynamic considering different social relations and the impacts of small groups on crowd dynamics in emergency evacuation. Three experiments are conducted in an 11-storey office building. In the first two experiments, all participants are classmates and know each other well. They are evacuated as individuals or pairs. In the third experiment, social relations among the participants are complex. Participants consist of 8 families, 6 lovers and several individuals. Space–time features, speed characteristics and density–speed relations for each experiment are analyzed and compared. Results conclude that small group behaviors can make positive impacts on crowd dynamics when evacuees know each other and are cooperative. This conclusion is also testified by four verified experiments. In the third experiment, speeds of evacuees are lowest. Small groups form automatically with the presence of intimate social relations. Small groups in this experiment slow down the average speed of the crowd and make disturbance on the crowd flow. Small groups in this case make negative impacts on the movement of the crowd. It is because that evacuees do not know each other and they are competitive to each other. Characteristics of different types of small groups are also investigated. Experimental data can provide foundational parameters for evacuation model development and are helpful for building designers.

The significant growth of pedestrian flows through stations of mass public transport has turned the two issues of passenger comfort and safety into major concerns for railway station operators. Managing these issues is a challenging task due to the complexity of in-station pedestrian facilities, of passenger behaviors and of train and railway operations. Therefore, station management can no longer be limited to the management of train traffic. Several research studies have been developed in recent years that aim to evaluate pedestrian infrastructure within railway facilities by monitoring pedestrian flows and/or modeling them (“crowd dynamics” models). However, the way from these studies to the management of train stations remains to be paved. This paper brings about a framework for station management with special focus on passenger flows. The framework is twofold, including first a general understanding of station as a system with specific transfer function and ad-hoc topology, second the dynamic supervision of system performance. We also address observation requirements and the toolbox available for modeling.

Event crowd management has been a significant research topic with high social impact. When some big events happen such as an earthquake, typhoon, and national festival, crowd management becomes the first priority for governments (e.g. police) and public service operators (e.g. subway/bus operator) to protect people's safety or maintain the operation of public infrastructures. However, under such event situations, human behavior will become very different from daily routines, which makes prediction of crowd dynamics at big events become highly challenging, especially at a citywide level. Therefore in this study, we aim to extract the deep trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations. Motivated by these, we build an online system called DeepUrbanEvent which can iteratively take citywide crowd dynamics from the current one hour as input and report the prediction results for the next one hour as output. A novel deep learning architecture built with recurrent neural networks is designed to effectively model these highly-complex sequential data in an analogous manner to video prediction tasks. Experimental results demonstrate the superior performance of our proposed methodology to the existing approaches. Lastly, we apply our prototype system to multiple big real-world events and show that it is highly deployable as an online crowd management system.

This paper proposes a critical analysis of the literature addressed to modelling and simulations of human crowds with the aim of selecting the most appropriate scale out of the microscopic (individual based), mesoscopic (kinetic), and macroscopic (hydrodynamical) approaches. The selection is made focusing on possible applications of the model. In particular, model validation and safety problems, where validation consists of studying the ability of models to depict empirical data and observed emerging behaviors. The contents of the paper look forward to computational applications related to the flow crowds on the Jamarat bridge.

By Islam’s principle, each adult Muslim who is physically and financially capable is obligated to perform Hajj—Pilgrimage to Mecca—once in his lifetime. With millions of faithful pilgrims, the Hajj...

