Modelling contra-flow in crowd dynamics DEM simulation

This paper highlights the growing need for a realistic crowd simulation in the design of large venues such as concert halls and stadia. A discrete element method (DEM) technique for modelling crowd dynamics has been developed that represents each person within the model as 3 overlapping circles, a position, orientation and velocity in 2D. Contact forces between elements are included in the model as well as psychological forces, motive forces and moments. The motion of each person is then modelled in a Newtonian manner with a numerical integration time-stepping scheme. The model has been shown previously to work well in predicting egress. In this paper the predicted model behaviour is compared to actual video footage shot at various locations around University Park Campus, Nottingham. It did not match well to the video footage when people are moving towards each other, as in cases of contra-flow on a walkway. In order to improve the model, a general algorithm for ‘avoidance’ was included which appeared to make the model significantly more realistic in these cases. The paper also shows areas for further potential development, such as incorporating people into associative groups such as family or friends.     

Study on mechanics of crowd jam based on the cusp-catastrophe model

Some characteristics of crowd jam like the phenomena of discontinuous jumping in reality are hard to be explained by the equations governing pedestrians. The catastrophe theory can explain these characteristics. A cusp-catastrophe model is developed to analyze the mechanics of crowd jam by drawing graphs for a cusp-catastrophe model of crowd, the bifurcation set and the projection of catastrophe model. Meanwhile, the critical density and the critical velocity are derived. It is concluded that the cusp model is a more efficient predictor than the linear model or the equations governing pedestrians and is reasonably realistic for dense crowd flow scenarios.     

Towards automatic and robust adjustment of human behavioral parameters in a pedestrian stream model to measured data

People die or get injured at mass events when the crowd gets out of control. Urbanization and the increasing popularity of mass events, from soccer games to religious celebrations, enforce this trend. Thus, there is a strong need to better control crowd behavior. Here, simulation of pedestrian streams can be very helpful: Simulations allow a user to run through a number of scenarios in a critical situation and thereby to investigate adequate measures to improve security. In order to make realistic, reliable predictions, a model must be able to reproduce the data known from experiments quantitatively. Therefore, automatic and fast calibration methods are needed that can easily adapt model parameters to different scenarios. Also, the model must be robust. Small changes or measurement errors in the crucial input parameters must not lead to disproportionally large changes in the simulation outcome and thus potentially useless results. In this paper we present two methods to automatically calibrate pedestrian simulations to the socio-cultural parameters captured through measured fundamental diagrams. We then introduce a concept of robustness to compare the two methods. In particular, we propose a quantitative estimation of parameter quality and a method of parameter selection based on a criterion for robustness. We discuss the results of our test scenarios and, based on our experience, propose further steps.     

人群密度对人群拥挤事故的影响

为了研究公共场所的人群密度与拥挤事故的发生概率.基于连续人群流动模型,从人群密度角度探讨了人群拥挤事故发生的机理.由于不同民族个体生理尺寸的差异,人群最大忍受密度不同,以此作为判断人群拥挤事故的标准,并结合我国情况提出我国人群最大忍受密度为9人/m2.最后模拟了某个拥挤事故场景,用该模型对其进行拥挤事故分析.结果表明,连续人群流动模型可以用于预测拥挤事故的发生,对预防和控制人群拥挤事故具有一定的指导意义.     

基于协调联动的电力无脚本演练情景模型研究

为了实现无脚本演练,提高电力企业应急演练的成效。在分析电力企业内外部协调联动关系的基础上,借鉴霍尔三维结构模型原理,提出情景模型分为三个维度,即组织逻辑维、处置知识维、事件时间维,分析了各个维度的组成内容与逻辑关系,并根据无脚本演练的特点,将情景规则作为在情景模型的要素之一,进而构建了电力无脚本演练情景模型。将模型在实际应急演练中进行了应用,为合理设计电力无脚本演练情景提供了支持。     

城市重点公共区域人群聚集风险的实时定量技术

城市公共区域人群高度聚集且流动性大,紧急状态时易发生群死群伤的拥挤踩踏事故,造成大量人员伤亡和社会负面影响。在FIST模型的基础上,提出以人员密度（D）、人员特性（C）、人与人的相互作用（I）以及人群聚集环境（E）作为表征公共场所人群聚集风险的基本参数,接下来对四个参数进行了相应的技术分析。第一,利用人群监控技术估计人群密度;第二,通过现场监测网络得到的人群压力值来表征人与人相互作用的强度;第三,忽略了个体差异对人群整体的影响;第四,把公共场所中导致事故发生的影响因子归结为综合扰动强度,并建立了相应的数学模型表征了这种强度的大小。最终建立了描述人群聚集风险的DICE模型。同时,给出了人群密度阈值、人群压力阈值以及人群聚集风险的总阈值及其判断标准,整个工作将人群聚集风险实时定量及管理技术推向了实用化的道路。     

