Crowd simulation plays a crucial role in the prevention and management of public safety events in cities. However, the inherent complexity and diverse nature of human behaviors present substantial challenges in developing realistic and precise simulation models. Traditional rule-based physical models are limited by their reliance on fixed physical formulas and parameters, which hinders their ability to effectively handle the complex tasks associated with crowd simulation. Although deep learning methods have emerged as a promising solution, existing approaches largely emphasize pedestrian trajectory generation while struggling with interpretability and real-time dynamic simulation requirements. To address the aforementioned issues, we propose a novel data-driven crowd simulation framework that integrates physics-informed machine learning (PIML) with navigation potential fields. Our approach takes advantage of the strengths of both physical models and PIML. Specifically, we design an innovative physics-informed spatial-temporal graph convolutional network (PI-STGCN) as a data-driven module. Leveraging PI-STGCN, pedestrian movement trends can be accurately predicted in real-time during the simulation. Additionally, we construct a physical model of navigation potential fields based on flow field theory to guide pedestrian movements, thereby reinforcing physical constraints during the simulation. In our framework, navigation potential fields are dynamically computed and updated based on the movement trends predicted by the PI-STGCN, while the updated crowd dynamics, guided by these fields, subsequently feed back into the PI-STGCN. Comparative experiments on two publicly available large-scale real-world datasets across five scenes demonstrate that our proposed framework outperforms existing rule-based methods in both accuracy and fidelity. The similarity between the simulated and actual pedestrian trajectories improves by 10.8%. Furthermore, our framework exhibits enhanced adaptability and superior interpretability compared with methods that rely solely on deep learning for trajectory generation.

Since the outbreak of the coronavirus disease 2019 (COVID-19), the issue of how to maintain economic development while containing the epidemic has become a significant concern for decision-makers. Though lockdown measures are verified to be very effective in containing the epidemic, its economic costs and other influences have not been fully explored. As a result, decision-makers in many countries are still hesitant to include the lockdown measure in an intervention strategy in response to COVID-19. To address this issue, we propose a universal computational experiment approach for policy evaluation and adjustment based on the Artificial societies, Computational experiments, Parallel execution (ACP) concept. First, we innovatively construct a model via observable CO2 emissions, which is able to estimate the economic costs affected by nonpharmaceutical interventions. Furthermore, based on the population movement data, a risk source model is proposed to estimate the local transmission risk for any prefectures outside the epicenter. Finally, we integrate the data models in a high-resolution agent-based artificial society and carry out large-scale computational experiments supported by the Tianhe supercomputer. Policy adjustments and evaluations are carried out in four cities: Wenzhou, Guangzhou, Beijing, and Wuhan. Our research findings show important implications for policy-making: 1) the local transmission of a city can be almost contained if lockdowns are adopted immediately when the risk index is larger than 1.645, 1.960, or 2.576 at the 90%, 95%, or 99% confidence interval, respectively; 2) if lockdowns are required, in-advance lockdown measures facilitate mitigation efficacy and reduce economic loss; and 3) lockdowns lasting for 7–14 days in a prefecture would be effective in controlling the spread of the epidemic. The duration of the measure should be prolonged with the increment of the initial transmission risk.

Interest management is a filtering technique to reduce communication in simulation. It involves a process called "interest matching" to identify intersections between two sets of d-dimensional axis-parallel rectangles. Because of frequent demands in simulation execution, interest matching becomes a bottleneck as the problem size grows. However, classical interest matching algorithms, mainly designed for serial processing, do not take advantage of modern multicore processors' computing power. Recent parallel interest matching algorithms can fill the gap, but there is scope for improvement. In this paper, we propose a parallel hierarchical sort-based interest matching algorithm. It embeds subscription regions into an interest management tree and allows update regions compare with nodes of the tree to find results in parallel. The association between adjacent nodes and the hierarchical relation between parent-child nodes can serve to eliminate unnecessary operations. Moreover, we also provide proof to confirm the correctness and a detailed analysis of time-complexity. The experimental results demonstrate that the proposed algorithm can achieve better performance than state-of-art algorithms.

The Internet of Things (IoT) and mobile techniques enable real-time sensing for urban computing systems. By recruiting only a small number of users to sense data from selected subareas (namely, cells), sparse mobile crowdsensing (MCS) emerges as an effective paradigm to reduce sensing costs for monitoring the overall status of a large-scale area. The current sparse MCS solutions reduce the sensing subareas (by selecting the most informative cells) based on the assumption that each sample has the same cost, which is not always realistic in the real world, as the cost of sensing in a subarea can be diverse due to many factors, e.g., the condition of the device, location, and routing distance. To address this issue, we proposed a new cell selection approach consisting of three steps (information modeling, cost estimation, and cost-quality beneficial cell selection) to further reduce the total costs and improve the task quality. Specifically, we discussed the properties of the optimization goals and modeled the cell selection problem as a solvable biobjective optimization problem under certain assumptions and approximations. Then, we presented two selection strategies, i.e., the Pareto optimization selection (POS) and generalized cost-benefit greedy (GCB-GREEDY) selection along with our proposed cell selection algorithm. Finally, the superiority of our cell selection approach is assessed through four real-life urban monitoring data sets (Parking, Flow, Traffic, and Humidity) and three cost maps (independent identically distributed with dynamic cost map, monotonic with dynamic cost map, and spatial-correlated cost map). Results show that our proposed selection strategies POS and GCB-GREEDY can save up to 15.2% and 15.02% sample costs and reduce the inference errors to a maximum of 16.8% (15.5%) compared to the baseline-query by committee (QBC) in a sensing cycle. The findings show important implications in sparse MCS for urban context properties.

