Crowd Activity Research Articles

A generalized trajectories-based evaluation approach for pedestrian evacuation models

The fundamental diagram and self-organized phenomena in crowds are widely used to test the applicability of evacuation models. These benchmarks are good indicators for the validity of a model, whereas they are insufficient descriptors for the realistic microscopic behaviors of pedestrians. In recent years, the rapid increase of the trajectory datasets which benefits from the development of recognition technologies open the door to new possibilities for an extensive quantitative validation of the models. In this work, a trajectories-based analysis approach which contains types of indexes is proposed. The indexes are a mix of macroscopic type (fundamental diagram index, speed choice index, and direction choice index) and microscopic type (trajectories pattern index), distribution type (route length distribution index, travel time distribution index) and time-series type (starting position distance time-series index, destination position distance time-series index. Moreover, the Kolmogorov-Smirnov (K-S) test as well as the dynamic time warping (DTW) method are introduced to quantify the similarities of results on different types of indexes. In brief, by comparing experimental and simulation trajectories, we can measure a set of performance scores in different perspectives. Here, the Social Force Model (SFM) and Heuristics Model (HM) are respectively introduced and evaluated. According to the proposed evaluation approach, we show that the HM performs better than the SFM. Our analysis approach is model agnostic and is defined in a general way, such that it can be applied for trajectory sets from different experiment settings. This work can help to improve the accuracy of simulation models, and the pedestrian safety in crowd activities and autonomous vehicle navigation will be benefited.

PM2.5 Pollutant Concentrations in Greenspaces of Nanjing Are High but Can Be Lowered with Environmental Planning.

Small-scale greenspaces in high-density central urban districts serve as important outdoor activity spaces for the surrounding residents, especially the elderly. This study selects six small-scale, popular greenspaces with distinct characteristics that are jointly situated along the same main urban artery in a high-density central urban district. Field investigations and questionnaires are conducted and combined with statistical analyses, to explore the spatial-temporal distribution and influencing factors of PM2.5 concentrations in these greenspaces. The study finds that the air quality conditions in the sites are non-ideal, and this has potential negative impacts on the health of the elderly visitors. Moreover, the difference values of PM2.5 concentrations’ spatial-temporal distributions are significantly affected by vehicle-related emissions, which have significant temporal characteristics. PM2.5 concentration is strongly correlated with percentage of green coverage (R = 0.82, p < 0.05), degree of airflow (R = −0.83, p < 0.05), humidity and comfort level (R = 0.54, p < 0.01 and R = −0.40, p < 0.01 respectively). Meanwhile, the sites’ “sky view factor” is strongly correlated with degree of airflow (R = 0.82, p < 0.05), and the comfort level plays an indirect role in the process of PM2.5 affecting crowd activities. Based on this analysis, an optimal set of index ranges for greenspace elements which are correlated with the best reduction in PM2.5 concentrations is derived. As such, this research reveals the technical methods to best reduce their concentrations and provides a basis and reference for improving the quality of small-scale greenspaces in high-density urban districts for the benefit of healthy aging.

Exploring the epidemiological changes of common respiratory viruses since the COVID-19 pandemic: a hospital study in Hangzhou, China.

Adenovirus, respiratory syncytial virus, and influenza virus are common causes of respiratory infections. The COVID-19 pandemic had a significant impact on their prevalence. The aim of this study was to analyze the epidemic changes of common respiratory viruses in the Affiliated Hospital of Hangzhou Normal University in Hangzhou, China, from October of 2017 to February of 2021. We collected statistics from 121,529 patients in the outpatient and inpatient departments of the hospital who had throat or nose swabs collected for testing for four virus antigens by the colloidal gold method. Of these, 13,200 (10.86%) were positive for influenza A virus, 8,402 (6.91%) were positive for influenza B virus, 6,056 (4.98%) were positive for adenovirus, and 4,739 (3.90%) were positive for respiratory syncytial virus. The positivity rates of the influenza A virus (0-14 years old, P = 0.376; over 14 years old, P = 0.197) and respiratory syncytial virus (0-14 years old, P = 0.763; over 14 years old, P = 0.465) did not differ significantly by gender. After January of 2020, influenza virus infection decreased significantly. The positivity rate of respiratory syncytial virus remained high, and its epidemic season was similar to before. Strict respiratory protection and regulation of crowd activities have a great impact on the epidemic characteristics of viruses. After major changes in the public health environment, virus epidemics and their mutations should be monitored closely, extensively, and continuously.