考虑人群拥堵的疏散出口选择行为研究及建模

出口选择是疏散过程中最重要和复杂的决策行为之一,受到空间结构、人群分布和行人认知等多方面因素的影响,为了使疏散仿真能够合理地模拟出行人的寻路和逃生过程,提出了考虑人群拥堵的出口选择模型。该模型基于多元Logit离散选择模型,考虑到影响个体疏散时间的因素以及对不同出口类型的偏好。研究发现个体的疏散时间随着拥堵人群位置的不同而有所差异,从而以决策者而不是出口的角度定义了一个计数区域来估计影响决策者排队时间的人数。并且研究出口重新选择的触发条件和程序。将出口选择模型和程序引入BuildingEXODUS软件进行疏散仿真,通过对比显示,仿真结果更符合真实情况,特别是在具有障碍物和人群分布不均匀的场景中更显优势。     

Modified porosity approach and laminar flamelet modelling for advanced simulation of accidental explosions

The production of oil and gas is an inherently hazardous task. Therefore it is crucial to provide reliable estimates of the risks involved. The major contributors to the risk level of an offshore installation, for example, arise from accidents involving explosion and fire. Computational Fluid Dynamics (CFD) can be a powerful tool to help with the calculation of accidental explosion scenarios. In this context, the present work suggests a novel implementation of a model based on a modified Porosity Distributed Resistance (MPDR) approach within an unstructured 3D Navier–Stokes solver. The model operates by representing parts of the filtered geometry from the original model through porosity values attributed to an unstructured tetrahedral mesh. Extra resistance terms are added in the momentum equation as well as extra sources of turbulence. Two extra sources of turbulence are modelled. The first of these is due to the shear layers of the non-resolved obstacles, whilst the second is due to the presence of wakes behind the non-resolved obstacles. Results for 2D and 3D test cases are compared against both experimental data and simulations with fully-resolved geometry and good agreement is observed.     

公路隧道火灾环境人群逃生行为分析及多项Logit建模*

为提高隧道疏散效率,研究环境、社会和个人因素对逃生行为的影响。基于虚拟现实技术从心理学和行为学角度对公路隧道逃生行为及影响因素进行研究,利用Unity 3D建立虚拟场景下的公路隧道场景,通过步行模拟器和ErgoLAB无线生理模块采集步速和心电;应用核密度曲线研究平均心率的变化特性;采用Hermite插值法描述步速的变化趋势。在试验结果的基础上,构建基于多项Logit的逃生行为模型。结果表明:平静状态下,男性的平均心率个体差异性较大,运动状态下,女性的平均心率波动较大;测试者在隧道内遇到障碍物会降低步速,在逐渐深入并穿越人群的过程中,呈现步速加快的特征;逃生行为的MNL模型R2和准确度分别达到87.41%和92%;隧道内障碍物越少,前方人群越多,且能见度越差,人越容易处于恐慌状态,更倾向于跟随他人。研究结果可为火灾环境下的公路隧道人群疏散模型的建立提供参考,对避免人员伤亡和减少财产损失具有实际意义。     

A conceptual offshore oil and gas process accident model

The purpose of this paper is to present and discuss an accident prevention model for offshore oil and gas processing environments. The accidents that are considered in this work relate specifically to hydrocarbon release scenarios and any escalating events that follow. Using reported industry data, the elements to prevent an accident scenario are identified and placed within a conceptual model to depict the accident progression. The proposed accident model elements are represented as safety barriers designed to prevent the accident scenario from developing. The accident model is intended to be a tool for highlighting vulnerabilities of oil and gas processing operations and to provide guidance on how to minimize their hazards. These vulnerabilities are discussed by applying the 1988 Piper Alpha and the 2005 BP Texas City disaster scenarios to the model.     

A framework for definition of logical scenarios for safety assurance of automated driving

Abstract

Objective: In order to introduce automated vehicles on public roads, it is necessary to ensure that these vehicles are safe to operate in traffic. One challenge is to prove that all physically possible variations of situations can be handled safely within the operational design domain of the vehicle. A promising approach to handling the set of possible situations is to identify a manageable number of logical scenarios, which provide an abstraction for object properties and behavior within the situations. These can then be transferred into concrete scenarios defining all parameters necessary to reproduce the situation in different test environments.

