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Journal articles on the topic 'Crowd Flow'
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1
Lin, Ziqian, Jie Feng, Ziyang Lu, Yong Li, and Depeng Jin. "DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1020–27. http://dx.doi.org/10.1609/aaai.v33i01.33011020.
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Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds entering a region in a given time interval) and outflow (the traffic of crowds leaving a region for other places) of each region in the city with knowing the historical flow data. In this paper, we propose DeepSTN+, a deep learning-based convolutional model, to predict crowd flows in the metropolis. First, DeepSTN+ employs the ConvPlus structure to model the longrange spatial dependence among crowd flows in different regions. Further, PoI distributions and time factor are combined to express the effect of location attributes to introduce prior knowledge of the crowd movements. Finally, we propose an effective fusion mechanism to stabilize the training process, which further improves the performance. Extensive experimental results based on two real-life datasets demonstrate the superiority of our model, i.e., DeepSTN+ reduces the error of the crowd flow prediction by approximately 8%∼13% compared with the state-of-the-art baselines.
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Feng, Jie, Yong Li, Ziqian Lin, Can Rong, Funing Sun, Diansheng Guo, and Depeng Jin. "Context-aware Spatial-Temporal Neural Network for Citywide Crowd Flow Prediction via Modeling Long-range Spatial Dependency." ACM Transactions on Knowledge Discovery from Data 16, no. 3 (June 30, 2022): 1–21. http://dx.doi.org/10.1145/3477577.
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Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims at predicting the inflow (the traffic of crowds entering a region in a given time interval) and outflow (the traffic of crowds leaving a region for other places) of each region in the city with knowing the historical flow data. In this article, we propose DeepSTN+, a deep learning-based convolutional model, to predict crowd flows in the metropolis. First, DeepSTN+ employs the ConvPlus structure to model the long-range spatial dependence among crowd flows in different regions. Further, PoI distributions and time factor are combined to express the effect of location attributes to introduce prior knowledge of the crowd movements. Finally, we propose a temporal attention-based fusion mechanism to stabilize the training process, which further improves the performance. Extensive experimental results based on four real-life datasets demonstrate the superiority of our model, i.e., DeepSTN+ reduces the error of the crowd flow prediction by approximately 10%–21% compared with the state-of-the-art baselines.
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JOHANSSON, ANDERS, DIRK HELBING, HABIB Z. AL-ABIDEEN, and SALIM AL-BOSTA. "FROM CROWD DYNAMICS TO CROWD SAFETY: A VIDEO-BASED ANALYSIS." Advances in Complex Systems 11, no. 04 (August 2008): 497–527. http://dx.doi.org/10.1142/s0219525908001854.
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The study of crowd dynamics is interesting because of the various self-organization phenomena resulting from the interactions of many pedestrians, which may improve or obstruct their flow. Besides formation of lanes of uniform walking direction and oscillations at bottlenecks at moderate densities, it was recently discovered that stop-and-go waves [D. Helbing et al., Phys. Rev. Lett.97 (2006) 168001] and a phenomenon called "crowd turbulence" can occur at high pedestrian densities [D. Helbing et al., Phys. Rev. E75 (2007) 046109]. Although the behavior of pedestrian crowds under extreme conditions is decisive for the safety of crowds during the access to or egress from mass events as well as for situations of emergency evacuation, there is still a lack of empirical studies of extreme crowding. Therefore, this paper discusses how one may study high-density conditions based on suitable video data. This is illustrated at the example of pilgrim flows entering the previous Jamarat Bridge in Mina, 5 kilometers from the Holy Mosque in Makkah, Saudi-Arabia. Our results reveal previously unexpected pattern formation phenomena and show that the average individual speed does not go to zero even at local densities of 10 persons per square meter. Since the maximum density and flow are different from measurements in other countries, this has implications for the capacity assessment and dimensioning of facilities for mass events. When conditions become congested, the flow drops significantly, which can cause stop-and-go waves and a further increase of the density until critical crowd conditions are reached. Then, "crowd turbulence" sets in, which may trigger crowd disasters. For this reason, it is important to operate pedestrian facilities sufficiently below their maximum capacity and to take measures to improve crowd safety, some of which are discussed in the end.
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He Wu, Li Qi, and Yunbo Rao. "Crowd Flow-Based Information for Crowd Simulation." International Journal of Digital Content Technology and its Applications 6, no. 23 (December 31, 2012): 145–52. http://dx.doi.org/10.4156/jdcta.vol6.issue23.17.
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Han, Bing, Daoye Zhu, Chengqi Cheng, Jiawen Pan, and Weixin Zhai. "Patterns of Nighttime Crowd Flows in Tourism Cities Based on Taxi Data—Take Haikou Prefecture as an Example." Remote Sensing 14, no. 6 (March 15, 2022): 1413. http://dx.doi.org/10.3390/rs14061413.
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The study of patterns of crowd flows represents an emerging and expanding research field. The most straightforward and efficient approach to investigate the patterns of crowd flows is to concentrate on traffic flow. However, assessments of simple point-to-point movement frequently lack universal validity, and little research has been conducted on the regularity of nighttime movement. Due to the suspension of public transportation at night, taxi orders are critical in capturing the features of nighttime crowd flows in a tourism city. Using Haikou as an example, this paper proposes a mixed Geogrid Spatio-temporal model (MG-STM) for the tourism city in order to address the challenges. Firstly, by collecting the pick-up/drop-off/in-out flow of crowds, this research uses DCNMF dimensionality reduction to extract semi-supervised spatio-temporal variation features and the K-Means clustering method to determine the cluster types of nighttime crowd flows’ changes in each geogrid. Secondly, by constructing a mixed-evaluation model based on LJ1-01 nighttime light data, crowd flows’ clusters, and land use data in geogrid-based regions, the pattern of nighttime crowd flows in urban land use areas is successfully determined. The results suggest that MG-STM can estimate changes in the number of collective flows in various regions of Haikou effectively and appropriately. Moreover, population density of land use areas shows a high positive correlation with the lag of crowd flows. Each 5% increase in population density results in a 30-min delay in the peak of crowd flows. The MG-STM will be extremely beneficial in developing and implementing systems for criminal tracking and pandemic prevention.
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Zang, Tianzi, Yanmin Zhu, Yanan Xu, and Jiadi Yu. "Jointly Modeling Spatio–Temporal Dependencies and Daily Flow Correlations for Crowd Flow Prediction." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (June 2021): 1–20. http://dx.doi.org/10.1145/3439346.
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Crowd flow prediction is a vital problem for an intelligent transportation system construction in a smart city. It plays a crucial role in traffic management and behavioral analysis, thus it has raised great attention from many researchers. However, predicting crowd flows timely and accurately is a challenging task that is affected by many complex factors such as the dependencies of adjacent regions or recent crowd flows. Existing models mainly focus on capturing such dependencies in spatial or temporal domains and fail to model relations between crowd flows of distant regions. We notice that each region has a relatively fixed daily flow and some regions (even very far away from each other) may share similar flow patterns which show strong correlations among them. In this article, we propose a novel model named Double-Encoder which follows a general encoder–decoder framework for multi-step citywide crowd flow prediction. The model consists of two encoder modules named ST-Encoder and FR-Encoder to model spatial-temporal dependencies and daily flow correlations, respectively. We conduct extensive experiments on two real-world datasets to evaluate the performance of the proposed model and show that our model consistently outperforms state-of-the-art methods.