Vitality evaluation of the waterfront space in the ancient city of Suzhou

With a history spanning thousands of years, the water system of Suzhou is an indispensable carrier of urban space and civil culture. Quantificational model analysis of the waterfront in the ancient city of Suzhou has significant implications for the future design of waterfront space and the establishment of an evaluation method to determine the vitality of such space. In this study, a vitality evaluation model was first constructed using river attributes, the spatial type of waterfront areas, vision accessibility, transportation accessibility, and combined new data extracted using spatial factor analysis. Second, a vitality evaluation matrix was established using the analytic hierarchy process to simulate the vitality of waterfront spaces. Third, a hash algorithm was employed to determine the fitting degree between the vitality model of Suzhou's waterfront space and crowd activity. The different areas between them were found and then the factor evaluation process was adjusted on this basis of analyzing the causes. Thus, this study identified the factors influencing the vitality of Suzhou's waterfront. Furthermore, this research constructed a model for evaluating the vitality of waterfront spaces. Finally, some guidelines were presented regarding the design and implementation of waterfront spaces in urban design.

Group Behavior Pattern Recognition Algorithm Based on Spatio-Temporal Graph Convolutional Networks

With the rapid growth of population, more diverse crowd activities, and the rapid development of socialization process, group scenes are becoming more common, so the demand for modeling, analyzing, and understanding group behavior data in video is increasing. Compared with the previous work on video content analysis, factors such as the increasing number of people in the group video and the more complex scene make the analysis of group behavior in video face great challenges. Therefore, a group behavior pattern recognition algorithm based on spatio-temporal graph convolutional network is proposed in this paper, aiming at group density analysis and group behavior recognition in the video. A crowd detection and location method based on density map regression-guided classification was designed. Finally, a crowd behavior analysis method based on density grade division was designed to complete crowd density analysis and video group behavior detection. In addition, this paper also proposes to extract spatio-temporal features of crowd posture and density by using the double-flow spatio-temporal map network model, so as to effectively capture the differentiated movement information among different groups. Experimental results on public datasets show that the proposed method has high accuracy and can effectively predict group behavior.

Crowd activity recognition in live video streaming via 3D‐ResNet and region graph convolution network

Crowd activity recognition in live video streaming via 3D‐ResNet and region graph convolution network

Study on the Transformation Strategy of Chinese Classical Gardens in Urban Public Space: Take Anning Nanyuan as an Example

Currently, urban parks are booming, but many public gardens are out of touch with modern city life. It is necessary to find out the functional positioning of public gardens and carry out the transformation to conform to the development of urban life, so that it can play a unique value in the urban space where all kinds of modern landscapes are emerging. This paper takes Anning Nanyuan and its adjacent Baihua Park as examples to compare and analyze the characteristics of the internal crowd activities of the two, explore the similarities and differences between public classical gardens and modern parks as urban landscape spatial identities, and provide references for the scientific development of public classical gardens in the future.

Analysis of Bus Accessibility Based on GIS and Big Data of Crowd -- Taking Wansheng Economic Development Zone as an example

Public transportation issues are increasingly becoming a key constraint to the sustainable development of urban economic and social development. Based on crowd big data and GIS spatial analysis methods, this article analyzes the accessibility of the public transportation in Wansheng Economic and Technological Development Zone (hereinafter referred to as the development zone). A comprehensive evaluation and analysis was conducted to study the matching relationship between population density and existing public transportation stations, and to propose the optimal suggestions. The results show that the overall public transportation accessibility in the development zone is average level, and the public transportation accessibility is better in the densest crowded areas, but in the east and southeast of the development zone, crowd activities are poorly matched with the public transportation network, and residents’ travel demands can’t get public transportation with timely responses.

Overview of the characteristics of and responses to the three waves of COVID-19 in Japan during 2020-2021.

