With the increasing number of studies on crowd behavior analysis, there has been a widespread interest in treating subgroups as an important topic. A previous experimental study has investigated the decision-making and motion behavior of subgroups when facing a static obstacle during movement. However, it is hard to quantify social attributes (e.g., interpersonal relationships and sense of identity) and little is known about how they affect the movement process of subgroups. Here, we propose a vision-driven model to solve this problem, in which two key model parameters are defined to control the spatial cohesion and attraction intensity, respectively. Numerical simulations demonstrate that the optimal regions of model parameters vary depending on different conditions of the three control variables (obstacle width, time pressure, and subgroup size). The spatial cohesion and attraction intensity barely change the movement process of subgroups in the maintaining state but significantly affect it in the splitting-merging state. This model can reproduce the herding effect of subgroup members in the merging process, which is affected to varying degrees by the modulation of model parameters. Overall, this work contributes to the simulation of subgroup behaviors from a sociopsychological perspective.

Magnetic sensor arrays are typically used to detect magnetic targets. Currently, research on sensor array calibration focuses on solving the problems of inconsistent sensitivity, zero offset, and non-orthogonality in individual sensors, and misalignment errors between sensors. However, in magnetic field detection, sensor arrays are usually mounted on a platform or carried as a handheld device and are prone to random swaying during the detection process, leading to changes in the attitude and position of the magnetic sensors, which in turn generates swaying errors. In this study, the source of swaying error in sensor arrays was first analyzed theoretically, and a swaying error model of a magnetic sensor was established. Second, a swaying error calibration method was proposed in combination with the classical prediction model—Gaussian process regression (GPR), backpropagation (BP) neural network, and support vector machine (SVM) in the field of artificial intelligence. The experimental results show that the prediction performance based on the BP neural network is the most outstanding. After correction, the relative error percentage of the swaying error in the magnetic field data decreased significantly from 165.50% to 9.35%, which is a significant improvement of the correction effect. In addition, we conducted model comparison experiments in different environments, and the results show that the BP model performs well in various environments, demonstrating its strong generalization ability and robustness. Finally, the distance error of the magnetic dipole position was significantly reduced after calibration, from 1.56, 1.12, and 2.50 m to 0.17, 0.04, and 0.04 m, respectively. Thus, the effectiveness of the calibration method was verified.

Due to the universal existence of human subgroups in reality, an increasing number of studies have incorporated them into the modeling process of crowd evacuation. However, such models seldom explain subgroup behaviors from the aspect of what individuals see and how they respond to visual input. Here, we propose a vision-driven model based on cognitive heuristics, in which the mechanisms of avoidance with the environment and attraction to other members within the field of view are explicitly clarified. Numerical simulations demonstrate that various spatial characteristics of subgroup members can be effectively represented by this model, and both the intensity and heterogeneity of spatial cohesion have significant impacts on subgroup evacuation. By comparing with an empirical study, the reproducibility of our model has been validated in terms of the temporal and spatial dimensions. This model produces more natural and realistic subgroup behaviors in multiple interaction contexts than existing models, and also quantitatively exhibits the superiority in reproduction effects. Overall, this work provides an interpretable mathematical framework for modeling subgroups from the perspective of visual perception.

Pedestrian and evacuation dynamics provide valuable insights into the understanding of human collective motion and have important implications for architectural design, safety management, and transportation science. In social and biological systems, the macroscopic patterns are displayed at the group level, whereas the microscopic behaviors are presented at the individual level. As an intermediate layer, subgroups play a crucial role in linking these two distinct levels of observation and have become one of the important research topics in this field. However, a comprehensive review is still lacking for summarizing current advancements around this topic. Therefore, this paper proposes a survey framework to conduct a systematic review of subgroup research from the following four aspects: data collection and extraction, analysis of phenomena and behaviors, modeling and simulations, and applications and solutions. More critically, a series of research gaps in each aspect are explicitly determined to help researchers grasp unsolved problems. Finally, we present future avenues to narrow the research gaps, which are expected to bring inspiration and guidance for subsequent studies.

In today’s high-density urban environments, understanding pedestrian behavior in emergency evacuations is increasingly crucial. This study develops a sophisticated model integrating small-world network dynamics with emotional contagion to dissect pedestrian behaviors in such situations. Utilizing agent-based and complex network analyses, it delves into phase transitions in pedestrian order under various risk scenarios. The findings demonstrate significant behavioral variations in medium-risk environments, which are especially susceptible to transitions from orderly to disorderly states. This model emphasizes the important roles of emotional contagion in shaping crowd dynamics and the psychological factors alongside physical ones in evacuation strategies. Notably, this research illuminates the impacts of crowd size and speed on disorder evolution, which offers valuable insights for urban planning, architectural design, intelligent transportation systems, and emergency management. Overall, our study enriches the understanding of pedestrian dynamics in emergencies and provides a foundation for developing more effective public safety measures.

