Crowd Mobility Research Articles

Application of the multi-grid modelling method to pedestrian social group dynamics through a bottleneck

There is a high proportion of social groups in crowds. The influence of social groups on crowd mobility is particularly evident in emergency situations and in confined movement environments. However, studies on the characteristics of social group movement through a bottleneck with different widths are limited. Therefore, the computation and simulation are performed in a corridor with a bottleneck involving individuals and three types of groups (namely two-person, three-person and four-person groups) using a modified multi-grid model. The original multi-grid model is modified by introducing interactions between pedestrians, avoidance behavior and group behavior, and validated by data from a controlled experiment. It is found that the group configuration can be adjusted to the changing scenarios during the passing movement. The movement efficiency and level of walking comfort for pedestrians are significantly improved in wider bottlenecks. Passing efficiency is evidently increased and congestion is reduced, when social groups occupy a large proportion of the crowd in a narrow bottleneck. These numerical findings may provide new insights for analyzing nonlinear pedestrian social group dynamics.

Impact of population size on epidemic spreading in a bipartite metapopulation network with recurrent mobility

The mobility pattern of a population plays a key role in the spread of epidemics. Despite extensive work on epidemic spreading, little attention has been paid to the impact of subpopulation size. This paper investigates the spread of epidemics on a bipartite metapopulation network considering recurrent mobility patterns and different sizes of subpopulations. With the Markovian process approach, the epidemic threshold can be predicted as a function of subpopulation size and epidemic parameters. Simulation and theoretical results indicate that there exists a critical mobility intensity below which the epidemic will be eliminated, while limiting the size of the subpopulation can suppress the epidemic. Additionally, the epidemic threshold will approach zero when the size of the public area is large. The results can help the prevention of epidemic spreading under recurrent crowd mobility.

Efficient Exclusion Strategy of Shadowed RIS in Dynamic Indoor Programmable Wireless Environments

Recent efforts have promoted programmable wireless environments (PWEs) to enhance the reception quality in high-frequency bands via reconfigurable intelligent surfaces (RISs). However, relevant research efforts are limited to setups with stationary users. This paper shows that crowd mobility in indoor PWEs induces spatio-temporal shadows on the surfaces, resulting in spatio-temporal sparsity in channel gains due to signal blockages. This overlooked aspect impacts the operation strategy of PWEs as the shadowed RIS tiles would contribute to the overheads while offering almost no improvement to the reception quality. Hence, this paper proposes an optimal strategy that excludes the shadowed tiles, which maximizes the utilization efficiency of RISs while minimizing the overheads. Since signal blockage is tied with the details of user mobility, a general model does not exist to identify such shadowed tiles for exclusion. Hence, we follow a data-driven approach that capitalizes on a realistic indoor mobility model and ray-tracing to generate the channel data. However, conventional ray-tracing presents high complexity that hinders data generation. So, we propose an approach to identify the shadow regions with a nine-order of magnitude reduction in complexity to efficiently generate the channel data. Furthermore, we present two exclusion strategies that offer guaranteed and best-effort quality-of-service support, and each can identify the tiles to be excluded via a search method with a complexity of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(N)</i> for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> tiles. The results indicate that the proposed strategies reduce the overheads by 45 - 50% while maintaining optimal service quality in various environments, operation frequencies, and user and access point density.

STICAP : Spatio-temporal Interactive Attention for Citywide Crowd Activity Prediction

