行人运动建模技术综述*

随着计算机数值模拟技术的迅速发展,行人运动仿真技术开始成为人群疏散仿真、城市规划和计算机疏散软件开发方面的研究热点。介绍了对行人建模仿真的初步研究,重点介绍了基于行人行为的微观行人运动建模方法,并对各个模型进行了比较分析和研究展望。     

考虑个体行为的改进CA模型人员疏散研究

为更好模拟行人疏散过程中微观个体行为,考虑行人身材半径及在疏散过程中行人步行速度随运动状态变化,将社会力模型运行规则引入元胞自动机模型,建立了一种社会力模型计算步行速度、空间离散化程度和步行速度较高的疏散模型,用于模拟紧急情况下的行人疏散过程。在该模型中空间划分为更小网格,每个行人占用一到多个单元格,行人的身材半径不再不变,每个行人移动的距离由其速度决定,根据基于速度的出口选择方法和行人运动规律,通过数值模拟分析,研究了疏散过程中的动态性。研究表明基于速度的网格移动数量、行人数量、期望速度、行人身材半径、松弛时间等参数影响疏散效率,结合连续模型的优点能够更加客观真实刻画疏散过程,有助于离散模型描述行人疏散微观行为特征。     

Learning behavior patterns from video for agent-based crowd modeling and simulation

This paper proposes a novel data-driven modeling framework to construct agent-based crowd model based on real-world video data. The constructed crowd model can generate crowd behaviors that match those observed in the video and can be used to predict trajectories of pedestrians in the same scenario. In the proposed framework, a dual-layer architecture is proposed to model crowd behaviors. The bottom layer models the microscopic collision avoidance behaviors, while the top layer models the macroscopic crowd behaviors such as the goal selection patterns and the path navigation patterns. An automatic learning algorithm is proposed to learn behavior patterns from video data. The learned behavior patterns are then integrated into the dual-layer architecture to generate realistic crowd behaviors. To validate its effectiveness, the proposed framework is applied to two different real world scenarios. The simulation results demonstrate that the proposed framework can generate crowd behaviors similar to those observed in the videos in terms of crowd density distribution. In addition, the proposed framework can also offer promising performance on predicting the trajectories of pedestrians.     

Generation and use of sparse navigation graphs for microscopic pedestrian simulation models

For the spatial design of buildings as well as for the layout of large event areas, the crowd behaviour of the future users plays a significant role. The designing engineer has to make sure that potentially critical situations, such as high densities in pedestrian crowds, are avoided in order to guarantee the integrity, safety and comfort of the users. To this end, computational pedestrian dynamics simulations have been developed and are increasingly used in practice. However, most of the available simulation systems rely on rather simple pedestrian navigation models, which reflect human behaviour only in a limited manner. This paper contributes to enhancing pedestrian simulation models by extending a microscopic model by a navigation graph layer serving as a basis for different routing algorithms. The paper presents an advanced method for the automated generation of a spatially embedded graph which is on the one hand as sparse as possible and on the other hand detailed enough to be able to serve as a navigation basis. Three different pedestrian types were modelled: pedestrians with good local knowledge, pedestrians with partly local knowledge and those without any local knowledge. The corresponding algorithms are discussed in detail. To illustrate how this approach improves on simulation results, an example scenario is presented to demonstrate the difference between results with and without using a graph as constructed here. Another example shows the application of the extended simulation in a real-world engineering context. The article concludes with an outlook of further potential application areas for such navigation graphs.     

Enhancing a tsunami evacuation simulation for a multi-scenario analysis using parallel computing

The numerical investigation of tsunami evacuation is becoming a major way to assess the potential evacuation risks and consider countermeasures, but it has been mostly limited to GIS-based static analysis or macroscopic agent-based modelling due to the costs of large-scale simulations. In this paper, we propose a simplified force-based evacuation simulation model and an easy-to-implement parallelization strategy for a large-scale microscopic tsunami evacuation simulation and demonstrate its applications in an actual urban environment. First, the simulation performance was verified and validated against experimental and observational results regarding basic pedestrian movement. The test results qualitatively and quantitatively showed good agreement with real pedestrian movements. The model was then applied to a case of tsunami evacuations in Kesennuma City, where the 2011 Tohoku tsunami caused devastating damage. The model was successfully scaled up to provide urban-scale characterization. In this application, the developed simulator was implemented by a hybrid MPI/OpenMP parallelized computing technique. By utilizing the proposed model with parallel computing, we achieved an urban-scale microscopic evacuation simulation five times faster than real-time and a stochastic simulation to evaluate the uncertainty in the evacuation simulation.     