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Zuo, Zhongyi, Wei Yin, Guangchuan Yang, Yunqi Zhang, Jiawen Yin, and Hongsheng Ge. "Determination of Bus Crowding Coefficient Based on Passenger Flow Forecasting." Journal of Advanced Transportation 2019 (April 1, 2019): 1–12. http://dx.doi.org/10.1155/2019/2751916.
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To improve bus passengers’ degree of comfort, it is necessary to determine the real-time crowd coefficient in the bus. With this concern, this paper employed the RBF Neural Networks approach to predict the number of passengers in the bus based on historical data. To minimize the impact of the randomness of passenger flow on the determination of bus crowd coefficient, a cloud model-based bus crowd coefficient identification method was proposed. This paper first selected the performance measurements for determining bus crowd coefficient and calculated the digital characteristics of the cloud model based on the boundary values of the selected performance measures under six Levels-of-Service (LOSs). Then the subclouds obtained under the six LOSs were synthesized into a standard cloud. According to the predicted number of passengers in the bus, the passenger density and loading frequency were calculated, which were imported into the cloud generator to set up the bus crowd coefficient identification model. By calculating the crowd degrees of identification cloud and template cloud at each site, this paper determined the crowed coefficient of each bus station. Finally, this paper took the bus line No. 10 in Dalian city as case study to verify the proposed model. It was found that the crowd coefficients of the selected route ranged from 60.265 to 109.825, and the corresponding LOSs ranged between C and F. The method of discriminating bus crowding coefficient can not only effectively determine the congestion coefficient, but also effectively avoid the fuzziness and randomness of the crowding coefficient judgment in the bus, which has strong theoretical and practical significance.
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Zhou, Yirong, Hao Chen, Jun Li, Ye Wu, Jiangjiang Wu, and Luo Chen. "Large-Scale Station-Level Crowd Flow Forecast with ST-Unet." ISPRS International Journal of Geo-Information 8, no. 3 (March 13, 2019): 140. http://dx.doi.org/10.3390/ijgi8030140.
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High crowd mobility is a characteristic of transportation hubs such as metro/bus/bike stations in cities worldwide. Forecasting the crowd flow for such places, known as station-level crowd flow forecast (SLCFF) in this paper, would have many benefits, for example traffic management and public safety. Concretely, SLCFF predicts the number of people that will arrive at or depart from stations in a given period. However, one challenge is that the crowd flows across hundreds of stations irregularly scattered throughout a city are affected by complicated spatio-temporal events. Additionally, some external factors such as weather conditions or holidays may change the crowd flow tremendously. In this paper, a spatio-temporal U-shape network model (ST-Unet) for SLCFF is proposed. It is a neural network-based multi-output regression model, handling hundreds of target variables, i.e., all stations’ in and out flows. ST-Unet emphasizes stations’ spatial dependence by integrating the crowd flow information from neighboring stations and the cluster it belongs to after hierarchical clustering. It learns the temporal dependence by modeling the temporal closeness, period, and trend of crowd flows. With proper modifications on the network structure, ST-Unet is easily trained and has reliable convergency. Experiments on four real-world datasets were carried out to verify the proposed method’s performance and the results show that ST-Unet outperforms seven baselines in terms of SLCFF.
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Muhammed Anees, V., and G. Santhosh Kumar. "Identification of crowd behaviour patterns using stability analysis." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 2829–43. http://dx.doi.org/10.3233/jifs-200667.
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Crowd behaviour analysis and management have become a significant research problem for the last few years because of the substantial growth in the world population and their security requirements. There are numerous unsolved problems like crowd flow modelling and crowd behaviour detection, which are still open in this area, seeking great attention from the research community. Crowd flow modelling is one of such problems, and it is also an integral part of an intelligent surveillance system. Modelling of crowd flow has now become a vital concern in the development of intelligent surveillance systems. Real-time analysis of crowd behavior needs accurate models that represent crowded scenarios. An intelligent surveillance system supporting a good crowd flow model will help identify the risks in a wide range of emergencies and facilitate human safety. Mathematical models of crowd flow developed from real-time video sequences enable further analysis and decision making. A novel method identifying eight possible crowd flow behaviours commonly seen in the crowd video sequences is explained in this paper. The proposed method uses crowd flow localisation using the Gunnar-Farneback optical flow method. The Jacobian and Hessian matrix analysis along with corresponding eigenvalues helps to find stability points identifying the flow patterns. This work is carried out on 80 videos taken from UCF crowd and CUHK video datasets. Comparison with existing works from the literature proves our method yields better results.
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Xia, Tong, Junjie Lin, Yong Li, Jie Feng, Pan Hui, Funing Sun, Diansheng Guo, and Depeng Jin. "3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction." ACM Transactions on Knowledge Discovery from Data 15, no. 6 (June 28, 2021): 1–21. http://dx.doi.org/10.1145/3451394.
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Crowd flow prediction is an essential task benefiting a wide range of applications for the transportation system and public safety. However, it is a challenging problem due to the complex spatio-temporal dependence and the complicated impact of urban structure on the crowd flow patterns. In this article, we propose a novel framework, 3- D imensional G raph C onvolution N etwork (3DGCN), to predict citywide crowd flow. We first model it as a dynamic spatio-temporal graph prediction problem, where each node represents a region with time-varying flows, and each edge represents the origin–destination (OD) flow between its corresponding regions. As such, OD flows among regions are treated as a proxy for the spatial interactions among regions. To tackle the complex spatio-temporal dependence, our proposed 3DGCN can model the correlation among graph spatial and temporal neighbors simultaneously. To learn and incorporate urban structures in crowd flow prediction, we design the GCN aggregator to be learned from both crowd flow prediction and region function inference at the same time. Extensive experiments with real-world datasets in two cities demonstrate that our model outperforms state-of-the-art baselines by 9.6%∼19.5% for the next-time-interval prediction.
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Liu, Menghang, Luning Li, Qiang Li, Yu Bai, and Cheng Hu. "Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network." ISPRS International Journal of Geo-Information 10, no. 7 (July 2, 2021): 455. http://dx.doi.org/10.3390/ijgi10070455.
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Open public places, such as pedestrian streets, parks, and squares, are vulnerable when the pedestrians thronged into the sidewalks. The crowd count changes dynamically over time with various external factors, such as surroundings, weekends, and peak hours, so it is essential to predict the accurate and timely crowd count. To address this issue, this study introduces graph convolutional network (GCN), a network-based model, to predict the crowd flow in a walking street. Compared with other grid-based methods, the model is capable of directly processing road network graphs. Experiments show the GCN model and its extension STGCN consistently and significantly outperform other five baseline models, namely HA, ARIMA, SVM, CNN and LSTM, in terms of RMSE, MAE and R2. Considering the computation efficiency, the standard GCN model was selected to predict the crowd. The results showed that the model obtains superior performances with higher prediction precision on weekends and peak hours, of which R2 are above 0.9, indicating the GCN model can capture the pedestrian features in the road network effectively, especially during the periods with massive crowds. The results will provide practical references for city managers to alleviate road congestion and help pedestrians make smarter planning and save travel time.
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Xu, Han, Xiangxia Ren, Weiguo Song, Jun Zhang, and Rayyan Saidahmed. "Spatial and temporal analysis of the bottleneck flow under different walking states with a moving obstacle." Journal of Statistical Mechanics: Theory and Experiment 2023, no. 1 (January 1, 2023): 013401. http://dx.doi.org/10.1088/1742-5468/aca2a2.