Accurate citywide crowd activity prediction (CAP) can enable proactive crowd mobility management and timely responses to urban events, which has become increasingly important for a myriad of smart city planning and management purposes. However, complex correlations across the crowd activities, spatial and temporal urban environment features and their interactive dependencies, and relevant external factors (e.g., weather conditions) make it highly challenging to predict crowd activities accurately in terms of different venue categories (for instance, venues related to dining, services, and residence) and varying degrees (e.g., daytime and nighttime). To address the above concerns, we propose STICAP , a citywide spatio-temporal interactive crowd activity prediction approach. In particular, STICAP takes in the location-based social network check-in data (e.g., from Foursquare/Gowalla) as the model inputs and forecasts the crowd activity within each time step for each venue category. Furthermore, we have integrated multiple levels of temporal discretization to interactively capture the relations with historical data. Then, three parallel Residual Spatial Attention Networks ( RSAN ) in the Spatial Attention Component exploit the hourly, daily, and weekly spatial features of crowd activities, which are further fused and processed by the Temporal Attention Component for interactive CAP . Along with other external factors such as weather conditions and holidays, STICAP adaptively and accurately forecasts the final crowd activities per venue category, enabling potential activity recommendation and other smart city applications. Extensive experimental studies based on three different real-world crowd activity datasets have demonstrated that our proposed STICAP outperforms the baseline and state-of-the-art algorithms in CAP accuracy, with an average error reduction of 35.02%.

MG-ASTN: Multigraph Framework With Attentive Spatial–Temporal Networks for Crowd Mobility Prediction

Predicting urban crowd patterns/flows is a challenging task due to complex spatial-temporal (ST) dependencies. In this paper, we aim to examine and report the capability and effectiveness of the current most widely used Graph Convolutional Networks (GCNs) on mobile data analysis in ST networks for crowd flow forecasting. Specifically, we propose a novel dual-stream framework leveraging Multi-Graph with Attentive Spatial-Temporal Networks (MG-ASTN) to simultaneously predict crowd in-out flow and OD flow based on the trajectory data collected by on-board devices (e.g., GPS). MG-ASTN utilizes Multi-Graph Convolutional Networks encoding non-Euclidean correlations to explore pair-wise relationships among regions. In addition, we further apply a cross-channel attention mechanism with 3D Temporal Convolutional Network to address the heterogeneity of ST features and capture more meaningful data representations for multi-task learning. In the evaluation, we conduct experiments based on 2 real-world datasets and verify most well-known state-of-the-art methods for crowd flow prediction. The results demonstrate that MG-ASTN could outperform other solutions -in-out flow prediction with lowest RMSE and MAE, and OD flow prediction beyond others in most cases, thus it has great potential in modeling the complex correlations among regions in ST networks and enabling accurate prediction in urban computing.

Disease Simulation in Airport Scenario Based on Individual Mobility Model

As the rapid-spreading disease COVID-19 occupies the world, most governments adopt strict control policies to alleviate the impact of the virus. These policies successfully reduced the prevalence and delayed the epidemic peak, while they are also associated with high economic and social costs. To bridge the microscopic epidemic transmission patterns and control policies, simulation systems play an important role. In this work, we propose an agent-based disease simulator for indoor public spaces, which contribute to most of the transmission in cities. As an example, we study Guangzhou Baiyun International Airport, which is one of the most bustling aviation hubs in China. Specifically, we design a high-efficiency mobility generation module to reconstruct the individual trajectories considering both lingering behavior and crowd mobility, which greatly enhances the credibility of the simulated mobility and ensures real-time performance. Based on the individual trajectories, we propose a multi-path disease transmission module optimized for indoor public spaces, which includes three main transmission paths as close contact transmission, aerosol transmission, and object surface transmission. We design a novel convolution-based algorithm to mimic the diffusion process, which can leverage the high concurrent capability of the graphics processing unit to accelerate the simulation process. Leveraging our simulation paradigm, the effectiveness of common policy interventions can be quantitatively evaluated. For mobility interventions, we find that lingering control is the most effective mobility intervention with 32.35% fewer infections, while increasing social distance and increasing walking speed have a similar effect with 15.15% and 18.02% fewer infections. It demonstrates the importance of introducing crowd mobility into disease transmission simulation. For transmission processes, we find the aerosol transmission involves in 99.99% of transmission, which highlights the importance of ventilation in indoor public spaces. Our simulation also demonstrates that without strict entrance detection to identify the input infections, only performing frequent disinfection cannot achieve desirable epidemic outcomes. Based on our simulation paradigm, we can shed light on better policy designs that achieve a good balance between disease spreading control and social costs.