To facilitate efficient and effective city management, it is important for urban authorities to understand the regular functionalities of urban areas and the irregular crowd dynamics moving around the city. However, existing methods relying on manual surveys and statistics usually cost substantial time and labor, hindering the fine-grain characterization of urban structures and the in-depth understanding of crowd dynamics. In this paper, we leverage large-scale mobility data collected from vehicle GPS devices to analyze the dynamics of crowd movement in different urban areas in a low-cost and automatic manner. We extract the regular crowd movement patterns in different areas, detect the abnormal crowd movement flow peaks, and then interpret the influences of different types of urban events. More specifically, we first divide the city into fine-grained geographic regions and cluster them according to the similarity of crowd movement characteristics. Second, we detect anomaly traffic flow for each cluster area, interpret urban events for each abnormal flow point, and correlate urban events to the interpretation results. Finally, we determine the scope of urban events and use visualization techniques to demonstrate the impact of different types of urban events. We leverage the large-scale real-world datasets from Xiamen City for evaluation. Experimental results validate the effectiveness of our method, and several case studies in Xiamen are conducted.

With the coronavirus (COVID-19) pandemic continuing to spread around the globe, there is an unprecedented need to develop different approaches to containing the pandemic from spreading further. One particular case of importance is mass-gathering events. Mass-gathering events have been shown to exhibit the possibility to be superspreader events, as such, the adoption of effective control strategies by policymakers is essential to curb the spread of the pandemic. This paper deals with modeling the possible spread of COVID-19 in the Hajj, the world’s largest religious gathering. We present an agent-based model (ABM) for two rituals of the Hajj: Tawaf and Ramy al-Jamarat. The model aims to investigate the effect of two control measures: buffers and face masks. We couple these control measures with a third control measure that can be adopted by policymakers, which is limiting the capacity of each ritual. Our findings show the impact of each control measure on the curbing of the spread of COVID-19 under the different crowd dynamics induced by the constraints of each ritual.

This paper proposes an essay concerning the understanding of human behaviours and crisis management of crowds in extreme situations, such as evacuation through complex venues. The first part focuses on the understanding of the main features of the crowd viewed as a living, hence complex system. The main concepts are subsequently addressed, in the second part, to a critical analysis of mathematical models suitable to capture them, as far as it is possible. Then, the third part focuses on the use, toward safety problems, of a model derived by the methods of the mathematical kinetic theory and theoretical tools of evolutionary game theory. It is shown how this model can depict critical situations and how these can be managed with the aim of minimizing the risk of catastrophic events.

The huge number of pilgrims to the holy Mecca in the Hajj needs high awareness of crowd safety management. The stoning of the Jamarat, which is one of the rituals of the Hajj, undergoes the most dangerous crowd movements where fatal accidents occurred. This work investigates some problems related with the crowd dynamics when stoning the Jamarat pillars and gives some solutions. The main idea of this research is to suppose that the crowd dynamics is assimilated to fluid movement under certain conditions. Numerical simulation using a computational fluid dynamics program is used to solve Navier-Stokes equations governing the mechanics of homogeneous and incompressible fluid in a domain similar to the Jamarat Bridge from the entrance to the middle Jamarah. Some solutions are proposed inspired by the flow solutions to better manage crowd movements in the Jamarat Bridge and eventually in other similar dynamics events like sporting events.

This article belongs to the Special Issue Innovative Technologies and Services for Smart Cities. Evidence of something unusual happening in urban areas can be collected from different data sources, such as police officers, cameras, or specialized physical infrastructures. In this paper, we propose using geotagged posts on location-based social networks (LBSNs) to detect crowd dynamics anomalies automatically as evidence of a potential unusual event. To this end, we use the Instagram API media/search endpoint to collect the location of the pictures posted by Instagram users in a given area periodically. The collected locations are summarized by their centroid. The novelty of our work relies on using the entropy of the sequence of centroid locations in order to detect abnormal patterns in the city. The proposal is tested on a data set collected from Instagram during seven months in New York City and validated with another data set from Manchester. The results have also been compared with an alternative approach, a training phase plus a ranking of outliers. The main conclusion is that the entropy algorithm succeeds inn finding abnormal events without the need for a training phase, being able to dynamically adapt to changes in crowd behavior. This research was partially funded by the Ministry of Economy and Competitiveness through TEC2017-84197-C4-1-R, TEC2017-84197-C4-2-R, TEC2014-54335-C4-2-R, and TEC2014-54335-C4-3-R, and the European Regional Development Fund (ERDF) and the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC).