Methods: This article proposes a framework for defining safety-relevant scenarios based on the potential collision between the subject vehicle and a challenging object, which forces the subject vehicle to depart from its planned course of action to avoid a collision. This allows defining only safety-relevant scenarios, which can directly be related to accident classification. The first criterion for defining a scenario is the area of the subject vehicle with which the object would collide. As a second criterion, 8 different positions around the subject vehicle are considered. To account for other relevant objects in the scenario, factors that influence the challenge for the subject vehicle can be added to the scenario. These are grouped as action constraints, dynamic occlusions, and causal chains.

Results: By applying the proposed systematics, a catalog of base scenarios for a vehicle traveling on controlled-access highways has been generated, which can directly be linked to parameters in accident classification. The catalog serves as a basis for scenario classification within the PEGASUS project.

Conclusions: Defining a limited number of safety-relevant scenarios helps to realize a systematic safety assurance process for automated vehicles. Scenarios are defined based on the point of the potential collision of a challenging object with the subject vehicle and its initial position. This approach allows defining scenarios for different environments and different driving states of the subject vehicle using the same mechanisms. A next step is the generation of logical scenarios for other driving states of the subject vehicle and for other traffic environments.     

基于灾变的人群疏散避灾路径优化及应用

突发事件条件下的应急疏散是一个复杂的过程,不仅要考虑时间因素,而且要考虑疏散网络中各路段的受灾风险水平。因此,对于人群快速转移避灾路径的解算结果,不仅要求快速,还要求避灾。基于灾害预测的疏散路径优化理论模型,在求解时变灾害条件下,集成考虑实时与未来灾害影响的转移避灾路径优化算法。采用C#程序设计语言和Visual studio 2013平台,开发了进行模型算法实现的原型系统,并通过算例进行了说明。结果表明:在预先设置的灾害情景中,提出的模型算法可以提前规划出避开灾害且疏散时间较短的路径,为制定应急预案提供参考依据。     

Evaluation on consequences prediction of fire accident in emergency processes for oil-gas storage and transportation by scenario deduction

This paper takes the safety in emergency processes as the starting point, from the perspective of scenario deduction, to study the consequences of fire accidents for oil-gas storage and transportation. Through the statistical analysis of actual accident cases, 19 frequently occurring basic scenarios in emergency processes are summarized. The scenario evolution paths of fire accidents for oil-gas storage and transportation are given by analyzing the evolution law of the accident development. Fuzzy numbers are introduced to express experts' qualitative judgment on accident scenarios. The empirical probabilities of scenario nodes are obtained by defuzzification calculation, and the state probability of each scenario node is calculated by using the dynamic Bayesian network joint probability formula. Under the comprehensive consideration about the probability statistics of actual accident cases, the critical scenario nodes on the evolution path and their final scenario probabilities are jointly determined to realize the optimization of the scenario evolution path. By constructing the correlation between the optimized scenario evolution path and the accident consequences, an accident consequence prediction model is established. The occurrence probability of accident consequences is calculated by the defuzzification method and dynamic Bayesian network. The accuracy of the consequence prediction model is verified by the July 16 Dalian's Xingang Harbor oil pipeline explosion accident. The research results provide scientific basis for helping decision makers to make the effective emergency measures that are most conducive to the rapid elimination of accidents and reducing the severity of accident consequences.     

人群应急疏散模型构建及疏散效率分析研究*

为更系统地分析社会网络内预警传播对人群疏散效率影响,以江苏德桥仓储有限公司“4·22”较大火灾事故为背景,构建人群应急疏散模型,模拟不同预警策略应用下社会预警传播及人群疏散动态过程,分析不同情景动态演化特征。研究结果表明:高效预警策略确保多数人被预警、疏散的同时,会造成人群集聚性疏散,引发严重交通拥堵,需采取有效方法降低交通实时承载量,保障疏散速度,进一步提升疏散效率。研究结果可为完善应急疏散方案提供借鉴。     

基于BP神经网络人群流量预测的实现

人群拥挤踩踏突发性的特点决定了现场的事故救援措施效果较差,事前预防是唯一有效的策略。对商业区人群流量进行预测,对于合理控制商业网点人口,预防人群类事故的发生具有重要的意义。本文介绍了基于BP神经网络的人群流量预测方法,利用Matlab建立了相关模型,并结合实际数据对模型进行了调整,分析了隐含层神经元个数、不同输入-输出结构、不同传递函数等因素对网络性能的影响。研究表明利用神经网络的非线性映射能力对人群流量进行预测时可行的。     

Scenario identification and evaluation for layers of protection analysis

The identification and screening of scenarios has been identified as a source of variation in Layers of Protection Analysis (LOPA). Often the experience of the analyst is a significant factor in determining what scenarios are evaluated and the worst credible consequences. This paper presents a simplified chemical process risk analysis that is effective in providing a semi-quantitative measure of consequence that may include human harm and is independent of the analyst. This process may be used in evaluation of Management of Change, inherently safer design decisions for capital projects and LOPA re-validation. Conditional and relational logic may be captured with the use of simple spreadsheets to further improve overall efficiency. For example, this method minimizes the overall time required for scenario development and re-validation relative to Hazard and Operability studies (HAZOP).The technique simplifies established models used by engineers engaged in the operation or design of a chemical manufacturing facility without special software or training. The results of this technique are realistic and may be directly compared with corporate or regulatory guidelines for risk of fatality or injury. At each step in the risk analysis process, more detailed or sophisticated methods may be used to refine the technique. Furthermore, results from any step may indicate that the hazard from a specific scenario case is not sufficient to continue with subsequent analysis steps.     