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Abstract The regulation of a moving obstacle on crowd movement offers the possibility to enhance evacuation efficiency in emergency situations. In this paper, a series of controlled experiments are conducted to study the effect of the moving obstacle on crowd dynamics for pedestrians in three different competitive levels, which respectively correspond to three different walking states. The enhancement effects of the moving obstacle on evacuation efficiency for the crowd in the dual-task and high-motivated walking states are confirmed, and the positions of the moving obstacle are crucial. It is found that the moving obstacle diminishes the order of the trajectories for the crowd in the dual-task and normal walking states, while it boosts near the exit for the crowd in the high-motivated walking state. And the moving obstacle makes the linear backward propagations of stop-and-go wave disappear for the crowd in the dual-task and high-motivated walking states, but the frequency of stop behavior increases for the crowd in the dual-task and normal walking states. The profiles of evacuation time show that the moving obstacle impedes the pedestrian flow from the front of the exit and increases evacuation efficiency for the pedestrians near the walls of the exit. The analysis of time headway suggests that the moving obstacle with a gap of 1.0 m or 1.2 m to the exit can reduce the number of the pedestrians waiting near the exit for the crowd in the dual-task and high-motivated walking states. Besides, the gap of 0.8 m between the moving obstacle and the exit makes the conflicts at the exit is increased, but the gap of 1.0 m or 1.2 m makes the number of conflicts at the exit be reduced. This study helps the evacuation management of dense crowds and improves the design of facilities to facilitate pedestrian traffic.
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Yugendar, Poojari, and K. V. R. Ravishankar. "Crowd Behavioural Analysis at a Mass Gathering Event." Journal of KONBiN 46, no. 1 (June 1, 2018): 5–20. http://dx.doi.org/10.2478/jok-2018-0020.
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Abstract Religious occasions, gathering at fairs and terminals, are the events of crowd gatherings. Such gatherings act as severe threats for crowds because of high density in less space, which ends up in adverse outcomes resulting in crowd stampedes. The movement of an individual person in a crowd is influenced by the physical factors. In the present study, characteristics like age, gender, group size, child holding, child carrying, people with luggage and without luggage are considered for crowd behaviour analysis. The average speed of the crowd movement was observed as 0.86 m/s. The statistical analysis concluded that there was a significant effect of age, gender, density and luggage on the crowd walking speed. Multi-linear regression (MLR) model was developed between crowd speed and significant factors observed from the statistical analysis. Location 1 data was used for the model development. This developed model was validated using Location 2 data. Gender has more significant effect on speed followed by luggage and age. This study helps in proper dispersal of crowd in a planned manner to that of diversified directional flow that exist during crowd gathering events.
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Riddell, Hugh, and Markus Lappe. "Heading Through a Crowd." Psychological Science 29, no. 9 (July 13, 2018): 1504–14. http://dx.doi.org/10.1177/0956797618778498.
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The ability to navigate through crowds of moving people accurately, efficiently, and without causing collisions is essential for our day-to-day lives. Vision provides key information about one’s own self-motion as well as the motions of other people in the crowd. These two types of information (optic flow and biological motion) have each been investigated extensively; however, surprisingly little research has been dedicated to investigating how they are processed when presented concurrently. Here, we showed that patterns of biological motion have a negative impact on visual-heading estimation when people within the crowd move their limbs but do not move through the scene. Conversely, limb motion facilitates heading estimation when walkers move independently through the scene. Interestingly, this facilitation occurs for crowds containing both regular and perturbed depictions of humans, suggesting that it is likely caused by low-level motion cues inherent in the biological motion of other people.
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Huang, Shaonian, Dongjun Huang, and Mansoor Ahmed Khuhro. "Crowd Motion Analysis Based on Social Force Graph with Streak Flow Attribute." Journal of Electrical and Computer Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/492051.
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Over the past decades, crowd management has attracted a great deal of attention in the area of video surveillance. Among various tasks of video surveillance analysis, crowd motion analysis is the basis of numerous subsequent applications of surveillance video. In this paper, a novel social force graph with streak flow attribute is proposed to capture the global spatiotemporal changes and the local motion of crowd video. Crowd motion analysis is hereby implemented based on the characteristics of social force graph. First, the streak flow of crowd sequence is extracted to represent the global crowd motion; after that, spatiotemporal analogous patches are obtained based on the crowd visual features. A weighted social force graph is then constructed based on multiple social properties of crowd video. The graph is segmented into particle groups to represent the similar motion patterns of crowd video. A codebook is then constructed by clustering all local particle groups, and consequently crowd abnormal behaviors are detected by using the Latent Dirichlet Allocation model. Extensive experiments on challenging datasets show that the proposed method achieves preferable results in the application of crowd motion segmentation and abnormal behavior detection.
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Zhang, Jin, Sheng Chen, Sen Tian, Wenan Gong, Guoshan Cai, and Ying Wang. "A Crowd Counting Framework Combining with Crowd Location." Journal of Advanced Transportation 2021 (February 17, 2021): 1–14. http://dx.doi.org/10.1155/2021/6664281.
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In the past ten years, crowd detection and counting have been applied in many fields such as station crowd statistics, urban safety prevention, and people flow statistics. However, obtaining accurate positions and improving the performance of crowd counting in dense scenes still face challenges, and it is worthwhile devoting much effort to this. In this paper, a new framework is proposed to resolve the problem. The proposed framework includes two parts. The first part is a fully convolutional neural network (CNN) consisting of backend and upsampling. In the first part, backend uses the residual network (ResNet) to encode the features of the input picture, and upsampling uses the deconvolution layer to decode the feature information. The first part processes the input image, and the processed image is input to the second part. The second part is a peak confidence map (PCM), which is proposed based on an improvement over the density map (DM). Compared with DM, PCM can not only solve the problem of crowd counting but also accurately predict the location of the person. The experimental results on several datasets (Beijing-BRT, Mall, Shanghai Tech, and UCF_CC_50 datasets) show that the proposed framework can achieve higher crowd counting performance in dense scenarios and can accurately predict the location of crowds.
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Lalit, Ruchika, and Ravindra Kumar Purwar. "Crowd Abnormality Detection Using Optical Flow and GLCM-Based Texture Features." Journal of Information Technology Research 15, no. 1 (January 2022): 1–15. http://dx.doi.org/10.4018/jitr.2022010110.
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Detection of abnormal crowd behavior is one of the important tasks in real-time video surveillance systems for public safety in public places such as subway, shopping malls, sport complexes and various other public gatherings. Due to high density crowded scenes, the detection of crowd behavior becomes a tedious task. Hence, crowd behavior analysis becomes a hot topic of research and requires an approach with higher rate of detection. In this work, the focus is on the crowd management and present an end-to-end model for crowd behavior analysis. A feature extraction-based model using contrast, entropy, homogeneity, and uniformity features to determine the threshold on normal and abnormal activity has been proposed in this paper. The crowd behavior analysis is measured in terms of receiver operating characteristic curve (ROC) & area under curve (AUC) for UMN dataset for the proposed model and compared with other crowd analysis methods in literature to prove its worthiness. YouTube video sequences also used for anomaly detection.
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Ebrahimpour, Wan, Cervantes, Luo, and Ullah. "Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis." ISPRS International Journal of Geo-Information 8, no. 10 (October 7, 2019): 440. http://dx.doi.org/10.3390/ijgi8100440.