Pedestrian evacuation simulation is vital for urban civil engineers. Although there exist many works addressing the issue of emergency evacuation, only a few study the phenomenon of people actively squeezing to pass through an exit. To model this behavior, a three-circle model is adopted to represent the shape of pedestrians. Active rotation torque (ART) is proposed to model the active rotation behavior of pedestrians turning their torsos in the desired direction. This torque occurs either when a pedestrian is not facing his velocity direction, or when he wants to pass through a bottleneck. The equation of ART is designed and regressed with real pedestrian experiments, in which a gyroscope was used to measure the angle of torso rotation. The proposed torque model is then applied to manifold scenarios with various door widths and different safety separation belt settings. Then, both microscopic and macroscopic indexes, including evacuation time, rotation angle, and crowd density, are obtained to show that the proposed model can simulate both non-competitive and competitive pedestrian behaviors near exit bottlenecks more accurately than the circular social force model. Thus, the evacuation time of the exit can be predicted more precisely, which helps to design optimal multi-exit assignment strategies.

Cloud computing is attracting an increased number of researches in delivering modeling and simulation abilities as a service. Among which, simulation execution as a service (EaaS) is a hot spot. It aims at releasing users from complex running configurations and meanwhile guaranteeing the QoS requirements. Under the motivation, focusing on EaaS for parallel and distributed simulation (PADS) application, the paper proposes a QoS-aware job scheduling framework in two-tier virtualization-based private cloud data center. In PADS EaaS, an adaptive job size adjustment component is designed to realize intelligent and adaptive job size setting for PADS instead of assigning by users. Furthermore, an adaptive deadline-aware job size adjustment algorithm, named ADaSA, is designed in the adjustment component to realize efficient job scheduling with high job responsiveness. ADaSA algorithm firstly computes a minimum processor requested that leads to maximum runtime stretch. It makes sure that more jobs can be scheduled at the same time while satisfying current job's deadline requirements. On other hand, ADaSA tries to pick up all possible idle CPU time in background virtual machines and reserved ones for other jobs. Through that way, more chances are generated to response more jobs in waiting queue. Finally, we conduct extensive experiments with trace-driven simulation. The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time (up to 90%) and bounded slow down (up to 95%), and at the same time guarantees approximately equivalent deadline-missed rate. ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate (up to 60%).

Real-time prediction of the air contaminant dispersion is an important issue in hazard assessment and emergency management of air pollution. The conventional atmospheric simulation can seldom give the precise prediction results due to inaccurate input parameters. To improve the accuracy of the prediction of atmospheric model, a real-time data driven atmospheric dispersion simulation based on data assimilation is proposed by applying particle filer to the Gaussian puff based model. The coefficients of dispersion in this model are selected as the system state variables and updated by assimilating observed data into the model in real time. To obtain high-quality observed data, a UAV-based air contaminant sensory system is developed. Two experiments are designed and implemented to verify the performance of the method. The results show that the method proposed can update the model parameters and improve the accuracy of prediction results effectively.

The identification of online robot is important for managing the internet order, such as forbidding spam, fictitious information, and inductive words published by online robot to mislead the public. We studied the behaviors of the WeChat users to identify online robot, including turning pages, scanning the web, and forwarding posts. In fact, the amount and time interval of information forwarding were applied to differentiate the abnormal behaviors of WeChat users. Furthermore, we employed information entropy to measure the amount and time interval of information forwarding of WeChat users. The results indicate that the amount of online robot is much less than normal users. What's more, online robots' behavior is abnormal apparently and this identification method is effective.

Artificial society is an effective way for social science research. However, in order to meet real-time and super real-time requirement of computational experiment, the execution efficiency of large-scale artificial society then becomes the burning question. The emergence of heterogeneous parallel system offers opportunities and challenges for accelerating large scale artificial society simulation. How to fully utilize heterogeneous computational resources in large scale agent based simulation becomes the key issue. The paper proposes a CPU/GPU-based accelerating computational method, in which GPU is fully utilized in two different ways at the same time. Firstly GPU is treated as host processor, and a GPU based simulation kernel is designed to execute the models collaboratively with CPU simulation kernel. Secondly, in order to accelerate the domain-specific models, a specific domain-oriented GPU simulation computational service component is proposed, and GPU is used as a co-processor to offer domain-specific parallel optimization. A SPMT (Single Process Multi Threads) based conservative parallel simulation framework is proposed to integrate the GPU simulation kernel and computational service component. At last, an experiment is designed to test the efficiency of GPU based simulation kernel, and argues about the application mode of GPU.

Improving simulation performance using activity tracking has attracted attention in the modeling field in recent years. The reference to activity has been successfully used to predict and promote the simulation performance. Tracking activity, however, uses only the inherent performance information contained in the models. To extend activity prediction in modeling, we propose the activity enhanced modeling with an activity meta-model at the meta-level. The meta-model provides a set of interfaces to model activity in a specific domain. The activity model transformation in subsequence is devised to deal with the simulation difference due to the heterogeneous activity model. Finally, the resource-aware simulation framework is implemented to integrate the activity models in activity-based simulation. The case study shows the improvement brought on by activity-based simulation using discrete event system specification (DEVS).

The cloud computing paradigm attracts increasing amount of efforts to move high performance parallel applications to run in remote data centres. How to balance the performance requirements of parallel applications and the data centre utilization is essential to the move. Existing parallel scheduling mechanisms normally do not consider workload consolidation for improving server utilization. In this paper, we propose a two-tier architecture to organize virtual machines for workload consolidation. The two-tier virtual machines have different CPU priorities. We give a parallel job scheduling algorithm called Two EASY, which extends EASY algorithm, to take care of the job responsiveness in such an architecture. Our extensive experiments show that TwoEASY significantly outperforms the commonly used EASY algorithm in a datacentre setting. The method is practical and effective for consolidating parallel workload in datacentres.