On-demand route allocation: A smart transportation scheme

For effective crowd mobility, smart cities must have smart transportation. Smart cities need a transportation system that is intelligent, simple, and effective. Here is a creative idea for modernizing the current public transit system. Hardware modules based on the Internet of Things (IoT) that are required for implementation are discussed in this work. Traffic circumstances and extra elements have been added to Dijkstra’s shortest path method. The proposed algorithm has been tested in a number of circumstances, and the outcomes have been displayed accordingly. At a cheaper cost, the suggested solution can upgrade the current infrastructure. With various request counts, the algorithm performs well.

On-Demand Route Allocation: A Smart Transportation Scheme

For effective crowd mobility, smart cities must have smart transportation. Smart cities need a transportation system that is intelligent, simple, and effective. Here is a creative idea for modernizing the current public transit system. Hardware modules based on the Internet of Things (IoT) that are required for implementation are discussed in this work. Traffic circumstances and extra elements have been added to Dijkstra’s shortest path method. The proposed algorithm has been tested in a number of circumstances, and the outcomes have been displayed accordingly. At a cheaper cost, the suggested solution can upgrade the current infrastructure. With various request counts, the algorithm performs well.

Fusion of CCTV Video and Spatial Information for Automated Crowd Congestion Monitoring in Public Urban Spaces

Crowd congestion is one of the main causes of modern public safety issues such as stampedes. Conventional crowd congestion monitoring using closed-circuit television (CCTV) video surveillance relies on manual observation, which is tedious and often error-prone in public urban spaces where crowds are dense, and occlusions are prominent. With the aim of managing crowded spaces safely, this study proposes a framework that combines spatial and temporal information to automatically map the trajectories of individual occupants, as well as to assist in real-time congestion monitoring and prediction. Through exploiting both features from CCTV footage and spatial information of the public space, the framework fuses raw CCTV video and floor plan information to create visual aids for crowd monitoring, as well as a sequence of crowd mobility graphs (CMGraphs) to store spatiotemporal features. This framework uses deep learning-based computer vision models, geometric transformations, and Kalman filter-based tracking algorithms to automate the retrieval of crowd congestion data, specifically the spatiotemporal distribution of individuals and the overall crowd flow. The resulting collective crowd movement data is then stored in the CMGraphs, which are designed to facilitate congestion forecasting at key exit/entry regions. We demonstrate our framework on two video data, one public from a train station dataset and the other recorded at a stadium following a crowded football game. Using both qualitative and quantitative insights from the experiments, we demonstrate that the suggested framework can be useful to help assist urban planners and infrastructure operators with the management of congestion hazards.

Context-aware and ethics-first crowd mobility portraits over massive smart card data

Context-aware and ethics-first crowd mobility portraits over massive smart card data

Spatio-Temporal Graph Attention Embedding for Joint Crowd Flow and Transition Predictions

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.

Artificial Intelligence for Smart Resource Management in Multi-User Mobile Heterogeneous RF-Light Networks

Recent trends in 5G and beyond wireless networks have encouraged the migration from the already congested radio frequency (RF) spectrum to higher frequency bands. In this context, the ubiquitous presence of lighting systems supports wide scale deployment of wireless communication links via light. However, the susceptibility of light to user mobility hinders its wide adoption. Hence, the coexistence of RF and light-based wireless communications has the potential to offer seamless heterogeneous network (HetNet) coverage through intelligent vertical handover policies in the presence of users' mobility. In this article, we first present new insights on the implementation of realistic indoor mobile optical channels and the impact of crowd mobility on relevant channel statistics. Then, an artificial intelligence (AI)-based framework for efficient resource management in mobile multi-user RF-light HetNets is proposed using a deep learning-empowered optical link predictor and a multiagent reinforcement learning-based link assignment strategy. The proposed AI-based framework helps to lay down the foundations of smart resource management in mobile multi-user HetNets.