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Extracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis approaches based on different data sources. This survey provides the fundamentals of crowd analysis and considers three main approaches: crowd video analysis, crowd spatio-temporal analysis, and crowd social media analysis. The key research contributions in each approach are presented, and the most significant techniques and algorithms used to improve the precision of results that could be integrated into solutions to enhance the quality of services in a smart city are analyzed.
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Hu, Yue, Zhixiang Fang, Xinyan Zou, Haoyu Zhong, and Lubin Wang. "Two-Stage Tour Route Recommendation Approach by Integrating Crowd Dynamics Derived from Mobile Tracking Data." Applied Sciences 13, no. 1 (January 1, 2023): 596. http://dx.doi.org/10.3390/app13010596.
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Tourism activities essentially represent the interaction between crowds and attractions. Thus, crowd dynamics are critical to the quality of the tourism experience in personalized tour recommendations. In order to generate dynamic, personalized tour routes, this paper develops a tourist trip design problem with crowd dynamics (TTDP-CD), which is quantified with the crowd dynamics indicators derived from mobile tracking data in terms of crowd flow, crowd interaction, and crowd structure. TTDP-CD attempts to minimize the perceived crowding and maximize the assessed value of destinations while minimizing the total distance and proposes a two-stage route strategy of “global optimization first, local update later” to deal with the sudden increase in crowding in realistic scenarios. An evolutionary algorithm is extended with container-index coding, mixed mutation operators, and a global archive to create a personalized day tour route at the urban scale. To corroborate the performance of this approach, a case study was carried out in Dalian, China. The results demonstrate that the suggested method outperforms previous approaches, such as NSGA-II, MOPSO, MOACO, and WSM, in terms of performance and solution quality and decreases real-time crowding by an average of 7%.
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Bodgi, Johanna, Silvano Erlicher, and Pierre Argoul. "Lateral Vibration of Footbridges under Crowd-Loading: Continuous Crowd Modeling Approach." Key Engineering Materials 347 (September 2007): 685–90. http://dx.doi.org/10.4028/www.scientific.net/kem.347.685.
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In this paper, a simple 1D crowd model is proposed, which aim is to properly describe the crowd-flow phenomena occurring when pedestrians walk on a flexible footbridge. The crowd is assumed to behave like a continuous compressible fluid and the pedestrian flow is modeled in a 1-D framework using the (total) mass (of pedestrians) conservation equation. This crowd model is then coupled with a simple model for the dynamical behavior of the footbridge and an optimized modeling of synchronization effects is performed. Numerical simulations are presented to show some preliminary results.
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Mladenow, Andreas, Christine Bauer, and Christine Strauss. "“Crowd logistics”: the contribution of social crowds in logistics activities." International Journal of Web Information Systems 12, no. 3 (August 15, 2016): 379–96. http://dx.doi.org/10.1108/ijwis-04-2016-0020.
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Purpose The paper aims to provide the necessary basis for a novel interdisciplinary research field. Various types and implementations of crowdsourcing have emerged in the market; many of them are related to logistics. While we can identify plenty of crowd logistics applications using information technology capabilities and information sharing in practice, theories behind this phenomenon have received only limited attention. This paper accounts for filling this research gap by analyzing the crowd’s contributions in logistics of goods and information. Design/methodology/approach This paper is part of an ongoing research endeavor in the field of location-based crowdsourcing. It represents conceptual work that builds on a literature review enriched with an in-depth analysis of real-world examples in the field of crowd logistics. Using a scoring method, we provide an example how a company may evaluate the alternatives of crowd logistics. The main approach is an analysis of variants of how the social crowd may be integrated in logistics processes. The work is conceptual in its core. Thereby, we use real-world examples of crowdsourcing applications to underpin the evaluated variants of crowd logistics. Findings The paper presents relevant theoretical background on crowd logistics. The authors differentiate between variants of crowd logistics with their flow of materials, goods and information. Thereby they zoom in the type, significance and process flow of the crowd’s contributions. They discuss potential advantages and challenges of logistics with the performing crowd and deeply discuss opportunities and challenges from a business and from an individual’s perspective. Finally, they highlight a route map for future research directions in this novel interdisciplinary research field. Research limitations/implications As this work is conceptual in its core, generalizations may be drawn only with great care. Still, we are in a position to propose a route map for further research in this area in this paper. Also the integration of an analysis of a scale of real-world applications allows us to highlight our research’s practical relevance and implications. Originality/value The main contribution of this paper is an in-depth analysis and consolidation of innovative crowd logistics applications to provide an overview on recent implementations. The authors propose a categorization scheme and contribute with a route map for further research in the field of crowd logistics.
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Shahhoseini, Zahra, and Majid Sarvi. "Traffic Flow of Merging Pedestrian Crowds: How Architectural Design Affects Collective Movement Efficiency." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 20 (September 18, 2018): 121–32. http://dx.doi.org/10.1177/0361198118796714.
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The need for developing reliable and rigorous models that can replicate and make predictions of pedestrian crowd evacuations has necessitated an understanding of the impact of architecture on individuals’ interactions with their surroundings and the behavioral rules that govern their movements. Due to the challenges of providing such behavioral data from natural evacuations and previous crowd incidents, simulation-based and laboratory-based evacuation experiments have recently been employed as innovative data-provision approaches to study crowd behavior notably under emergency conditions. This study explores pioneer experiments of emergency escape with a view to investigating the relationship between spatial constraints and collective behavior of human crowds. Here, we make use of two types of empirical and analytical data obtained from a large number of well-controlled laboratory and evacuation simulation experiments. This study presents findings corresponding to how and to what extent the presence of conflicting layouts in egress areas, particularly merging corridors, affect the collective motion of pedestrians. The focus of attention will be on measures of performance at macroscopic level derived from both observations. Our results suggested that the movement patterns observed in both types of experiments are sensitive to the angle between the two merging streams and the symmetry/asymmetry of the merging layouts, with symmetric layouts almost invariably outperforming the asymmetric counterparts. Also, within each symmetry/asymmetry structural type, the angle at which the flows combined with each other affected the efficiency of discharge. Our findings provide further evidence as to the significant role of the architectural structure of the movement area in facilitating the traffic flow of heavy crowds of pedestrians.
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Yang, Xi, Suining He, Bing Wang, and Mahan Tabatabaie. "Spatio-Temporal Graph Attention Embedding for Joint Crowd Flow and Transition Predictions." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 4 (December 27, 2021): 1–24. http://dx.doi.org/10.1145/3495003.
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Crowd mobility prediction, in particular, forecasting flows at and transitions across different locations, is essential for crowd analytics and management in spacious environments featured with large gathering. We propose GAEFT, a novel crowd mobility analytics system based on the multi-task graph attention neural network to forecast crowd flows (inflows/outflows) and transitions. Specifically, we leverage the collective and sanitized campus Wi-Fi association data provided by our university information technology service and conduct a relatable case study. Our comprehensive data analysis reveals the important challenges of sparsity and skewness, as well as the complex spatio-temporal variations within the crowd mobility data. Therefore, we design a novel spatio-temporal clustering method to group Wi-Fi access points (APs) with similar transition features, and obtain more regular mobility features for model inputs. We then propose an attention-based graph embedding design to capture the correlations among the crowd flows and transitions, and jointly predict the AP-level flows as well as transitions across buildings and clusters through a multi-task formulation. Extensive experimental studies using more than 28 million association records collected during 2020-2021 academic year validate the excellent accuracy of GAEFT in forecasting dynamic and complex crowd mobility.