Great events as concerts, music festivals and football matches bring together many people from different backgrounds and interests. In these events, people act, most of the time, as passive spectators, with little or none interaction with the artists in the event. Nowadays, there is some effort to engage people in those spaces, as well as using simple technology such as objects that can be raised on pre-defined moments or, as most recently, using LED technology with sensors. This work proposes an interaction technology for crowds in great events using mobile devices. This technology involves a dynamic and an app to redefine crowd role in those events, increasing their participation and engagement. In order to evaluate people immersion in great events, a case study has been carried out in a controlled environment mimicking a music concert. As a result, it was observed that the participants felt engaged, immersed and motivated to use the proposed technology in music concerts or even other great events and they are also willing to use their own devices as a way of an active participation.

In this paper, we explore the application of system identification techniques to the inference of a model that characterizes crowd dynamics, inspired by the social force model proposed by Helbing and Molnar. We focus then on sensor observations of pedestrians' actions considering that wearables, smart mobile phones and other IoT devices embedded in the environment give significant insights on their expected mobility patterns. Previous work using IoT sensors to uncover social interactions is not based on mathematical models, while most models used for tracking mobility ignore the strong coupling between the model-agents as well as their surroundings. Our aim is to bridge these approaches, by capturing in the data model the swarming behavior of the network, including social interactions

According to conventional wisdom on crowdsourcing, the number of people defines the crowd and maximization of this number is often assumed to be the goal of any crowdsourcing exercise. However, some structural characteristics of the crowd might be more important than the sheer number of participants. These characteristics include (1) the growth rate and its attractiveness to members, (2) equality among members, (3) density within provisional boundaries, (4) goal orientation of the crowd, and (5) “seriality” of the interactions between members. Therefore, a typology is proposed that may allow managers to position their companies’ initiatives among four strategic types for driving innovation: crowd crystals, online communities, closed crowds, and open crowds. Incumbent companies may prefer closed and controlled access to the crowd, limiting the potential for gaining results and insights from fully open crowd-driven innovation initiatives. Thus, the effects on industries and organizations by open crowds are still to be explored.

International audience; Understanding and predicting the collective behaviour of crowds is essential to improve the efficiency of pedestrian flows in urban areas and minimize the risks of accidents at mass events. We advocate for the development of crowd traffic management systems, whereby observations of crowds can be coupled to fast and reliable models to produce rapid predictions of the crowd movement and eventually help crowd managers choose between tailored optimization strategies. Here, we propose a Bi-directional Macroscopic (BM) model as the core of such a system. Its key input is the fundamental diagram for bi-directional flows, i.e. the relation between the pedestrian fluxes and densities. We design and run a laboratory experiments involving a total of 119 participants walking in opposite directions in a circular corridor and show that the model is able to accurately capture the experimental data in a typical crowd forecasting situation. Finally, we propose a simple segregation strategy for enhancing the traffic efficiency, and use the BM model to determine the conditions under which this strategy would be beneficial. The BM model, therefore, could serve as a building block to develop on the fly prediction of crowd movements and help deploying real-time crowd optimization strategies.

Crowd simulations facilitate the study of how an environment layout impacts the movement and behavior of its inhabitants. However, simulations are computationally expensive, which make them infeasible when used as part of interactive systems (e.g., Computer-Assisted Design software). Machine learning models, such as neural networks (NN), can learn observed behaviors from examples, and can potentially offer a rational prediction of a crowd's behavior efficiently. To this end, we propose a method to predict the aggregate characteristics of crowd dynamics using regression neural networks (NN). We parametrize the environment, the crowd distribution and the steering method to serve as inputs to the NN models, while a number of common performance measures serve as the output. Our preliminary experiments show that our approach can help users evaluate a large number of environments efficiently.