The study of identifying human subgroups from videos is a significant topic, which has received a lot of attention in multiple disciplines. So far, however, there has been little consideration about combining it with relevant conceptions in network science. Therefore, this article proposes a novel method for the automatic identification of human subgroups in dynamic pedestrian flows. The spatial proximity and temporal continuity are combined to calculate the interaction intensity between pedestrians, by which a time-dependent pedestrian flow network is constructed. Based on the objective function of weighted partition density, the optimal threshold is used to determine community structures that correspond to human subgroups in frame images. Numerical experiments demonstrate that our method achieves high identification accuracy under various evaluation datasets, and exhibits better performance than existing methods in terms of different crowd densities, various numbers of subgroup members, and certain levels of trajectory noise. Furthermore, this work provides valuable implications for the understanding of subgroup behaviors and the modeling of subgroup movements.

The study of crowd evacuation has received considerable attention as the frequent occurrence of crowd disasters in public places. Notably, the increasing proportion of disabled pedestrians makes vulnerable crowds an indispensable part of the evacuation process. However, most previous research neglects to introduce the motion characteristics of disabled pedestrians into the modeling of crowd evacuation. Therefore, we develop an extended model to simulate the evacuation process involving nondisabled, visual-disabled, acoustic-disabled, and physical-disabled pedestrians. Numerical simulations indicate that this model achieves a more realistic mixed crowd evacuation in the library scene and reproduces the escape movement of multitype disabled pedestrians. Moreover, several management strategies are provided to guide the evacuation of disabled pedestrians, and the appropriate strategy can be determined by comprehensively considering multiple factors such as efficiency, safety, and cost.

A positioning method based on magnetic gradient tensor was proposed for UXO detection on movable platforms. A multi-resolution linear regression algorithm was developed to select measurement points collected when the instrument was relatively stable. In this algorithm, Pearson Correlation Coefficient (PCC) was adopted as a quality factor for characterizing the stability of instrument. Then, selected measurement points were processed in a modified Euler method for UXO position inversion. The results from the modified Euler method were selected again based on normalized magnetic source strength (NSS) and clustered by the DBSCAN algorithm. Validity of the method was tested with both simulation data and field experiment data collected by a backpack gradiometer. The test result showed that all 4 targets were detected and positioning errors were all lower than 0.15m.

Human crowds exhibit rich self-organizing behaviors through local interactions. The understanding of interaction mechanisms has important implications for the management of large-scale crowds. Although most vision-based heuristic models are successful, some features such as distracted pedestrians, and empirical phenomena like sudden turns are difficult to be explained. Here, a heuristic interaction model is proposed, which incorporates the extracted laws of pedestrian heterogeneity and mutual anticipation. We argue that pedestrians are heterogeneous in terms of speed and attention (e.g., distracted by cell phones), and have anticipations for other pedestrians’ velocities during the interaction. Numerical simulations indicate that our model realistically simulates the self-organizing phenomenon in the “distraction experiment,” along with related experimental findings. The “freezing-by-heating” phenomenon, as well as interesting phenomena such as “sidewalk shuffling” are successfully predicted, as exhibited in empirical observations. Taken together, our model may serve various potential fields involving the control of traffic flows and navigation of autonomous swarm robots.

As one of the most effective technologies for network reconstruction, compressive sensing can recover signals from a small amount of observed data through sparse search or greedy algorithms in the assumption that the unknown signal is sufficiently sparse on a specific basis. However, there often occurs loss of precision even failure in the process of reconstruction without enough prior information. Therefore, the purpose of this article is to solve the problem of low reconstruction accuracy by mining implicit structural information in the network. Specifically, we propose a novel and efficient algorithm (MCM_TRA) for reconstructing the structure of the K -forked tree network. Based on evolutionary game dynamics, the modified clustering method (MCM) classifies all nodes into two sets, then a two-stage reconstruction algorithm (TRA) is illustrated to recover the node signals in different sets. The experimental results demonstrate that the MCM_TRA enhances the reconstruction accuracy prominently than previous algorithms. Moreover, extensive sensitivity analysis shows that the reconstruction effect can be promoted for a broad range of parameters, which further indicates the superiority of the proposed method.

As one of the most effective models for human collective motion, the social force model (SFM) simulates the dynamics of crowd evacuation from a microscopic perspective. However, it treats pedestrians as the homogeneous rigid particles, whereas pedestrians are diverse and heterogeneous in real life. Therefore, this paper develops a pedestrian heterogeneity-based social force model (PHSFM) by introducing physique and mentality coefficients into the SFM to quantify physiology and psychology attributes of pedestrians, respectively. These two coefficients can affect the self-driven force by changing the desired speed, thus characterizing the pedestrian heterogeneity more realistically. Simulation experiments demonstrate that the PHSFM designs a more general and accurate theoretical framework for the expression of pedestrian heterogeneity, which realizes special behavior patterns caused by individual diversity. Furthermore, our model provides effective guidelines for the management of crowds in potential research fields such as transportation, architectural science and safety science.