The first case of COVID-19 in Japan was reported on 16 January 2020. The total number of the infected has reached 313,844 and the number of deaths has reached 4,379 as of 16 January 2021. This article reviews the characteristics of and responses to the three waves of COVID-19 in Japan during 2020-2021 in order to provide a reference for the next step in epidemic prevention and control. The Japanese Government declared a state of emergency on 7 April 2020, which suppressed the increase in the number of the infected by curtailing economic activity. The first wave peaked at 701 new cases a day and it decreased to 21 new cases on May 25 when the state of emergency was lifted. However, the number of the infected increased again due to the resumption of economic activity, with a peak of 1,762 new cases a day during the second wave. Although the situation was worse than that during the first wave, the government succeeded in limiting the increase without declaring a state of emergency again, and that may be attributed to a decrease in crowd activities and an increase in the number of inspections. During the third wave, the number of the infected continued to exceed the peak during previous waves for two months. Major factors for this rise include the government's implementation of further policies to encourage certain activities, relaxed immigration restrictions, and people not reducing their level of activity. An even more serious problem is the bed usage for patients with COVID-19; bed usage exceeds 50% not only in major cities but also in various areas. On 7 January 2021, 5,953 new cases were reported a day; this greatly exceeded the previous peak, and the state of emergency was declared again. Although Japan has been preparing its medical system since the first wave, maintaining that system has imposed a large economic burden on medical facilities, hence stronger measures and additional support are urgently needed to combat COVID-19 in the coming few months.

Prediction of floor responses to crowd bouncing loads by response reduction factor and spectrum method

Bouncing is a typical rhythmic crowd activity in entertaining venues, such as concert halls and stadia. When the activity’s frequency is close to the natural frequency of the occupied structure, the corresponding bouncing loads can cause intense structural vibrations resulting in vibration serviceability problems, even structural damage. This study suggests a method for prediction of vibration response due to crowd bouncing by a response reduction factor (RRF) in conjunction with a previously established response spectrum approach pertinent to a single person bouncing. The RRF is defined as a ratio between structural responses with and without taking into account synchronization of body movements of individuals in a bouncing crowd. The variations of RRF with number of persons, structural frequency, bouncing frequency and structural damping ratios have been studied using experimental records of crowd bouncing loads. Based on the findings a practical design curve for RRF has been proposed. Application of the proposed method has been validated on numerical simulations and field measurements of a long-span floor subjected to crowd bouncing loads.

Supervised Method for Congestion Detection at Entry and Exit Corridors of Public Places

Continuous monitoring and automatic detection of crowd activities is extremely helpful for management at public places to avoid any possible disaster. Analysis of crowded scene is a critical task as it typically involves poor resolution of objects, occlusions and complex dynamics. In this paper, we propose a novel, systematic and generalized method based on global motion analysis of people to detect Congestion situation in crowded scenes at entry/exit corridors. Our approach is tested on video footages acquired from surveillance cameras installed at exit corridors of public places. The results show the expediency of our approach.

The Authority of the Police in Issuance of Music Performance Permits as Efforts to Control Security and Order

Aim This study is to determine and analyze the regulatory procedures for issuing music stage permits, the authority of the National Police in issuing permits for music performances as an effort to control security and order and factors inhibiting Polri's authority in issuing permits for music performances as an effort to control security and order. This study uses an empirical juridical approach. The conclusion of this research is about the Procedure for Issuance of Music Performance Permits, namely; The applicant brings the requirements (photocopy of ID card and musical performance record, then the officer provides a Community Service Accountability Statement, Environmental Permit Statement, Location Permit Statement, after completing a Music Performance Permit issued by the Chief of Police, which is then submitted to the permit applicant. . the authority of the National Police in issuing permits for music performances as an effort to control security and order. Provide permits and supervise public crowd activities and other community activities, Fostering the community to increase community participation, public legal awareness and obedience to the laws and regulations of the community, Maintain orderliness and ensure public safety. factors inhibiting the authority of the National Police in issuing musical performances as an effort to control security and order. There is a music stage permit that has not extended or does not make a Performance Recommendation, The time for a sudden permit application should be 7 (seven) days before the activity or event takes place, When the environmental permit statement is not approved by the environment around the location of the event

Using Wi-Fi probe and location data to analyze the human distribution characteristics of green spaces: A case study of the Yanfu Greenland Park, China

Public open space constitutes a significant part of the sustainable urban environment. Urban green space—which includes parks, sports fields, and wetlands—is a crucial type of public open space in a healthy urban ecosystem. Most studies on urban green spaces comprise large-scale objective evaluations, with relatively few related theoretical studies from the perspective of human behavior. This is predominantly due to the technical constraints involved in obtaining small-scale public open space population activity data. To address this issue, this study examines the crowd activities of an urban green space by combining Wi-Fi probe and location data to discern population distribution characteristics. This study uses Yanfu Greenland Park, a green space in a central area of Shanghai—a typical high-density city—as a case study. This study proposes a technical method for measuring population distribution in a small-scale public space, demonstrating its applicability through an analysis of the population density distribution and activity trajectory in an urban green space. The methodology demonstrated in this study can help urban planning designers and managers create more successful public open spaces. This technical method may also assist urban planning and related research on different scales.

