基于ACP方法的应急疏散系统研究

目前地铁、建筑及煤矿等人员密集空间突发事件频发,解决人员密集空间人员安全疏散是当前社会发展亟待解决的问题. 本文以地铁为例研究了基于ACP方法的平行应急疏散系统,该系统由应急疏散人工系统、计算实验 和平行执行三部分组成. 平行应急疏散系统不仅适用于地铁,还适用于建筑及煤矿等生活和工作空间应急疏散管理,可实现人员培训、疏散演练、疏散方案优化和评估等功能. 本文初步建立了地铁枢纽站平行应急疏散系统,并进行了两种典型场景的计算实验,实验结果表明平行应急疏散系统可有效提高应急疏散效率.     

Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

This paper presents two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSNs). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters. Each cluster is managed by a cluster head (CH) node. The first algorithm requires the CH nodes to be mobile: by dynamically varying the CH node positions, the algorithm is proved to converge to a specific partition of the mission area, the generalised Voronoi tessellation, in which the loads of the CH nodes are balanced. Conversely, if the CH nodes are fixed, a weighted Voronoi clustering approach is proposed with the same load-balancing objective: a reinforcement learning approach is used to dynamically vary the mission space partition by controlling the weights of the Voronoi regions. Numerical simulations are provided to validate the approaches.     

人群应急疏散可视仿真研究进展和问题

人群应急疏散可视仿真是用智能体来模拟具有自主感知、情绪和行为能力的人群个体,并采用3维可视的方式来直观呈现人群应急疏散情景,可以为制定人群应急预案提供形象直观的分析方法。本文从人群仿真数据的来源、人群导航模型的构建、人群行为模型、人群情绪感染、人群渲染5个方面概述目前研究的进展,然后从仿真模型的可验证性、人群疏散导航模型的构建、人与环境的物理模型、动物逃生实验与仿真、疏散中的社会行为表现以及人群情绪的可视计算6个角度讨论需要进一步研究的问题。针对需要深入研究的问题,指出借助于紧急事件的视频监控分析和虚拟人群情景的用户调查,有助于完善人群仿真模型。结合物理模型,可以更准确地描述人群应急疏散场景。开展动物逃生实验分析,有助于完善人群运动导航算法。建立人群社会行为模型,可以更详细描述疏散中人群行为的多样性。构建基于多通道感知的人群情绪感染计算方法,可以详尽描述情绪感染的过程。人群应急疏散行为的可视仿真研究在城市的安全管理方面具有重要的应用前景,但其研究仍存在很多亟待解决的问题,综合地运用多学科知识,完善实验手段是进一步推动研究的关键所在。     

基于粒子群神经网络算法的交通枢纽火灾应急疏散评价

在构建交通枢纽火灾应急疏散评价系统的基础上,建立用粒子群优化算法（PS0）和BP神经网络相结合的混合算法来进行综合评价的模型。该模型全面考虑影响应急疏散的各种因素,将定性分析和定量分析相结合,使评价结果更符合实际、更可靠。实例计算结果表明：PSO-BP方法简便、通用,其结果符合运营实际,能很好的反应交通枢纽火灾状况下应急疏散的实施效果。     

一种平面点集Voronoi 图的细分算法

Voronoi 图是计算几何中的重要概念之一,在计算机图形学、计算几何、 计算机辅助几何设计、有限元网格划分、机器人轨迹控制、模式识别、气象学和地质学研究 中得到广泛应用。借助于四叉树和区间算术,提出了一种新的构造平面点集Voronoi 图的细 分算法, 并且和经典的增量算法、栅格扩张法进行了比较, 结果显示新细分算法更为有效。 最重要的是细分算法原理简单,很容易编程实现。     

Efficient computation of evacuation routes on a three-dimensional geometric network

We consider a real-time emergency evacuation problem that seeks to compute a set of rapid evacuation routes in a building. Given a three-dimensional geometric structure of the evacuation network, an emergency evacuation route is a sequence of movements of people away from the threat or actual occurrence of a hazard (such as a fire, a hidden bomb) to a safe exit in the network. In such a network each room/crossing/exit in the building is designated as a node and the corridors/staircases/links between the rooms are edges. The evacuation times assigned to the edges are normally distributed random variables. This stochastic routing problem subject to deadline constraints is NP-hard. We provide a new pseudo-polynomial-time dynamic programming algorithm to solve this problem. Based on this algorithm, we construct two types of approximation algorithm, namely a fully polynomial-time approximation scheme providing “almost-optimal” solutions and a fully polynomial-time approximately feasible scheme yielding a best “almost feasible” solution. We present a case study and results of computational experiments to illustrate the working and efficacy of the proposed solution methods, respectively.     

基于ACP方法的高层建筑火灾中人员疏散策略研究

高层建筑结构规模巨大、人员众多,而疏散出口数量有限,如何有效安排疏散出口人员分布,提高疏散效率,是火灾应急管理的重要研究内容. 以人为中心的疏散系统是典型的复杂系统,具有难以真实实验分析的困难. 本文基于ACP（人工系统、计算实验、平行执行）方法,以智能体技术为核心建立了人工疏散系统,基于火灾场景,利用计算实验对疏散策略进行了验证、评估,给出了实际疏散系统与人工疏散系统的平行执行实现思想. 最后,通过案例验证了方法的可行性.     