Statistical analysis of the community lockdown for COVID-19 pandemic

As the global pandemic of the COVID-19 continues, the statistical modeling and analysis of the spreading process of COVID-19 have attracted widespread attention. Various propagation simulation models have been proposed to predict the spread of the epidemic and the effectiveness of related control measures. These models play an indispensable role in understanding the complex dynamic situation of the epidemic. Most existing work studies the spread of epidemic at two levels including population and agent. However, there is no comprehensive statistical analysis of community lockdown measures and corresponding control effects. This paper performs a statistical analysis of the effectiveness of community lockdown based on the Agent-Level Pandemic Simulation (ALPS) model. We propose a statistical model to analyze multiple variables affecting the COVID-19 pandemic, which include the timings of implementing and lifting lockdown, the crowd mobility, and other factors. Specifically, a motion model followed by ALPS and related basic assumptions is discussed first. Then the model has been evaluated using the real data of COVID-19. The simulation study and comparison with real data have validated the effectiveness of our model.

Community Perceptions, Attitudes and Behavior on The 3M Program to Avoid The Spread of Covid-19

The Covid-19 pandemic with its high existence and significant spread requires efforts to break the chain of distribution, including with 3M (washing hands, wearing masks and maintaining a distance). This study aims to examine the perceptions, attitudes and behavior of the community towards the implementation of the 3M program in avoiding the spread of covid-19 in Sutawinangun Village, Kedawung District, Cirebon Regency. Qualitative research was chosen as a method to explore and understand meanings ascribed to social or human problems, focusing on individual meanings, and translating the complexity of a problem. Descriptive data was developed from the results of obtaining a number of written and oral information from observations. After conducting the research, it can be concluded that after a member of the community was exposed and declared the red zone of Covid-19, then there was a plan to relocate Covid sufferers in Sutawinangun, just to grow awareness of implementing 3M. Community solidarity is an important factor as social capital to fight Covid-19. The community focuses on implementing 3M, a clean and healthy lifestyle and reducing crowd mobility in the community to avoid the virus.

Service Migration Scheduling with Bandwidth Limitation against Crowd Mobility in Edge Computing Environments

Service Migration Scheduling with Bandwidth Limitation against Crowd Mobility in Edge Computing Environments

Rethinking Communication and Crowdsourced Technology: Mediating Role of Mobile-Learning Tie to Broadband

The proliferation of online crowdsourcing information via mobile technology intervention achieved progressive learning in recent times. The study seeks the mobility of crowds using internet-contents as crowdsourcing knowledge phenomenon in community-learning task actualization. Bandura’s Social Learning Theory (SLT) and TPB induced and investigated 361 respondents among international students using IBM Amos v. 25 for the analysis. Results found exogenous variables were positively significant, whiles broadband moderation on mobile learning behavior run-up. Mobile learning mediation magnifies the behavior actualization effectiveness. Significantly, crowdsource at the individual level colored internet-content via mobile learning technology collaborated communication problem-solving tasks. Mobility of learning makes a mountain of molehills in knowledge sourcing, communication community-centered performance.

On a method for location and mobility analytics using location-based services: a case study of retail store recommendation

PurposeLocation-based services (LBS) have become an effective commercial marketing tool. However, regarding retail store location selection, it is challenging to collect analytical data. In this study, location-based social network data are employed to develop a retail store recommendation method by analyzing the relationship between user footprint and point-of-interest (POI). According to the correlation analysis of the target area and the extraction of crowd mobility patterns, the features of retail store recommendation are constructed.Design/methodology/approachThe industrial density, area category, clustering and area saturation calculations between POIs are designed. Methods such as Kernel Density Estimation and K-means are used to calculate the influence of the area relevance on the retail store selection.FindingsThe coffee retail industry is used as an example to analyze the retail location recommendation method and assess the accuracy of the method.Research limitations/implicationsThis study is mainly limited by the size and density of the datasets. Owing to the limitations imposed by the location-based privacy policy, it is challenging to perform experimental verification using the latest data.Originality/valueAn industrial relevance questionnaire is designed, and the responses are arranged using a simple checklist to conveniently establish a method for filtering the industrial nature of the adjacent areas. The New York and Tokyo datasets from Foursquare and the Tainan city dataset from Facebook are employed for feature extraction and validation. A higher evaluation score is obtained compared with relevant studies with regard to the normalized discounted cumulative gain index.