Crowdsourced Innovation: How Community Managers Affect Crowd Activities

In this study, we investigate whether and to what extent community managers in online collaborative communities can stimulate crowd activities through their engagement. Using a novel data set of 22 large online idea crowdsourcing campaigns, we find that active engagement of community managers positively affects crowd activities in an inverted U-shaped manner. Moreover, we evidence that intellectual stimulation by managers increases community participation, while individual consideration of users has no impact on user activities. Finally, the data reveal that community manager activities that require more effort, such as media file uploads instead of simple written comments, have a larger effect on crowd participation.

Behavior modeling method and its application in combat simulation

Behavior Modeling and Simulation is an important method to analyze macro-and micro-behaviors of groups and individuals. It can predict and analyze the law of crowd activity under a certain density and provide important support for the study of social and economic activities and war and military activities. It has been widely used in emergency response, combat simulation and many other fields. This paper reviews behavioral modeling and simulation, mainly from the current situation of behavioral simulation applications, the main methods of behavioral modeling and the needs of future development direction. Firstly, the application status of behavior simulation is analyzed from both military and social aspects. In the aspect of social behavior simulation, it mainly analyses several aspects, such as safe evacuation, automatic driving, road traffic planning, intelligent robots and so on. In the aspect of military behavior simulation, it mainly analyses individual combat behavior simulation, battlefield element model construction, combat action combination model, multi-agent coupling simulation and so on. Secondly, the method of behavior modeling is deeply analyzed, and the main models of behavior modeling are summarized, including social force model, cellular automata model, agent model, etc. At the same time, the behavior model of individual’s multiple forces in a certain environment and the emergence effect model of group are deeply analyzed. Finally, based on the above two aspects of research and analysis results, this paper aims at the simulation of multi-person cooperative attack and defense, which is a military behavior simulation problem. From the four aspects of dynamic environmental factors, operational rules constraints, enemy threat factors and multi-agent cooperative control, this paper puts forward several problems that need to be further improved in cooperative attack and defense.

Petitioning and Petitioners to the Westminster Parliament, 1660–1788

AbstractNarratives of the mid‐18th‐century state as drifting towards oligarchy and closure have long been superseded by accounts stressing its responsiveness to outside interests and opinion. Participatory election rituals, crowd activity and the expansion of print media demonstrate that political activism was not limited to a narrow circle of elites, even after the passage of the 1716 Septennial Act. Additionally, as an institution after 1688 the Westminster parliament rendered itself useful to propertied society through its passage of greater amounts of legislation. However, while the strength of party and trajectory of electoral participation is well mapped, as are addresses to the crown, the petitioning of interest groups has had few systematic quantitative surveys. This article first sets out the chronology of petitioning to both houses of parliament from 1660 to 1788, demonstrating that petitioning was at its height before 1722, rather than rising to its greatest levels with the reform movements in the 1780s. Second, the article demonstrates the role of petitioning in expanding the political nation beyond the boundaries of parliamentary boroughs, and why, on occasion, petitioning involved women and illiterate men. Third, in order to emphasize the continuity in petitioning and its contribution to the formation of public opinion, the article compares the different roles that concurrent subscription practices envisaged for the public. Finally, the article argues that despite the rising tendency to cite numerical strength to arbitrate political divisions, petitioners continued to legitimise their voices on the basis of representing collections of reasons and interests rather than opinion.

The Social Rank of Zoo-Housed Japanese Macaques is a Predictor of Visitor-Directed Aggression.