Spice: a cognitive agent framework for computational crowd simulations in complex environments

Pedestrian behavior is an omnipresent topic, but the underlying cognitive processes and the various influences on movement behavior are still not fully understood. Nonetheless, computational simulations that predict crowd behavior are essential for safety, economics, and transport. Contemporary approaches of pedestrian behavior modeling focus strongly on the movement aspects and seldom address the rich body of research from cognitive science. Similarly, general purpose cognitive architectures are not suitable for agents that can move in spatial domains because they do not consider the profound findings of pedestrian dynamics research. Thus, multi-agent simulations of crowd behavior that strongly incorporate both research domains have not yet been fully realized. Here, we propose the cognitive agent framework Spice. The framework provides an approach to structure pedestrian agent models by integrating concepts of pedestrian dynamics and cognition. Further, we provide a model that implements the framework. The model solves spatial sequential choice problems in sufficient detail, including movement and cognition aspects. We apply the model in a computer simulation and validate the Spice approach by means of data from an uncontrolled field study. The Spice framework is an important starting point for further research, as we believe that fostering interdisciplinary modeling approaches will be highly beneficial to the field of pedestrian dynamics.     

基于Agent的应急疏散模型研究

根据突发事件出现的等级,选择安全区域,并采用适当的疏散方式,选择受灾区域的周边安全区域作为避难所;通过分析影响应急情况下人员选择目的地的因素,对人员疏散行为直观分析并结合人机功效评估,对疏散行为规范,建立基于Agent的应急疏散人员避难所选择模型。通过设定可能影响人员疏散的多种可能因素,该模型能够比较真实地模拟紧急情况下的人员疏散状态。仿真过程与实际情况相似,方法可广泛用于人员应急疏散过程分析研究。     

群体动力学的群组行为仿真

目的 许多群体动画算法侧重从宏观或微观角度模拟人群运动,而结合两种方法模拟群组动态的算法较少,为解决这个问题,提出一种基于群体动力学的群组行为仿真算法。方法 首先,采用连续模型构建动态势能场,为个体计算运动初始速度;然后,基于群体动力学模拟组内跟随和组间避让行为;在组内跟随行为中采用“Car-following”模型为个体计算跟随加速度;在组间避让行为中提出群组的凸包表示方法,并引入局部势能场;最后,结合动态势能场和局部势能场实现群组行为仿真。结果 在每个仿真循环中动态更新全局势能场信息,对比不同群体规模及网格精度的人群仿真效率。实验结果表明本文算法能用于模拟规模较大的多样性群组运动。在网格分辨率为80×80像素的场景中对5 000个个体的运动进行仿真,平均帧速率为35.7 ms（约28帧/s）,与传统的连续模型相比产生了更多的群组行为。采用快速行进法构建全局动态势能场,即使在粗糙网格中也能得到较为平滑的路径。结论 提出算法适用于多样性群组行为仿真,同时结合全局规划和局部控制,无需额外碰撞检测便能真实地模拟组内跟随和组间避让行为,仿真效果具有高效性和多样性。     

Collective Crowd Formation Transform with Mutual Information–Based Runtime Feedback

This paper introduces a new crowd formation transform approach to achieve visually pleasing group formation transition and control. Its core idea is to transform crowd formation shapes with a least effort pair assignment using the Kuhn–Munkres algorithm, discover clusters of agent subgroups using affinity propagation and Delaunay triangulation algorithms and apply subgroup‐based social force model (SFM) to the agent subgroups to achieve alignment, cohesion and collision avoidance. Meanwhile, mutual information of the dynamic crowd is used to guide agents' movement at runtime. This approach combines both macroscopic (involving least effort position assignment and clustering) and microscopic (involving SFM) controls of the crowd transformation to maximally maintain subgroups' local stability and dynamic collective behaviour, while minimizing the overall effort (i.e. travelling distance) of the agents during the transformation. Through simulation experiments and comparisons, we demonstrate that this approach is efficient and effective to generate visually pleasing and smooth transformations and outperform several existing crowd simulation approaches including reciprocal velocity avoidances, optimal reciprocal collision avoidance and OpenSteer.     