With the increasing scale of crowds in public places, the study of modeling crowd evacuation has become a significant research field. However, most previous research ignores to incorporate behavioral heterogeneity of individuals into the modeling framework, making it hard to replicate more realistic evacuation processes. Therefore, a behavioral heterogeneity-based social force model (BHSFM) is proposed to reveal the heterogeneity characteristics from the aspect of individual behavior. Numerical experiments show that the BHSFM provides a general mathematical framework for describing behavioral heterogeneity and forms a more reasonable and elaborate evacuation process. Notably, some interesting evacuation phenomena can emerge by integrating the behavioral heterogeneity coefficient with temporal-spatial dynamic risk indexes. Compared with the social force model (SFM), higher frequencies of small-scale displacements are performed by BHSFM due to more pushing behaviors. Furthermore, the periods and areas of a potential crowd disaster are revealed by our model under different numbers of pedestrians, which has important guiding significance for formulating reasonable evacuation schemes in specific scenarios.

This work proposes a fiber-enabled optical wireless communication (FE-OWC) system for bidirectional communications between the base station (BS) and a number of mobile user terminals (UTs) via full beam coverage, aimed at facilitating ultra-high data rate communications. The FE-OWC system comprises optical antennas, optical chains, and baseband units at both BS and UTs. The innovative optical antenna consists of an array of fiber ports and a transceiver lens, which can form a number of transmit and receive optical beams and provide a full beam coverage for simultaneous downlink and uplink connections with a number of UTs, respectively. We present analysis to characterize downlink and uplink channel models and gains, including both optical and electrical parts, between the BS and UTs and conduct a complete link budget analysis. We further design downlink and uplink multiuser multiple-input multiple-output (MIMO) as well as massive MIMO transmission protocols and develop asymptotically optimal schemes for a large number of fiber ports. Numerical results illustrate that the FE-OWC system has the potential to support over 10 Gbps data rate per UT and Tbps system throughput required in future 6G mobile communication systems.

Transient traffic spikes are becoming a crucial challenge for network operators from both user-experience and network-maintenance perspectives. Different from long-term traffic growth, the bursty nature of short-term traffic fluctuations makes it difficult to be provisioned effectively. Luckily, next-generation elastic optical networks (EONs) provide an economical way to deal with such short-term traffic fluctuations. In this paper, we go beyond conventional network reconfiguration approaches by proposing the novel lightpath-splitting scheme in EONs. In lightpath splitting, we introduce the concept of SplitPoints to describe how lightpath splitting is performed. Lightpaths traversing multiple nodes in the optical layer can be split into shorter ones by SplitPoints to serve more traffic demands by raising signal modulation levels of lightpaths accordingly. We formulate the problem into a mathematical optimization model and linearize it into an integer linear program (ILP). We solve the optimization model on a small network instance and design scalable heuristic algorithms based on greedy and simulated annealing approaches. Numerical results show the tradeoff between throughput gain and negative impacts like traffic interruptions. Especially, by selecting SplitPoints wisely, operators can achieve almost twice as much throughput as conventional schemes without lightpath splitting.

Degraded provisioning provides an effective solution to flexibly allocate resources in various dimensions to reduce blocking for differentiated demands when network congestion occurs. In this work, we investigate the novel problem of online degraded provisioning in service-differentiated multi-layer networks with optical elasticity. Quality of Service (QoS) is assured by service-holding-time prolongation and immediate access as soon as the service arrives without set-up delay. We decompose the problem into degraded routing and degraded resource allocation stages, and design polynomial-time algorithms with the enhanced multi-layer architecture to exploit network flexibility in temporal and spectral dimensions. Numerical results verify that we can achieve significant blocking reduction, especially for requests with higher priorities. They also indicate that degradation in optical layer can increase the network capacity, while degradation in electric layer provides flexible time-bandwidth exchange.

A class-aware routing scheme is proposed in this paper for OBS networks providing offset-time-based differentiated services (DiffServ). To unravel the structure of the routing problem, mathematical programming formulations are utilized. An optimization model for two-class OBS networks is presented and studied in particular. The objective is to minimize the burst loss probability over the entire network for both traffic classes. The effectiveness of the routing model is evaluated via illustrative examples based on programming techniques. Numerical results show that compared with traditional class-oblivious routing schemes, class-aware routing further reduces the burst loss probability of multiple classes especially when the network load is light or moderate.

This paper investigates backup reprovisioning technique to mitigate the impact of double near-simultaneous link failure scenarios. First, a refined model is introduced for accurate identification of vulnerable connections after the failure of the first link. Then, an improved approach termed Successive Backup Reprovisioning (SBR), is developed for online backup reprovisioning to further reduce the number of affected connections when no extra capacities are added to the network for reprovisioning purpose. Complexity analysis and simulation results show that SBR has advantages in tradeoff between capacity efficiency and execution speed