Two discrete choice experiments on laboratory safety decisions and practices

IntroductionThe path toward enhancing laboratory safety requires a thorough understanding of the factors that influence the safety-related decision making of laboratory personnel. Method: We developed and administered a web-based survey to assess safety-related decision making of laboratory personnel of a government research organization. The survey included two brief discrete choice experiments (DCEs) that allowed for quantitative analysis of specific factors that potentially influence safety-related decisions and practices associated with two different hypothetical laboratory safety scenarios. One scenario related to reporting a laboratory spill, and the other scenario involved changing protective gloves between laboratory rooms. The survey also included several brief self-report measures of attitude, perception, and behavior related to safety practices. Results: Risk perception was the most influential factor in safety-related decision making in both scenarios. Potential negative consequences and effort associated with reporting an incident and the likelihood an incident was detected by others also affected reporting likelihood. Wearing gloves was also affected somewhat by perceived exposure risk, but not by other social or work-related factors included in the scenarios. Conclusions: The study demonstrated the promise of DCEs in quantifying the relative impact of several factors on safety-related choices of laboratory workers in two hypothetical but realistic scenarios. Participants were faced with hypothetical choice scenarios with realistic features instead of traditional scaling techniques that ask about attitudes and perceptions. The methods are suitable for addressing many occupational safety concerns in which workers face tradeoffs in their safety-related decisions and behavior. Practical Application: Safety-related decisions regarding laboratory practices such as incident reporting and use of PPE were influenced primarily by workers’ perceptions of risk of exposure and severity of risks to health and safety. This finding suggests the importance of providing laboratory workers with adequate and effective education and training on the hazards and risks associated with their work. DCEs are a promising research method for better understanding the relative influences of various personal, social, and organizational factors that shape laboratory safety decisions and practices. The information gained from DCEs may lead to more targeted training materials and interventions.     

考虑熟悉度与恐慌效应的人群引导疏散仿真算法研究*

为研究紧急情况下人群疏散行为特点,结合势场理论和多出口选择模型,建立考虑引导、熟悉度和恐慌效应的PFT-LMES人群疏散算法。通过搭建有障碍和无障碍2种室内场景,研究不同环境熟悉度以及行人密度下引导作用对人群疏散效果的影响。研究结果表明:引导作用能够提高疏散效率,平衡各出口利用率,引导效果随行人密度增加更加显著;总体疏散时间随引导强度的提高表现为先减小后增大;当接受引导的人数占总数的42%时,2出口各自选择人数与出口通行能力比例基本一致,引导效果达到最优;相对于环境熟悉度较高的人群,不熟悉环境行人的引导效果更为显著。研究结果可为大型场所的紧急高效疏散提供参考。     

考虑从众阈值和主体特征的人群疏散元胞自动机模型*

为研究从众行为对人群疏散效率的影响,建立考虑从众阈值和主体特征的人群疏散元胞自动机模型,通过引入从众阈值反映人群中不同个体的从众选择程度,耦合每个主体不同特征属性、不同人员认知能力导致的疏散从众选择差异,利用元胞自动机模拟人群运动状态及动态演化过程,探究从众行为对人群疏散过程的作用规律。结果表明:人群疏散中选择从众行为的人员比例与整体疏散效率存在对应关系,本模型原始工况下,当人群中约60%的人员做出从众选择时,疏散效率相对最优。     

考虑行为特征的人群疏散模拟研究*

为研究聚集活动中的行人在移动过程中所表现出的行为特征对人群整体移动过程的影响,通过实验观察与计算机仿真模拟的方法对人员移动进行研究。通过对实验视频的观察分析,在CTM模型基础上建立考虑人员跟随行为与保持距离行为特性的移动模型,并在简单无障碍和复杂多障碍的场景进行仿真实验。结果表明:模型能有效地描述行人的运动特征,而且拥挤程度和行人间排斥作用越大时行人移动越慢,整个疏散过程花费时间越长;障碍物的存在会影响人群在出口处的“成拱现象”,对理解人员的运动规律和防止拥挤踩踏事故具有一定理论和现实意义。