Structure and dynamics of hydrodynamically interacting finite-size Brownian particles in a spherical cavity: Spheres and cylinders.

The structure and dynamics of confined suspensions of particles of arbitrary shape are of interest in multiple disciplines from biology to engineering. Theoretical studies are often limited by the complexity of long-range particle-particle and particle-wall forces, including many-body fluctuating hydrodynamic interactions. Here, we report a computational study on the diffusion of spherical and cylindrical particles confined in a spherical cavity. We rely on an immersed-boundary general geometry Ewald-like method to capture lubrication and long-range hydrodynamics and include appropriate non-slip conditions at the confining walls. A Chebyshev polynomial approximation is used to satisfy the fluctuation-dissipation theorem for the Brownian suspension. We explore how lubrication, long-range hydrodynamics, particle volume fraction, and shape affect the equilibrium structure and the diffusion of the particles. It is found that once the particle volume fraction is greater than 10%, the particles start to form layered aggregates that greatly influence particle dynamics. Hydrodynamic interactions strongly influence the particle diffusion by inducing spatially dependent short-time diffusion coefficients, stronger wall effects on the particle diffusion toward the walls, and a sub-diffusive regime-caused by crowding-in the long-time particle mobility. The level of asymmetry of the cylindrical particles considered here is enough to induce an orientational order in the layered structure, decreasing the diffusion rate and facilitating a transition to the crowded mobility regime at low particle concentrations. Our results offer fundamental insights into the diffusion and distribution of globular and fibrillar proteins inside cells.

TraG: A Trajectory Generation Technique for Simulating Urban Crowd Mobility

Mobility models, which reproduce traces with basic crowd mobility patterns, are crucial for realistic mobile network simulation and performance evaluation of the planning strategies used for urban networks (such as transit network, communication network, and crowdsensing network). However, trajectories generated by traditional models are often perceived as not realistic for urban context or lack of scalability and universality. This article presents a data-driven trajectory generating technique, named as TraG, that produces synthetic trajectories with the help of some real-world trajectories. Our technique can automatically extract the context features and statistical mobility features, which characterize the mobility of a specific urban crowd from the input empirical traces and, then, regenerate more trajectories based on demand. Moreover, we also summarize the power-law scale correlation of crowd mobility based on four real-world open datasets, including public bicycle traces in New York City and Washington, D.C., taxicab traces in San Francisco and Shenzhen. Finally, we validate the proposed TraG model via the continuous San Francisco taxicab traces, and the result demonstrates that the trajectories simulated by TraG not only inherit the fundamental statistical features of crowd mobility from real traces, but also reflect the features of urban context.

CityTracker: Citywide Individual and Crowd Trajectory Analysis Using Hidden Markov Model

Geospatial big data analytics are changing the way that businesses operate, and have enabled various intelligent services in smart cities. User mobility plays an important role in many context-aware applications, such as location-based advertisement, traffic planning, and urban resource management. This paper proposes CityTracker, a hidden Markov model (HMM)-based framework to predict the temporal-spatial individual trajectory and analyze the representative citywide crowd mobility. After the locations are segmented into points of interests and modeled as states, HMM can find the most likely state sequence in a maximum-likelihood sense, and thus perform trajectory prediction with different orders. In addition, CityTracker can integrate the individual trajectories, achieving representative crowd mobility visualization in the target area. The proposed mechanism is evaluated on a database provided by HyXen, which contains temporal-geospatial records from thousands of smartphones in Taipei. Experimental results confirm the effectiveness of CityTracker, which achieves prediction distance errors of approximately 1.28 km and outperforms traditional probabilistic and regression-based methods. The results also show the most representative crowd mobility behaviors in the map, which is essential for citywide applications.