Toward Interactive Crowd Dynamics in Virtual Built Environment: A BIM-driven and Macro–Micro Integrated Crowd Simulation Framework

Learning crowd behavior from real data: A residual network method for crowd simulation

The real‐time simulation of human crowds has many applications. In a typical crowd simulation, each person ('agent') in the crowd moves towards a goal while adhering to local constraints. Many algorithms exist for specific local ‘steering’ tasks such as collision avoidance or group behavior. However, these do not easily extend to completely new types of behavior, such as circling around another agent or hiding behind an obstacle. They also tend to focus purely on an agent's velocity without explicitly controlling its orientation. This paper presents a novel sketch‐based method for modelling and simulating many steering behaviors for agents in a crowd. Central to this is the concept of aninteraction field(IF): a vector field that describes the velocities or orientations that agents should use around a given ‘source’ agent or obstacle. An IF can also change dynamically according to parameters, such as the walking speed of the source agent. IFs can be easily combined with other aspects of crowd simulation, such as collision avoidance. Using an implementation of IFs in a real‐time crowd simulation framework, we demonstrate the capabilities of IFs in various scenarios. This includes game‐like scenarios where the crowd responds to a user‐controlled avatar. We also present an interactive tool that computes an IF based on input sketches. This IF editor lets users intuitively and quickly design new types of behavior, without the need for programming extra behavioral rules. We thoroughly evaluate the efficacy of the IF editor through a user study, which demonstrates that our method enables non‐expert users to easily enrich any agent‐based crowd simulation with new agent interactions.

This paper studies both the macro and micro level, as well as the linkage between the two, to answer the question of how economic, political, and demographic factors impact a country’s development trajectory. Combining system dynamics, agent-based modeling, and evolutionary games in a complex adaptive system, I formalize a simulation framework of Politics of Fertility and Economic Development (POFED) to understand the relationship between those factors over time. I validate the original system dynamics model with updated data and measure, fuse the endogenous attributes with non-cooperative game theory in an agent-based framework, and simulate the heterogeneous interactions between individuals. This paper demonstrates the linkage between macro environment and micro behavior. Simulations of real world scenarios show network emergence under different environments. The results suggest policy implications for societies at different stages of development.

Transformation induced plasticity steels (TRIP steel) is a kind of low – alloying high strength steel with good combination of strength and plasticity. But the macro mechanical properties depend on the microstructure greatly. For simulation, macro finite element can’t consider the microstructure development fully and micro molecular dynamics can’t be used in macro engineering widely, so to investigate the material behavior of trip steel a multi-scale simulation framework which combined macro finite element simulation and micro molecular dynamics together was presented in this paper. The transformation technology between macro and micro simulation by internal variable was considered and macro displacement of integral point as boundary condition of micro molecular dynamics was discussed.

Crowd Simulation for Dynamic Environments based on Information Spreading and Agents’ Personal Interests

Crowds are a feature of large cities, occurring not only at mass gatherings but also at routine events such as the journey to work. To address extreme crowding, various computer models for crowd movement have been developed in the past decade, and we review these and show how they can be used to identify health and safety issues. State-of-the-art models that simulate the spread of epidemics operate on a population level, but the collection of fine-scale data might enable the development of models for epidemics that operate on a microscopic scale, similar to models for crowd movement. We provide an example of such simulations, showing how an individual-based crowd model can mirror aggregate susceptible-infected-recovered models that have been the main models for epidemics so far.

An understanding of how to alter crowd dynamics would have a significant impact in a number of scenarios, e.g., during riots or evacuations. The social force model, where individuals are self-driven particles interacting through social and physical forces, is one approach that has been used to describe crowd dynamics. This work uses the framework of the social force model to study the effects of introducing autonomous robots into crowds. Two simple pedestrian flow problems are used as illustrative examples, namely flow in varying width hallways and lane formation in bi-directional pedestrian flow. Preliminary results indicate that robots capable of inducing an attractive social force are effective at improving pedestrian flow in both of these scenarios.

Understanding the effects of panic on crowd dynamics in emergency situations has long been considered necessary for pedestrian evacuation control. In the case of disasters, stampedes caused by panic behaviors occur with high possibility, and pedestrians are crushed or trampled, leading to enormous casualties. To eliminate the computational errors accumulated in the traditional macromodel, a macro-microconversion model based on the SF (social force) model and the AR (Aw-Rascle) model is proposed in this paper. The purpose is to use the crowd parameters of the microscopic model as the input part of the macroscopic model and to combine the advantages of the two models to ensure accuracy and improve calculation performance. The concept of the “pressure term” is defined to measure the panic level of the crowd. In addition, a flowchart of the numerical simulation is designed based on the road network conditions at the trampling site. To validate the conversion model, a numerical simulation is conducted in a case study of the Mecca Hajj stampede in 2015. The simulation results display the whole process of crowd marching and meeting with the dynamic variations of the “pressure term.” The simulation results are compared with the traditional simulation results based on a Gaussian distribution, which verifies that the simulation results obtained by the proposed method are closer to the real situation. Moreover, in this study, a new micromacro transformation method for crowd evaluation dynamics, which can enhance computing speed and execution efficiency, is provided.