Simple SummaryMillions of people visit zoological parks around the world every year. As a result, there has been significant interest in how the presence of these visitors affects the behavior and wellbeing of zoo animals, of which primates are a group that has been extensively studied in this regard. However, many studies fail to consider the possibility that group dynamics, particularly social rank, may affect an individual animal’s reaction to visitors. We investigated how visitor presence, both crowd size and activity level, along with the individual rank of zoo-housed monkeys (Japanese macaques) influenced the occurrence of behavior directed toward the visitors. Behaviors directed toward visitors were predominantly threatening. The results revealed that the social rank of a monkey was a significant predictor of visitor-directed aggression, and lower-ranked individuals displayed more frequent aggression toward visitors. We think it is likely that this finding is a result of low ranking individuals redirecting their aggression to visitors as safe targets. Furthermore, the activity level of the visitors affected visitor-directed aggression by the monkeys more so than the size of the crowd. These findings highlight that although environmental factors impact the behavior of animals at the zoo, social rank is also important to consider when understanding how animals may react to zoo visitors.The effect that visitors have on the behavior and welfare of animals is a widely-studied topic in zoo animal welfare. Typically, these studies focus on how the presence or activity levels of visitors affect animals. However, for many species, and particularly primates, social factors, such as social rank, can also have a large impact on behavior. Here, we considered the influence of both the role of visitors (crowd size and activity levels) and rank on the occurrence of visitor-directed aggression by zoo-housed Japanese macaques (Macaca fuscata, N = 12). We conducted 52 weeks of observation (443.8 h) of macaques living in a large outdoor habitat and recorded 1574 events of visitor-directed behavior, 94.2% of which was characterized as aggressive. We calculated rank using the Elo-rating method. GLMM comparisons indicate that rank was a significant predictor of visitor-directed aggression, with lower-ranked individuals displaying more frequent aggression towards visitors. Additionally, visitor-directed aggression differed by crowd activity levels, but not crowd size. These results support our prediction that rank is associated with differences in visitor-directed aggression, and we interpret this pattern as lower-ranking macaques redirecting aggression toward zoo visitors as safe targets. This work emphasizes how factors emanating from the zoo environment can combine with social dynamics to influence primate response to human presence in the zoo setting.

FBVA: A Flow-Based Visual Analytics Approach for Citywide Crowd Mobility

Analyzing structures of crowd mobility at city level is a challenging task due to the complex crowd mobility and dynamic changes generated by the social activities over time. These structures, defined as high-dimensional mobility structures (HMSs), contain spatiotemporal information and are simultaneously influenced by the geographical distributions and daily activities of citywide crowd. However, few work has been dedicated to depict and analyze these structures, mainly due to the lack of effective models. In this paper, we propose to model the crowd mobility as a dynamical system and characterize the irregular mobility data with a novel local coherence of sparse field (LCSF) algorithm. The proposed algorithm makes it possible to measure the separation behavior of trajectories in an irregular and sparse topology network. Detected HMS, referred as local separation measure of LCSF, divides the geographical urban areas into distinct functional regions over time. We design and implement a visual analytics system to facilitate situation-aware analysis of a huge amount of crowd mobility and their socialized behaviors. Case studies based on a real-world data set demonstrate the effectiveness of the proposed approach.

Analysis of structures subjected to crowd loads

The movement of people on any structure induces dynamic crowd load. The enthusiastic potential behavior of people from overwhelming happiness in concert, shows etc. on structures such as stadiums, auditorium, grandstands, bridges, malls and convention centers causes synchronized rhythmic movements. Resonance between forcing frequency and one or more natural frequencies of the structure resulting from human-structure synchronization leads to strong vibrations. Such vibrations not only affect structural stability, but also create discomfort among people. Pedestrian induced lateral vibration of Millennium Bridge, London is a classic example for the same. Crowd loads impose vertical as well as horizontal loads on the structure. Both these loads induce vibrations in corresponding directions. Beyond an acceptable limit of vibration level, people may feel uncomfortable. If there is a mode of low natural frequency, even a significantly low forcing frequency can lead to strong vibrations, which in turn may cause panic. The present research focuses on analysis of a simply supported slab strip subjected to crowd load. The crowd-structure interaction is analytically modelled and the modal parameters and the response of the occupied structure are determined. Furthermore, the effect of various parameters such as crowd size, crowd location and crowd activity on the behavior of occupied structure is studied. The comfort level of the people on the structure is also estimated. It is observed that crowd parameters affect the behavior of the system as well as the comfort level of the occupants. It is further seen that the trend of variation of natural frequency of the occupied structure with variation in crowd properties changes with change in the empty structure properties.

IEEE Access Special Section Editorial: Visual Surveillance and Biometrics: Practices, Challenges, and Possibilities

Visual surveillance is the latest paradigm for social security through machine intelligence. It includes the use of visual data captured by infrared sensors or visible-light cameras mounted in cars, corridors, traffic signals etc. Visual surveillance facilitates the classification of human behavior, crowd activity, and gesture analysis to achieve application-specific objectives.