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Zhao, Rongyong, Ping Jia, Yan Wang, Cuiling Li, Chuanfeng Han, and Zhishu Zhang. "Dynamic model of macro crowd merging based on abnormal pedestrian posture." MATEC Web of Conferences 355 (2022): 03009. http://dx.doi.org/10.1051/matecconf/202235503009.
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Crowd merging is a complex process, and any sudden external or internal disturbance will destroy the stability of the crowd. The occurrence of abnormal behavior will affect the crowd flow process and inevitably affect the stability of the crowd flow system. The position information of the joint points is obtained through the OpenPose algorithm, and the kinematics characteristics of each node are studied. It is judged whether the number of pedestrians in the crowd and the scale of the building scene are greater than the empirical setting value based on engineering statistical data and expert experience. When the number of pedestrians is more than 2,000 and the total area of the passage is more than 2,000 square meters, the appropriate macro-dynamic model is selected. The Aw-Rascle (AR) fluid dynamics model is selected in this study. The joint point information obtained through the OpenPose is combined with the macroscopic fluid dynamics model to construct a macroscopic crowd flow dynamics model based on the pedestrian's abnormal posture.
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Zhang, Dawei, Haitao Zhu, Shi Qiu, and Boyan Wang. "Characterization of Collision Avoidance in Pedestrian Crowds." Mathematical Problems in Engineering 2019 (March 28, 2019): 1–9. http://dx.doi.org/10.1155/2019/9237674.
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The avoidance behavior of pedestrians was characterized in the present paper by simulating the movement of crowds in both unidirectional and bidirectional pedestrian flow. A phase change of alternative lane formation observed in real bidirectional pedestrian flows has been studied, where pedestrians tended to evade individuals in counterflow and simultaneously keep a certain distance from each other in the uniform pedestrian flow when the counterflow disappeared. What is more, the comparison between the effect of evading and pushing behavior on evacuation has been investigated in the room egress scenario. Additionally, the evading and overtaking behavior of fast pedestrians have also been simulated in heterogeneous crowds. The performance of the proposed model was compared to the experimental data and the results obtained using other evacuation models. Numerical results showed that both the phase change of alternative lane formation in bidirectional pedestrian flow and the effective evading behavior in unidirectional pedestrian flow were conductive to reduce the evacuation time of pedestrian crowds. Even though pushing behavior of fast pedestrians seemed to improve the flow through the wide exit, it might lead to the panic and other negative effect on the crowds, such as crowds trample. The proposed model in this paper could provide a theoretical basis for the pedestrian crowd management during emergency evacuation.
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Lei, Wenjun, Chuanmin Tai, Chuanliang Rong, Xinye Qi, Linhua Zhang, and Junzi Cong. "Environmental parameters and analysis of crowd flow in an academic building of a university." Indoor and Built Environment 30, no. 1 (November 7, 2019): 39–55. http://dx.doi.org/10.1177/1420326x19886921.
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Academic buildings are the main gathering places in universities and colleges. The crowd flow could cause a change in environmental parameters of buildings. In other words, the change in environmental parameters in buildings can be somewhat reflective of the crowd flow. Therefore, field measurements of the crowd flow and environmental parameters in an academic building at a university were conducted in this study. During the periods of 7:30–8:00 and 13:20–13:50, the average speed of the crowd was the highest, which was about 1.2 m/s; the CO2 concentration was low, which was about 750 ppm. In the periods of 9:20–9:50 and 15:10–15:40, the average speed of people walking in the opposite directions was the lowest, which is 0.42 m/s; however, the CO2 concentration could reach 1800 ppm. Test results showed that the variations of CO2 concentrations were inversely related to the average speed of the crowd in the evacuation passage, except for the periods of 7:30–8:00 and 13:20–13:50. Spatial separation and physical separation could be used in the management of personnel flow in the academic building. The results may provide reference for the management of the crowd flow in similar academic buildings of other universities.
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Mayr, Christina Maria, and Gerta Köster. "Guiding crowds when facing limited compliance: Simulating strategies." PLOS ONE 17, no. 11 (November 11, 2022): e0276229. http://dx.doi.org/10.1371/journal.pone.0276229.
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At traffic hubs, it is important to avoid congestion of pedestrian streams to ensure safety and a good level of service. This presents a challenge, since distributing crowds on different routes is much more difficult than opening valves to, for example, regulate fluid flow. Humans may or may not comply with re-directions suggested to them typically with the help of signage, loudspeakers, apps, or by staff. This remains true, even if they perceive and understand the suggestions. Yet, simulation studies so far have neglected the influence of compliance. In view of this, we complement a state-of-the-art model of crowd motion and crowd behavior, so that we can vary the compliance rate. We consider an abstracted scenario that is inspired by a metro station in the city of Munich, where traffic regulators wish to make some passengers abandon the obviously shortest route so that the flow evens out. We investigate the effect of compliance for two very simple guiding strategies. In the first strategy, we alternate routes. In the second strategy, we recommend the path with the lowest crowd density. We observe that, in both cases, it suffices to reroute a small fraction of the crowd to reduce travel times. But we also find that taking densities into account is much more efficient when facing low compliance rates.
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Zhao, Rongyong, Ping Jia, Yan Wang, Cuiling Li, Yunlong Ma, and Zhishu Zhang. "A close-loop verification approach for pedestrian stability based on machine vision." MATEC Web of Conferences 355 (2022): 03037. http://dx.doi.org/10.1051/matecconf/202235503037.
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In public places, it is significant to analyze the stability of the crowd which can support the crowd management and control, and protect the evacuees safely and effectively. The numerical analysis method of system stability based on Lyapunov theory suffers problems that it is difficult to avoid random errors in the initialization of pedestrian density and velocity, as well as cumulative errors due to time increasing, limiting its application. This study adopts a complementary model of theoretical numerical analysis and machine vision with a parallel convolutional neural network (CNN) model. It proposes an approach of stability analysis and closed-loop verification for crowd merging systems. Thereby, this research provides theoretical and methodological support for planning of the functional layout of crowd flow in public crowd-gathering places and the control measures for stable crowd flow.
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Xiang, Jun, and Na Liu. "Crowd Density Estimation Method Using Deep Learning for Passenger Flow Detection System in Exhibition Center." Scientific Programming 2022 (February 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/1990951.
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Aiming at the problems of crowd distribution, scale feature, and crowd feature extraction difficulties in exhibition centers, this paper proposes a crowd density estimation method using deep learning for passenger flow detection systems in exhibition centers. Firstly, based on the pixel difference symbol feature, the difference amplitude feature and gray feature of the central pixel are extracted to form the CLBP feature to obtain more crowd group description information. Secondly, use the LR activation function to add nonlinear factors to the convolution neural network (CNN) and use dense blocks derived from crowd density estimation to train the LR-CNN crowd density estimation model. Finally, experimental results show that the mean absolute error (MAE) and mean square error (MSE) of the proposed method in the UCF_CC_50 dataset are 325.6 and 369.4, respectively. Besides, MAE and MSE in part_A of the Shanghai Tech dataset are 213.5 and 247.1, respectively, and they in part_B are 85.3 and 99.7, respectively. The proposed method effectively improves the accuracy of crowd density estimation in exhibition centers.
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Xie, Kefan, Benbu Liang, Yu Song, and Xueqin Dong. "Analysis of Walking-Edge Effect in Train Station Evacuation Scenarios: A Sustainable Transportation Perspective." Sustainability 11, no. 24 (December 15, 2019): 7188. http://dx.doi.org/10.3390/su11247188.