Realistic models of locomotion, accounting for both individual pedestrian behavior and crowd dynamics, are crucial for crowd simulation. Most existing pedestrian models have been based on ad-hoc rules of interaction and parameters, or on theoretical frameworks like physics-inspired approaches that are not cognitively grounded. Based on the cognitively-plausible behavioral dynamics approach, we argue here for a bottom-up approach, in which the local control laws for locomotor behavior are derived experimentally and the global crowd behavior is emergent. The behavioral dynamics approach describes human behavior in terms of stable, yet flexible behavioral patterns. It enabled us to build an empirically-grounded model of human locomotion that accounts for elementary locomotor behaviors. Based on our existing components, we then elaborate the model with two new components for wall avoidance and speed control for collision avoidance. We show how the model behaves with many stationary obstacles and interacting agents, and how it can be used in agent-based simulations. Five scenarios show how complex individual behavioral patterns and crowd dynamics patterns can emerge from the combination of our simple behavioral strategies. We argue that our model is parsimonious and simple, yet accounts realistically for individual locomotor behaviors while yielding plausible crowd dynamics, like lane formation. Our model and the behavioral dynamics approach thus provide a relevant framework for crowd simulation.

An integrated Belief-Desire-Intention (BDI) modeling framework is proposed for human decision making and planning, whose sub-modules are based on Bayesian belief network (BBN), Decision-Field-Theory (DFT), and probabilistic depth first search (PDFS) technique. To mimic realistic human behaviors, attributes of the BDI framework are reverse-engineered from the human-in-the-loop experiments conducted in the Cave Automatic Virtual Environment (CAVE). The proposed modeling framework is demonstrated for human's evacuation behaviors under a terrorist bomb attack situation. The simulated environment and agents (human model) conforming to the proposed BDI framework are implemented in AnyLogic® agent-based simulation software, where each agent calls external Netica BBN software to perform its perceptual processing function and Soar software to perform its real-time planning and decision-execution functions. The constructed simulation has been used to test impact of several factors (e.g. demographics of people, number of policemen) on evacuation performance (e.g. average evacuation time, percentage of casualties).

An integrated Belief-Desire-Intention (BDI) modeling framework is proposed for human decision making and planning, whose sub-modules are based on Bayesian belief network (BBN), Decision-Field-Theory (DFT), and probabilistic depth first search (PDFS) technique. To mimic realistic human behaviors, attributes of the BDI framework are reverse-engineered from the human-in-the-loop experiments conducted in the Cave Automatic Virtual Environment (CAVE). The proposed modeling framework is demonstrated for human?s evacuation behaviors under a terrorist bomb attack situation. The simulated environment and agents (human model) conforming to the proposed BDI framework are implemented in AnyLogic® agent-based simulation software, where each agent calls external Netica BBN software to perform its perceptual processing function and Soar software to perform its real-time planning and decision-execution functions. The constructed simulation has been used to test impact of several factors (e.g. demographics of people, number of policemen) on evacuation performance (e.g. average evacuation time, percentage of casualties).

To effectively carry out disaster prevention and mitigation, it is essential to study the special disaster risk system. When unexpected events occur in disaster cases with large-scale crowd, crowd evacuation is particularly important. Research on safety evacuation of large-scale crowd has been lasted for a long time. However, current simulation models focus on a closed simulation environment. That means the simulations cannot receive information and data from surroundings. Nevertheless, in real life, with the development of IoT and CPS, the crowd simulations need to dynamically adjust and correct their simulation steps, according to inputting data from sensors or analyzed information. Therefore, in this study, a novel CPS-aware crowd simulation framework is proposed. The framework can help simulation system to conduct and correct “better” results. The framework mainly consists of two components: (1) the simulation mode and (2) the feedback mode. The experimental results show that we can get better evacuation paths with our crowd simulation framework.

Using real life incidents for creating realistic virtual crowds with data-driven emotion contagion

This study introduces a novel crowd simulation framework tailored for special natural environments, such as earthquakes, landslides, and hunting scenarios. The framework integrates continuous dynamics with agent-based mechanisms to model diverse biological cluster interactions effectively. By combining global path planning and local collision detection, it enhances crowd-driven interactions through innovative strategies for path finding, motion, and crowd management. A lightweight 3D reconstruction approach ensures a balance between large-scale simulations, high-fidelity scenarios, and computational efficiency. This framework allows each agent to maintain individual initiative and behavioral diversity, making it highly suitable for large-scale simulations. The proposed model can simulate crowd behavior realistically across various natural scenarios and adapt seamlessly to different environments, offering a robust solution for crowd simulation.

Pedestrian crowd dynamics is influenced by various factors, such as walkway width, travel purpose, number of pedestrians, direction composition, etc. However, most research on pedestrian simulation models regard pedestrians as homogeneous individuals with the same attributes and having the same behavior strategy. This hypothesis develops a simulation model that is easy to understand and compute, but it neglects the influence of individual differences on crowd dynamics. Based on the social-forces model, the authors developed a heterogeneity pedestrian activity model in an agent framework. Different simulation test scenarios were designed for the comparison study of homogeneous and heterogeneous crowd dynamics, and how pedestrian attribute differentiation influences crowd dynamics is discussed. The simulation test results indicate that the heterogeneous mechanism has significant effects on crowd dynamics. For bi-directional pedestrian flow in a walkway, the speed of flow is decreases with the introduction of heterogeneous pedestrians. For uni-directional pedestrians blocked in a bottleneck, compared with homogeneous groups, heterogeneous pedestrian flows are faster to break through the bottleneck congestion situation; even when the average speed of the heterogeneous groups is lower, the total time consumed in the bottleneck is shorter than the homogeneous group.