A social force evacuation model driven by video data

This paper proposes a video data-driven social force model for simulating crowd evacuation. The initialization of pedestrian position, path navigation, and goal selection in the improved social force model was guided by real video data. To initialize pedestrian position and determine path navigation, the distribution of the pedestrians is set according to the real video. We also extracted the trajectories of pedestrian movement from the videos, and these trajectories were stored into a path set to guide the evacuation of pedestrians. Moreover, a fitness function was defined to model the behavior of a pedestrian goal selection. The fitness function could process the evacuation parameters, which were extracted from the video, and consider the degree and distance of exit congestion. Furthermore, we quantified the relationship values among pedestrians, and a new force called “group force” was added to the primary social force model. Pedestrians with close relationship gathered into one group and walked together. To validate the effectiveness of the proposed method, the video data-driven model was applied to simulate campus halls and roads. Simulation results show that the proposed approach is consistent with real-world situations and can assist in analyzing emergency evacuation scenarios.     

社会力模型中恐慌度概念的分析和改进

社会力模型是人群仿真领域的经典模型,自1995年提出后就被广为应用和调整。该模型2000年又推出一个改进版,增加了恐慌度概念。虽然目前基于社会力模型的研究已经很多,但是针对恐慌度概念分析的研究尚不多见。因此梳理了社会力模型中关键参数物理意义和恐慌度的概念,用恐慌度的变化来解释人群疏散中的“快即是慢”与“从众行为”现象。指出了社会力模型中由于对行人感知描述得不够细致,导致在一定条件下,人群仿真结果存在个别行人不从众与到达出口却不撤离的问题。针对所提问题,通过增加行人视野范围描述以及重定义行人自运动状态等方式来完善社会力模型。实验结果显示,改进后的模型能够较好地模拟人群的从众现象,并且有助于加深对社会力恐慌度概念的理解。     

数据驱动的人群场景动画合成方法

为了向虚拟城区环境加入复杂而灵活的人群动画,提出一种数据驱动的人群场景动画合成方法.通过采集各种典型场景下人群流动的视频,提取出agent状态响应数据集;并将这些状态响应数据集嵌入到虚拟城区知识模型中.最后基于该虚拟城区知识模型实现了一个虚拟城区中人群流动场景的动画合成系统.     

基于人工势场和Agent的人员疏散系统仿真

从聚众场合火灾中疏散群体的个体角度出发,结合人工势场和Agent,提出了微观的仿真模型。该模型首先将场化影响人行为的火、门和障碍物等因素量化,构建了人工势场体系,其次以个体为对象,通过受力分析确定人员移动方向,最后通过是否开启安全通道,有无人员疏导的分析对比,合理仿真了人员疏散情形。基于Visual C++实现了疏散仿真系统,通过仿真结果分析,该系统能够较真实地模拟人员疏散过程。     

考虑行人相对速度的改进社会力模型的验证与评估

在人群仿真的研究领域中,社会力模型是由Helbing提出的一种非常经典的微观仿真模型,能够模拟一些常见的人群自组织现象。但社会力模型仍然存在行人震荡、行人重叠等问题,因此许多学者在参数设置、受力范围、算法优化等方面对社会力模型进行了丰富和改进。目前,Gao等提出的一种考虑行人相对速度的改进社会力模型依然是一些学者进行改进社会力模型研究以及各种仿真实验的基础和重要参考。由于Gao等仅通过两个实验就表明了他们的改进社会力模型的优势这一情况欠缺可靠性,以及没有进行更多的人群自组织实验来表明改进后的社会力模型仍然保留原始社会力模型能够模拟人群自组织现象这一能力,因此文中对Gao等提出的改进社会力模型进行了验证性和评估性实验。通过验证性实验验证了Gao等进行的两个实验,证实了该改进社会力模型的优势。文中通过评估性实验证实了Gao等的改进社会力模型保留了能够模拟人群自组织现象的能力,发现并分析了Gao等的改进社会力模型所存在的行人重叠问题。     

Right of way

Pedestrian models typically represent interactions between agents in a symmetric fashion. In general, these symmetric relationships are valid for a large number of crowd simulation scenarios. However, there are many cases in which symmetric responses between agents are inappropriate, leading to unrealistic behavior or undesirable simulation artifacts. We present a novel formulation, called right of way, which provides a well-disciplined mechanism for modeling asymmetric relationships between pedestrians. Right of way is a general principle, which can be applied to different types of pedestrian models. We illustrate this by applying right of way to three different pedestrian models (two based on social forces and one based on velocity obstacles) and show its impact in multiple scenarios. Particularly, we show how it enables simulation of the complex relationships exhibited by pilgrims performing the Islamic religious ritual, the Tawaf.     