Understanding and predicting the collective behaviour of crowds is essential to improve the efficiency of pedestrian flows in urban areas and minimize the risks of accidents at mass events. We advocate for the development of a & “crowd forecasting system„whereby real-time observations of crowds are coupled to fast and reliable models to produce rapid predictions of the crowd movement and eventually help crowd managers choose between tailored optimization strategies. Here, we propose a Bi-directional Macroscopic (BM) model as the core of such a system. Its key input is the fundamental diagram for bi-directional flows, i.e. the relation between the pedestrian fluxes and densities. We design and run a laboratory experiments involving a total of 119 participants walking in opposite directions in a circular corridor and show that the model is able to accurately capture the experimental data in a typical crowd forecasting situation. Finally, we propose a simple segregation strategy for enhancing the traffic efficiency, and use the BM model to determine the conditions under which this strategy would be beneficial. The BM model, therefore, could serve as a building block to develop on the fly prediction of crowd movements and help deploying real-time crowd optimization strategies.

Crowd model has been very important in the investigation and study of the dynamics of a crowd. Crowd analysis using simulation models such as the Social Force Model (SFM) in representing crowd dynamics is instrumental in this study where experiments with real humans are not practical. However, the SFM lacks features that can represent more detailed individual characteristics and realistic movement when dealing with multi-room environment. Therefore, in this paper, we propose some modifications to the original SFM with a magnetic model approach that also integrates a path finding feature to produce a more realistic simulation of individuals’ movements in the crowd. Results show that common collective crowd behaviours have been produced by the modified crowd model in a multi-room environment.

This study presents a modelling framework of human-structure interaction in the vertical direction, which integrates the three following key issues: crowd dynamics, pedestrian-structure interaction (PSI) and inter-subject and intra-subject variability of pedestrian walking loads. The framework comprises two main models: a microscopic model of crowd dynamics and a coupled dynamic model of the PSI. The latter is composed of three sub-models: a SDOF system having the dynamic properties of the empty structure, a SDOF system for each pedestrian and a stochastic force model. Each pedestrian SDOF moves along the footbridge following the trajectory and at the velocity simulated by the crowd model and is accompanied by a stochastic walking force time-history that accounts for inter- and intra-subject variability. Performance of the suggested modelling framework is studied through simulations of the vibration responses of four virtual footbridges due to different traffic scenarios.

Managing the uncertainties that arise in disasters --- such as a ship or building fire --- can be extremely challenging. Previous work has typically focused either on modeling crowd behavior, hazard dynamics, or targeting fully known environments. However, when a disaster strikes, uncertainties about the nature, extent and further development of the hazard is the rule rather than the exception. Additionally, crowds and hazard dynamics are both intertwined and uncertain, making evacuation planning extremely difficult. To address this challenge, we propose a novel spatio-temporal probabilistic model that integrates crowd and hazard dynamics, using ship- and building fire as proof-of-concept scenarios. The model is realized as a dynamic Bayesian network (DBN), supporting distinct kinds of crowd evacuation behavior, being based on studies of physical fire models, crowd psychology models, and corresponding flow models. Simulation results demonstrate that the DBN model allows us to track and forecast the movement of people until they escape, as the hazard develops from time step to time step. Our scheme thus opens up for novel in situ threat mapping and evacuation planning under uncertainty, with applications to emergency response.

In this paper, we present a developed cellular automata model to simulate the pedestrians’ crowd behaviors in congested public facility during emergency evacuation. Pedestrian perception and choice behavior considering the queue time of door changing were investigated in pedestrian decision and evacuation time. The model was used to mimic the emergency evacuation with a range of densities for the study of relationship between individual behavior and evacuation time. The results indicate that pedestrian is sensitively for the queue time and prefer to change the exit if the evacuation time perceived by the pedestrian is large then other exit during the escape process. The model is useful to study the pedestrian’s behavior during the emergency evacuation, and helpful for safe layout of the public facilities.