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Due to the highly developed rail transit over the past decades, the phenomena of complex individual self-organized behaviors and mass crowd dynamics have become a great concern in the train station. In order to understand passengers’ walking-edge effect and analyze the relationship between the layout and sustainable service abilities of the train station, a heuristics-based social force model is proposed to elaborate the crowd dynamics. Several evacuation scenarios are implemented to describe the walking-edge effect in a train station with the evacuation efficiency, pedestrian flow, and crowd density map. The results show that decentralizing crowd flow can significantly increase the evacuation efficiency in different scenarios. When the exits are far away from the central axis of the railway station, the walking-edge effect has little influence on the evacuation efficiency. Obstacles can guide the movement of passengers by channelizing pedestrian flows. In addition, a wider side exit of the funnel-shaped corridors can promote walking-edge effect and decrease the pressure among a congested crowd. Besides providing a modified social force model with considering walking-edge effect, several suggestions are put forward for managers and architects of the train station in designing sustainable layouts.
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Martani, Claudio, Simon Stent, Sinan Acikgoz, Kenichi Soga, Dean Bain, and Ying Jin. "Pedestrian monitoring techniques for crowd-flow prediction." Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction 170, no. 2 (June 2017): 17–27. http://dx.doi.org/10.1680/jsmic.17.00001.
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Liang, Ronghua, Yuge Zhu, and Haixia Wang. "Counting crowd flow based on feature points." Neurocomputing 133 (June 2014): 377–84. http://dx.doi.org/10.1016/j.neucom.2013.12.040.
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Rao, Aravinda S., Jayavardhana Gubbi, Slaven Marusic, and Marimuthu Palaniswami. "Crowd Event Detection on Optical Flow Manifolds." IEEE Transactions on Cybernetics 46, no. 7 (July 2016): 1524–37. http://dx.doi.org/10.1109/tcyb.2015.2451136.
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Haworth, Brandon, Muhammad Usman, Glen Berseth, Mubbasir Kapadia, and Petros Faloutsos. "On density-flow relationships during crowd evacuation." Computer Animation and Virtual Worlds 28, no. 3-4 (May 2017): e1783. http://dx.doi.org/10.1002/cav.1783.
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Yao, Liya, Lishan Sun, Zhiyong Zhang, Shuwei Wang, and Jian Rong. "Research on the Behavior Characteristics of Pedestrian Crowd Weaving Flow in Transport Terminal." Mathematical Problems in Engineering 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/264295.
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Due to the poor transfer organization in urban public transport terminal, pedestrian crowd are often forced to weaving in their transfer flow lines. Frequent weaving behaviors not only decrease passengers’ transfer comfort, but may also trigger serious crowd disaster such as trampling. In order to get accurate understanding of the weaving features of pedestrian crowd and analyze the relevant evolution law, researches have been conducted on the basis of field investigation. First, the typical weaving phenomenon were defined and classified, and a microscopic parameters system of pedestrian crowd weaving flow was constructed. The detection and quantification methods of multiple indicator parameters were also given. Then, correlation between different behavioral parameters was analyzed based on the survey data of weaving pedestrian crowd on the stairs of DongZhiMen (DZM) hub. The basic characteristics and evolution law of the weaving behaviors were then discussed, and conclusions were drawn.
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Song, Beibei, and Rui Sheng. "Crowd Counting and Abnormal Behavior Detection via Multiscale GAN Network Combined with Deep Optical Flow." Mathematical Problems in Engineering 2020 (December 15, 2020): 1–11. http://dx.doi.org/10.1155/2020/6692257.
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Aiming at the problem of low performance of crowd abnormal behavior detection caused by complex backgrounds and occlusions, this paper proposes a single-image crowd counting and abnormal behavior detection via multiscale GAN network. The proposed method firstly designed an embedded GAN module with a multibranch generator and a regional discriminator to initially generate crowd-density maps; and then our proposed multiscale GAN module is added to further strengthen the generalization ability of the model, which can effectively improve the accuracy and robustness of the prediction detection and counting. On the basis of single-image crowd counting, synthetic optical-flow feature descriptor is adopted to obtain the crowd motion trajectory, and the classification of abnormal behavior is finally implemented. The simulation results show that the proposed algorithm can significantly improve the accuracy and robustness of crowd counting and abnormal behavior detection in real complex scenarios compared with the existing mainstream algorithms, which is suitable for engineering applications.
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Zhu, Wenjie, Rongyong Zhao, Hao Zhang, Ping Jia, Yan Wang, Cuiling Li, and Yunlong Ma. "Crowd Stability Analysis Based on Pedestrian Abnormal Postures." Journal of Physics: Conference Series 2224, no. 1 (April 1, 2022): 012062. http://dx.doi.org/10.1088/1742-6596/2224/1/012062.
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Abstract Abnormal behaviors of pedestrians in crowd gathering public places are important factors affecting the stability of crowd flow. Pedestrian abnormal postures are important manifestation of abnormal behaviors, which often leads to local turbulence, disturbance and density-speed fluctuations. It is urgent to discover the disturbance mechanism of abnormal pedestrian posture on the stability of crowd flow. This study intends to establish machine vision, kinematics, dynamic models and crowd confluence dynamic models for typical abnormal pedestrian postures in public places. We mainly use computer vision related technology based to recognize abnormal postures of pedestrians in videos, constructs a network matrix of key posture nodes, and studys the kinematics characteristics of abnormal posture nodes. Considering the number of pedestrians and the characteristics of the architectural scenes, we design a workflow to select the appropriate macro or micro dynamic model to build the crowd flow model. To validate the propuesd model, case in Shanghai Hongqiao railway station is studied.
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Saif, A. F. M. Saifuddin, and Zainal Rasyid Mahayuddin. "Crowd Density Estimation from Autonomous Drones Using Deep Learning: Challenges and Applications." Journal of Engineering and Science Research 5, no. 6 (December 20, 2021): 1–6. http://dx.doi.org/10.26666/rmp.jesr.2021.6.1.
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Crowd flow estimation from Drones or normally referred as Unmanned Aerial Vehicle (UAV ) for crowd management and monitoring is an essential research problem for adaptive monitoring and controlling dynamic crowd gatherings. Various challenges exist in this context, i.e. variation in density, scale, brightness, height from UAV platform, occlusion and inefficient pose estimation. Currently, gathering of crowd is mostly monitored by Close Circuit Television (CCTV) cameras where various problems exist, i.e. coverage in little area and constant involvement of human to monitor crowd which encourage researchers to move towards deep learning and computer vision techniques to minimize the need of human operator and thus develop intelligent crowd counting techniques. Deep learning frameworks are promising for intelligent crowd analysis from frames of video despite the fact of various challenges for detecting humans from unstable UAV camera platforms. This research presents rigorous investigation and analysis in existing methods with their applications for crowd flow estimation from UAV. Besides, comprehensive performance evaluation for existing methods using recent deep learning frameworks is illustrated for crowd counting purposes. In addition, strong foundation for future direction is given by elaborating observations on existing research frameworks.
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Sharif, Md Haidar. "An Eigenvalue Approach to Detect Flows and Events in Crowd Videos." Journal of Circuits, Systems and Computers 26, no. 07 (March 17, 2017): 1750110. http://dx.doi.org/10.1142/s0218126617501109.