A collective motion model based on two-layer relationship mechanism for bi-direction pedestrian flow simulation

In crowd dynamic, relations are existed among some pedestrians, which cause frequent interactions during evacuation, creating collective motion phenomena, such as the most common pattern of team-groups. Besides, collective behavior can make a beneficial effect on the evacuation process. Therefore, this paper proposes a collective motion model to simulate bi-direction pedestrian flow. First, a method of group vision sharing is proposed to help pedestrians learn the crowd around. Based on two-layer relationship mechanism proposed, aggregate force and collective collision avoidance force are added into the original social force formula. The aggregate force is the resultant of two forces, one is the attraction among the leader and team members, and the other one is that among members of groups due to the social relations. Simulation results show that the modified model can reproduce the team-groups collective pattern in real world bi-direction pedestrian flow, and can reduce the collision risk with regarding the group as collision avoidance unit. Furthermore, the evacuation efficiency is improved.     

A GPU-assisted hybrid model for real-time crowd simulations

In this paper, we propose two new techniques for real-time crowd simulations; the first one is the clustering of agents on the GPU and the second one is incorporating the global cluster information into the existing microscopic navigation technique. The proposed model combines the agent-based models with macroscopic information (agent clusters) into a single framework. The global cluster information is determined on the GPU, and based on the agents' positions and velocities. Then, this information is used as input for the existing agent-based models (velocity obstacles, rule-based steering and social forces). The proposed hybrid model not only considers the nearby agents but also the distant agent configurations. Our test scenarios indicate that, in very dense circumstances, agents that use the proposed hybrid model navigate the environment with actual speeds closer to their intended speeds (less stuck) than the agents that are using only the agent-based models.     

A cognitive pedestrian behavior model for exploratory navigation: Visibility graph based heuristics approach

Navigation is a complex issue in simulating pedestrian dynamics. Many existing studies have investigated how pedestrians navigate from an origin to a specific destination, i.e., traveling to familiar or novel destinations. However, pedestrians’ navigation through built environment without specific destinations still remains as an open issue. This exploratory navigation usually involves more spatial cognitive behaviors and perceptual considerations. To represent realistic route choice and natural movement of theses pedestrians, this paper presents a cognitive pedestrian behavior model with a focus on the space visibility and individual characteristics. Then the sensitivity of model parameters is analyzed, indicating that regional target, memory of visiting records and individual crowd tendency can have varied influences on pedestrians’ navigation decisions and movement patterns. As a case study, the proposed model is implemented using the pedestrian movement data collected from an observational study of Tate Gallery Museum. The simulation results show good consistency with the actual data at the aggregate level and those obtained individually, indicating that our proposed model is credible and can benefit real applications such as master planning of a museum.     

Modelling the plastic anisotropy of metals

Summary This work is an overview of available constitutive laws used in finite element codes to model elastoplastic metal anisotropy behaviour at a macroscopic level. It focuses on models with strong links with the phenomena occurring at microscopic level. Starting from macroscopic well-known models such as Hill or Barlat's laws, the limits of these macroscopic phenomenological yield loci are defined, which helps to understand the current trends to develop micro-macro laws. The characteristics of micro-macro laws, where physical behaviour at the level of grains and crystals are taken into account to provide an average macroscopic answer are described. Some basic knowledge about crystal plasticity models is given for non-specialists, so every one can understand the microscopic models used to reach macroscopic values. The assumptions defining the transition between the microscopic and macroscopic scales are summarized: full constraint or relaxed Taylor's model, self-consistent approach, homogenisation technique. Then, the two generic families of micromacro models are presented: macroscopic laws without yield locus where computations on discrete set of crystals provide the macroscopic material behaviour and macroscopic laws with macroscopic yield locus defined by microscopic computations. The models proposed by Anand, Dawson, Miehe, Geers, Kalidindi or Nakamachi belong to the first family when proposals by Montheillet, Lequeu, Darrieulat, Arminjon, Van Houtte, Habraken enter the second family. The characteristics of all these models are presented and commented. This paper enhances interests of each model and suggests possible future developments.     

ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent‐Based Crowd Simulation

How to realistically model an agent's steering behaviour is a critical issue in agent‐based crowd simulation. In this work, we investigate some proactive steering strategies for agents to minimize potential collisions. To this end, a behaviour‐based modelling framework is first introduced to model the process of how humans select and execute a proactive steering strategy in crowded situations and execute the corresponding behaviour accordingly. We then propose behaviour models for two inter‐related proactive steering behaviours, namely gap seeking and following. These behaviours can be frequently observed in real‐life scenarios, and they can easily affect overall crowd dynamics. We validate our work by evaluating the simulation results of our model with the real‐world data and comparing the performance of our model with that of two state‐of‐the‐art crowd models. The results show that the performance of our model is better or at least comparable to the compared models in terms of the realism at both individual and crowd levels.