The collective behavior of crowds is thought to emerge from local interactions between pedestrians. People widely assumed interaction is heading alignment. The authors tested four dynamical models of alignment against human data from pairs of pedestrians. A “follower” walked with a “leader” while head trajectories were recorded. In the simplest model, the follower's angular acceleration is proportional to the sine of the heading difference (r=0.71); additional damping or delay parameters did not improve the fit. The authors conclude that alignment is controlled by nulling the heading difference. The results provide a cognitively- grounded model of alignment that may be generalized to larger crowds.

The paper describes relation between positions of individuals in a crowd, namely dependence between position of a pedestrian and position of his/her nearest neighbors. Two main characteristics have been analyzed: nth nearest neighbors’ spatial and angular distributions. At first sight, people in human crowd seem to be located randomly, however, the authors findings indicate that there are clearly visible patterns in analyzed characteristics. The authors discover symptoms of strong correlations between position of closely located pedestrians. Simple, local movement rules for pedestrian are proposed to explain observed patterns.

Understanding pedestrian walking characteristics is important for the planning and design of mass gathering places for day-to-day activities as well as for emergency evacuations. The optimization of architectural designs and properly managing crowds at public buildings and the built environment is essential to ensure the safety and efficiency of crowd dynamics. Most previous empirical and theoretical studies highlight the behavior of a crowd as a whole system (macroscopic behavior) or interpersonal microscopic interactions within the crowd. A major gap in the knowledge is that no sufficient research has been carried out to examine solo walking characteristics, particularly when individual pedestrians interact with complex geometries such as turning. Whether the existing mathematical or simulation models can accurately reflect the characteristics related to solo human walking characteristics in these conditions is questionable. A series of experiments was conducted to understand the solo walking characteristics of individuals walking through angled corridors at different speeds. Initial results are discussed in detail. Results suggest that an individual tends to reduce speeds within a fixed region on the angled path and that the dimensions of this region are independent of turning angle but dependent on the individual's desired speed. These findings are important for the calibration of parameters or behavioral rules for microscopic pedestrian models.

Over the last years multiple variations of the Social Force model have been proposed. While most of the available force-based models are calibrated on observed human movement data, validation for investigating the model characteristics, e.g. variance in parameter values, is still sparse. The authors present a novel methodology for validating Social Force based models which investigates the reproducibility of human movement behavior on the individual trajectory level with real-world movement data. The approach estimates model parameter values and their distribution with non-linear regression on observed trajectory data, where the resulting variances of the parameter values represent the model's validity. The authors demonstrate the approach on a comprehensive (235 pedestrians) and highly accurate (within a few centimeters) set of human movement trajectories obtained from real-world pedestrian traffic with bidirectional flow using an automatic people tracking approach based on Kinect sensors. The authors validate the Social Force model of Helbing and Molnar (1995), Helbing and Johansson (2009) and Rudloff et al. (2011).

Automated object detection and crowd density estimation are popular and important area in visual surveillance research. The last decades witnessed many significant research in this field however, it is still a challenging problem for automatic visual surveillance. The ever increase in research of the field of crowd dynamics and crowd motion necessitates a detailed and updated survey of different techniques and trends in this field. This paper presents a survey on crowd detection and crowd density estimation from moving platform and surveys the different methods employed for this purpose. This review category and delineates several detections and counting estimation methods that have been applied for the examination of scenes from static and moving platforms.