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Analysis of flows in crowd videos is a remarkable topic with practical implementations in many different areas. In this paper, we present a wide overview of this topic along with our own approach to this problem. Our approach treats the difficulty of crowd flow analysis by distinguishing single versus multiple flows in a scene. Spatiotemporal features of two consecutive frames are extracted by optical flows to create a three-dimensional tensor, which retains appearance and velocity information. Tensor’s upper left minor matrix captures intensity structure. A normalized continuous rank-increase measure for each frame is calculated by a generalized interlacing property of the eigenvalues of these matrices. In essence, measure values put through the knowledge of existing flows. Yet they do not go into effect desirably due to optical flow estimation error and some other factors. A proper set of the degree of polynomial fitting functions decodes their existence. But how can we estimate that set? Its detailed study is performed. Zero flow, single flow, multiple flows, and interesting events are detected as frame basis using thresholds on the polynomial fitting measure values. Plausible mean outputs of recall rate (88.9%), precision rate (86.7%), area under the receiver operating characteristic curve (98.9%), and accuracy (92.9%) reported from conducted experiments on PETS2009 and UMN benchmark datasets make clear and visible that our method gains high-quality results to detect flows and events in crowd videos in terms of both robustness and potency.
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Xiong, Liyan, Lei Zhang, Xiaohui Huang, Xiaofei Yang, Weichun Huang, Hui Zeng, and Hong Tang. "DCAST: A Spatiotemporal Model with DenseNet and GRU Based on Attention Mechanism." Mathematical Problems in Engineering 2021 (February 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/8867776.
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The accurate prediction of crowd flow in urban areas is becoming more and more important in many fields such as traffic management and public safety. However, the complex spatiotemporal relationship of the traffic data and the influence of events, weather, and other factors makes it very difficult to accurately predict the crowd flow. In this study, we propose a spatiotemporal prediction model that is based on densely connected convolutional networks and gated recurrent units (GRU) with the attention mechanism to predict the inflow and outflow of the crowds in regions within a specific area. The DCAST model divides the time axis into three parts: short-term dependence, period rule, and long-term dependence. For each part, we employ densely connected convolutional networks to extract spatial characteristics. Attention-based GRU module is used to capture the temporal features. And then, the outputs of the three parts are fused by weighting elementwise addition. At last, we combine the results of the fusion and external factors to predict the crowd flow in each region. The root mean square errors of the DCAST model in two real datasets of taxis in Beijing (TaxiBJ) and bikes in New York (BikeNYC) are 15.70 and 5.53, respectively. The experimental results show that the results are more accurate and reliable than that of the baseline model.
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Shao, Yanhua, Wenfeng Li, Hongyu Chu, Zhiyuan Chang, Xiaoqiang Zhang, and Huayi Zhan. "A Multitask Cascading CNN with MultiScale Infrared Optical Flow Feature Fusion-Based Abnormal Crowd Behavior Monitoring UAV." Sensors 20, no. 19 (September 28, 2020): 5550. http://dx.doi.org/10.3390/s20195550.
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Visual-based object detection and understanding is an important problem in computer vision and signal processing. Due to their advantages of high mobility and easy deployment, unmanned aerial vehicles (UAV) have become a flexible monitoring platform in recent years. However, visible-light-based methods are often greatly influenced by the environment. As a result, a single type of feature derived from aerial monitoring videos is often insufficient to characterize variations among different abnormal crowd behaviors. To address this, we propose combining two types of features to better represent behavior, namely, multitask cascading CNN (MC-CNN) and multiscale infrared optical flow (MIR-OF), capturing both crowd density and average speed and the appearances of the crowd behaviors, respectively. First, an infrared (IR) camera and Nvidia Jetson TX1 were chosen as an infrared vision system. Since there are no published infrared-based aerial abnormal-behavior datasets, we provide a new infrared aerial dataset named the IR-flying dataset, which includes sample pictures and videos in different scenes of public areas. Second, MC-CNN was used to estimate the crowd density. Third, MIR-OF was designed to characterize the average speed of crowd. Finally, considering two typical abnormal crowd behaviors of crowd aggregating and crowd escaping, the experimental results show that the monitoring UAV system can detect abnormal crowd behaviors in public areas effectively.
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Liu, Linfan, Huajun Zhang, Jupeng Xie, and Qin Zhao. "Dynamic Evacuation Planning on Cruise Ships Based on an Improved Ant Colony System (IACS)." Journal of Marine Science and Engineering 9, no. 2 (February 19, 2021): 220. http://dx.doi.org/10.3390/jmse9020220.
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The emergency evacuation route planning of cruise ships directly affects the safety of all crew members and passengers during emergencies. Research on the planning of emergency evacuation routes for cruise ships is a frontier subject of maritime safety. This study proposes an improved ant colony system (IACS) to solve the evacuation route planning of crowds on cruise ships. The IACS, which is different from common single-path ant colony system (ACS) evacuation algorithms, is used to solve the multipath planning problem of crowd evacuation from cruise ships by considering crowd density and speed in the model. An increasing flow method is introduced into the IACS to improve the efficiency of the proposed algorithm. Numerical experiments show that this method meets the requirements of evacuation analysis guidelines for new and existing passenger ships (MSC.1/Circ.1533)and can effectively and efficiently plan the emergency evacuation path for cruise ship crowd.
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Chakole, Pallavi D., Vishal R. Satpute, and Naveen Cheggoju. "Crowd behavior anomaly detection using correlation of optical flow magnitude." Journal of Physics: Conference Series 2273, no. 1 (May 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2273/1/012023.
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Abstract Nowadays, crowd monitoring is a contentious issue. Because of the increasing population and diversity of human activities, crowd scenarios in the real world are becoming more common, demanding the need for an automotive anomaly detection system. Crowd behavior is influenced by the thoughts and attitudes of others around them. An unexpected event can turn a peaceful crowd into a riot. A mechanism based on optical flow must be implemented to compensate for all of these factors. The amount of motion present in two successive frames is estimated using optical flow. It includes information on velocity in the x & y plane, along with magnitude and line of action. By means of “anomalous event” in this paper is quick and sudden dispersal of the crowd. For detecting an event the magnitude of two successive frames should be taken into account followed by estimating a correlation. We expect a high correlation, slight motion, and low rate of change in velocities at non-anomalous events, but as soon as an anomalous event occurs, the correlation begins to decrease with a significant change in velocity and large motion vectors. The methodology was tested on a dataset from the University of Minnesota that included 11 movies from three different circumstances. Almost all anomalous occurrences in videos were successfully detected using this method.
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Chakole, Pallavi D., Vishal R. Satpute, and Naveen Cheggoju. "Crowd behavior anomaly detection using correlation of optical flow magnitude." Journal of Physics: Conference Series 2273, no. 1 (May 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2273/1/012023.
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Abstract Nowadays, crowd monitoring is a contentious issue. Because of the increasing population and diversity of human activities, crowd scenarios in the real world are becoming more common, demanding the need for an automotive anomaly detection system. Crowd behavior is influenced by the thoughts and attitudes of others around them. An unexpected event can turn a peaceful crowd into a riot. A mechanism based on optical flow must be implemented to compensate for all of these factors. The amount of motion present in two successive frames is estimated using optical flow. It includes information on velocity in the x & y plane, along with magnitude and line of action. By means of “anomalous event” in this paper is quick and sudden dispersal of the crowd. For detecting an event the magnitude of two successive frames should be taken into account followed by estimating a correlation. We expect a high correlation, slight motion, and low rate of change in velocities at non-anomalous events, but as soon as an anomalous event occurs, the correlation begins to decrease with a significant change in velocity and large motion vectors. The methodology was tested on a dataset from the University of Minnesota that included 11 movies from three different circumstances. Almost all anomalous occurrences in videos were successfully detected using this method.