Given the amount of data publically available relating to the incident at the Duisburg Love Parade of 2010, this event is a valuable case study for the development and management of large-scale crowd circulation and emergency incidents. Here, we study the crowd dynamics associated with a large crowd event similar to that of the Love Parade in Duisburg. The paper discusses the analysis of observations of the incident and explores the application of an evacuation model to investigate similar crowd scenarios. The data-set produced from this analysis was used to configure the buildingEXODUS model to approximate the original incident to verify whether buildingEXODUS can reliably represent agent actions, the conditions that develop and the impact of these developments upon performance. The model is then used to identify potential alternative crowd management and ingress/egress strategies to prevent the crowd crush incident from occurring. Traditionally, fine mesh evacuation models, such as buildingEXODUS, are not used to investigate high-density crowd conditions, because the underlying discretisation of space limits the maximum density that can be achieved within the models. In this paper, a novel approach is used to combine the data of pedestrian flow and space utilisation to indicate the onset of potentially dangerous population densities using a fine node model. The analysis demonstrates that the model can capture the key elements of such a large-scale incident and identify important causal factors. The work demonstrates that the model is broadly able to capture key crowd features if representative assumptions are implemented.

In this work we present a theoretical and experimental study of the natural movement of pedestrians when they walked through a limited and known area. The experimental study was conducted in a shopping center using infrared cameras. We have modeled human pedestrian trajectories by using the principle of least-action principle, which we compared against experimental results.

Video recording right before the Love Parade, Duisburg (Germany) crowd-quake, occurred on 24 July 2010, has been analysed in order to investigate the spatial properties of the crowd (collective case) and those of the single pedestrians in the crowd (individual case). The Grassberger–Procaccia correlation dimension, well known to be able to distinguish patterns in spatial point processes, was used. Our results for this case-study reveal that crowd and single pedestrians are characterized by different spatial behaviour: the whole crowd behaves as a quasi-homogeneous spatial point process through time, with an averaged correlation dimension of about 1.92; while the single pedestrians show a quite large variation of correlation dimensions indicating different spatial patterns, ranging from clustered to quasi-homogeneous.

This paper proposes a critical survey of crowd analysis techniques using visual and non-visual sensors. Automatic crowd understanding has a massive impact on several applications including surveillance and security, situation awareness, crowd management, public space design, intelligent and virtual environments. In case of emergency, it enables practical safety applications by identifying crowd situational context information. This survey identifies different approaches as well as relevant work on crowd analysis by means of visual and non-visual techniques. Multidisciplinary research groups are addressing crowd phenomenon and its dynamics ranging from social, and psychological aspects to computational perspectives. The possibility to use smartphones as sensing devices and fuse this information with video sensors data, allows to better describe crowd dynamics and behaviors. Eventually, challenges and further research opportunities with reference to crowd analysis are exposed.

The modelling and simulation of pedestrians and crowd dynamics require empirical evidences and quantitative data describing the relevant phenomena that models must be able to reproduce. Computer vision can provide several tools both to semi-automatically acquire the demand of a given situation and actually configure a simulation model, as well as to gather information for the sake of model calibration and validation. This paper proposes methods supporting the segmentation and pedestrian counting of crowd flows, the identification and characterisation of main flows in an analysed scene and the detection of social groups in an observed population. The methods are briefly introduced and the achieved results are presented and discussed with reference to the current state of the art.

This paper presents an overview of the mathematical approaches to modeling crowd dynamics by taking into account the complexity of living systems. The contents refer to the classical representation scales (microscopic, kinetic, and macroscopic) and to the mathematical frameworks that can be used for the modeling approach. The analysis focuses on the kinetic scale and, specifically, on developments of the mathematical kinetic theory of particles with heterogeneous behaviors. The existing literature is critically analyzed looking ahead to research perspectives and, more precisely, to a unified modeling strategy in view also of swarm dynamics modeling.

Collective movement is important during emergencies such as natural disasters or terrorist attacks, when rapid egress is essential for escape. The development of quantitative theories and models to explain and predict the collective dynamics of pedestrians has been hindered by the lack of complementary data under emergency conditions. Collective patterns are not restricted to humans, but have been observed in other non-human biological systems. In this study, a mathematical model for crowd panic is derived from collective animal dynamics. The development and validation of the model is supported by data from experiments with panicking Argentine ants (Linepithema humile). A first attempt is also made to scale the model parameters for collective pedestrian traffic from those for ant traffic, by employing a scaling concept approach commonly used in biology.