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Wei, Juan, Hong Zhang, Zhenya Wu, Junlin He, and Yangyong Guo. "A novel crowd flow model based on linear fractional stable motion." International Journal of Modern Physics B 30, no. 09 (April 10, 2016): 1650049. http://dx.doi.org/10.1142/s0217979216500491.
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For the evacuation dynamics in indoor space, a novel crowd flow model is put forward based on Linear Fractional Stable Motion. Based on position attraction and queuing time, the calculation formula of movement probability is defined and the queuing time is depicted according to linear fractal stable movement. At last, an experiment and simulation platform can be used for performance analysis, studying deeply the relation among system evacuation time, crowd density and exit flow rate. It is concluded that the evacuation time and the exit flow rate have positive correlations with the crowd density, and when the exit width reaches to the threshold value, it will not effectively decrease the evacuation time by further increasing the exit width.
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Guo, Hui, Ying-Hua Song, Wei Lv, Xin-Yao Guo, and Zhi-Wu Lei. "Study on Critical Density of Percolation in Crowds in Public Areas." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 13 (April 30, 2020): 2059044. http://dx.doi.org/10.1142/s0218001420590442.
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The percolation model is an effective tool to solve the problem of fluid flow in the pores; the situation of outsiders crossing the crowd is similar. This paper verifies the obvious percolation phenomenon in the randomly distributed crowd by [Formula: see text] simulation and reveals several characteristics and laws of the crowd percolation phenomena. Studies have shown that sites with different spatial dimensions have different densities of crowd percolation: when the actual density of the crowd is greater than the critical density of the crowd percolation, the outsider is difficult to pass through the crowd; otherwise, the outsider can pass through the crowd easily. Therefore, the critical density of crowd percolation can be one of the indicators of crowd management in public areas, which will provide important guidance for the design of public areas, site selection of public activities and crowd management.
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Khademipour, Gholamreza, Nouzar Nakhaee, Seyed Mohammad Saberi Anari, Maryam Sadeghi, Hojjat Ebrahimnejad, and Hojjat Sheikhbardsiri. "Crowd Simulations and Determining the Critical Density Point of Emergency Situations." Disaster Medicine and Public Health Preparedness 11, no. 6 (May 30, 2017): 674–80. http://dx.doi.org/10.1017/dmp.2017.7.
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AbstractObjectiveIn modern societies, crowds and mass gatherings are recurrent. A combination of inadequate facilities and inefficient population management can lead to injury and death. Simulating people’s behavior in crowds and mass gatherings can assist in the planning and management of gatherings, especially in emergency situations.MethodsWe aimed to determine the crowd pattern and the critical density point in the grand bazaar of Kerman in Iran. We collected data by use of a census method with a questionnaire. To determine the critical density point, height and weight data were placed in the equation$\,s\,{\equals}\,\sqrt {{{L{\vskip -1.5pt \,\,\asterisk\,\,}M} \over {3600}}} $and the outer body surface of all the individuals in the bazaar was calculated. The crowd was simulated by use of flow-based modeling. Flow rate was determined by using the equation (flow rate=density * speed). By use of SketchUp Pro software (version 8; Trimble, Sunnyvale, CA), the movement of each person and the general flow rate were simulated in the three-dimensional environment of Kerman bazaar.ResultsOur findings showed that the population critical density point in Kerman bazaar would be 6112 people. In an accident, the critical density point in Kerman bazaar would be created in about 1 minute 10 seconds after the event.ConclusionIt seems necessary to identify and provide solutions for reducing the risk of disasters caused by overcrowding in Kerman bazaar. It is suggested that researchers conduct studies to design safe and secure emergency evacuation of Kerman bazaar as well as proper planning for better and faster access of aid squads to this location. (Disaster Med Public Health Preparedness. 2017;11:674–680)
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Liu, Yuanyuan, and Toshiyuki Kaneda. "Using agent-based simulation for public space design based on the Shanghai Bund waterfront crowd disaster." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 34, no. 2 (January 29, 2020): 176–90. http://dx.doi.org/10.1017/s0890060420000049.
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AbstractWith growing city density and mass gatherings held all over the world in urban spaces, crowd disasters have been happening each year. In considering the avoidance of crowd disasters and the reduction of fatalities, it is important to analyze the efficient spatial layout of the public space in situations of high crowd density. Compared with traditional empirical design methods, computational approaches have better abilities for quantitative analysis and are gradually being adopted in the planning and management of the urban public space. In this paper, we investigated the official documents, publicly available videos, and materials of the Shanghai waterfront crowd disaster which happened on December 31, 2014. Based on the investigation, a detailed site survey was conducted and pedestrian flow data were acquired. To test the influence of different spatial layouts, an agent-based simulator is built, following the ASPFver4.0 (Agent Simulator of Pedestrian Flow) pedestrian walking rules. With the surveyed pedestrian flow data, the original spatial layout of the Shanghai Bund waterfront together with five other comparison scenarios are tested, including both space design and crowd management improvements. In the simulation results, the efficiencies of different space design and crowd management solutions are compared. The results show that even simple crowd control measures such as capacity reserve and more proper route planning will allow for a positive improvement in crowd safety. The results also compare the efficiency of different spatial operations and give general suggestions to the problems urban public space designers should consider in high-density environments.
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Liu, Ying, Cheng Sun, and Yiming Bie. "Modeling Unidirectional Pedestrian Movement: An Investigation of Diffusion Behavior in the Built Environment." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/308261.
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Unidirectional pedestrian movement is a special phenomenon in the evacuation process of large public buildings and urban environments at pedestrian scale. Several macroscopic models for collective behaviors have been built to predict pedestrian flow. However, current models do not explain the diffusion behavior in pedestrian crowd movement, which can be important in representing spatial-temporal crowd density differentiation in the movement process. This study builds a macroscopic model for describing crowd diffusion behavior and evaluating unidirectional pedestrian flow. The proposed model employs discretization of time and walking speed in geometric distribution to calculate downstream pedestrian crowd flow and analyze movement process based on upstream number of pedestrians and average walking speed. The simulated results are calibrated with video observation data in a baseball stadium to verify the model precision. Statistical results have verified that the proposed pedestrian diffusion model could accurately describe pedestrian macromovement behavior within the margin of error.
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Zhao, Rongyong, Ping Jia, Yan Wang, Cuiling Li, Yunlong Ma, and Zhishu Zhang. "Acceleration-critical density time-delay model for crowd stability analysis based on Lyapunov theory." MATEC Web of Conferences 355 (2022): 03019. http://dx.doi.org/10.1051/matecconf/202235503019.
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Crowd stability analysis is one of research hotspots to alleviate the severe situation of stampede accidents worldwide. Different from the conventional analysis models for crowd stability based on pedestrian density, this study analyses the characteristics of external disturbances and internal obstacle disturbance based on Lyapunov's theory. The critical range of crowd acceleration in crowd evacuation is obtained, a crowd merging acceleration-critical density time delay model is established, and a stability criterion of acceleration vector based on Lyapunov is obtained based on Lyapunov stability analysis. This provides new information for ensuring the stability of crowd movement in public places, assessing the stability of the crowd in the area, and taking reasonable protection and guidance measures prior to instability of a crowd flow.