基于深度时空Q网络的机器人疏散人群算法

针对目前人群疏散方法中机器人灵活性低、场景适应性有限与疏散效率低的问题,提出一种基于深度强化学习的机器人疏散人群算法。利用人机社会力模型模拟突发事件发生时的人群疏散状态,设计一种卷积神经网络结构提取人群疏散场景中复杂的空间特征,将传统的深度Q网络与长短期记忆网络相结合,解决机器人在学习中无法记忆长期时间信息的问题。实验结果表明,与现有基于人机社会力模型的机器人疏散人群方法相比,该算法能够提高在不同仿真场景中机器人疏散人群的效率,从而验证了算法的有效性。     

Scalable evacuation routing in a dynamic environment

In emergency management, tools are needed so we can take the appropriate action at different stages of an evacuation. Recent wildfires in California showed how quickly a natural disaster can affect a large geographical area. Natural disasters can create unpredicted traffic congestion or can temporarily block urban or rural roads. Evacuating a large area in an emergency situation is not possible without prior knowledge of the road network and the ability to generate an efficient evacuation plan. An ideal evacuation routing algorithm should be able to generate realistic and efficient routes for each evacuee from the source to the closest shelter. It should also be able to quickly update routes as the road network changes during the evacuation. For example, if a main road is blocked during a flood, the evacuation routing algorithm should update the plan based on this change in the road network. In this article major works in evacuation routing have been studied and a new algorithm is developed that is faster and can generate better evacuation routes. Additionally, it can quickly adjust the routes if changes in the road network are detected. The new algorithm's performance and running time are reported.     

Regional Evacuation Modeling System (REMS): A decision support system for emergency area evacuations

In this paper we present a decision support system being developed at the University of Florida. The system is a PC-based software package that is capable of testing different emergency scenarios due to hurricanes, chemical accidents or nuclear accidents. The underlying models are optimization models based on a regional transportation network. One of the most significant aspects of the software is its ability to handle time dimension of the problem explicitly. It is this aspect of REMS that makes incorporation at any time of road blockages due to the presence of extremely hazardous substances or inundation of roads due to accidents or flooding possible in evacuation scenarios tested. The software has the ability to animate the evacuation process in time and display the flow of traffic on the links of the transportation network in a time-lapsed manner with color codes. Additionally REMS is also capable to animate the progress of the plume exposure pathway of an extremely hazardous substances as it evolves in time onto the evacuation network and to display the dynamic vehicle flow in time simultaneously.     

A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations

Many computational tools for the simulation and design of emergency evacuation and egress are now available. However, due to the scarcity of human and social behavioral data, these computational tools rely on assumptions that have been found inconsistent or unrealistic. This paper presents a multi-agent based framework for simulating human and social behavior during emergency evacuation. A prototype system has been developed, which is able to demonstrate some emergent behaviors, such as competitive, queuing, and herding behaviors. For illustration, an example application of the system for safe egress design is provided.     

Optimizing the use of public transit system during no-notice evacuation of urban areas

This paper proposes a methodology that can be used to design plans for evacuating transit-dependent citizens during no-notice disasters. A mixed-integer linear program is proposed to model the problem of finding optimal evacuation routes. The objective of the problem is to minimize the total evacuation time and the number of casualties, simultaneously. A traffic simulation package is used to explicitly incorporate the traffic flow dynamics into our model in order to generate solutions which are consistent with the dynamics of traffic network. Due to the long running time of CPLEX, a Tabu search algorithm is designed that finds evacuation routes for transit vehicles. Computational experiments demonstrate that the solutions found are of high-quality. Numerical experiments are conducted using the transportation network of the city of Forth Worth, TX to illustrate the modeling procedure and solution approach.     

分布式虚拟环境的可扩充性研究

讨论了与分布式虚拟环境系统扩充性相关的两类技术,一类是通过简单的硬件并行来实现系统扩容,典型的例子是多服务器并行分区技术。这类技术能够支持大规模的分布式虚拟环境(DVE)系统,缺点是费用太高;另一类技术试图通过减少服务器通信负载来实现系统容量的增加,主要包括感兴趣区域（AOI）技术,DR算法和组播技术。此外,还讨论了一些复合方法：如基于AOI的组播技术等。网络负载的减少意味着相同的硬件条件可以支持更大规模的客户,从而有助于解决大规模DVE系统的扩充性问题。     

基于改进Raft共识算法和PBFT共识算法的双层共识算法

针对目前应用于联盟链中的实用拜占庭（PBFT）共识算法可扩展性不足、通信开销增长过大、难以适用于大规模网络节点环境等问题,提出了一种基于改进Raft共识算法和PBFT共识算法的双层共识算法（DL_RBFT）。首先将区块链中的节点分成若干小组,组成下层共识网络,然后小组的组长再构成上层共识网络,形成一个双层共识网络结构;在下层共识网络的小组内部使用引入监督机制和声誉机制来改进Raft共识算法,在初始组长的选举流程引入了蚁群算法,使选举效率始终维持在较高水平;在上层共识网络中,使用PBFT共识算法进行共识。改进后的Raft共识算法具备了抗拜占庭节点攻击的能力,提升了算法的安全性。实验结果分析表明,相较于传统的PBFT共识算法,在100个节点的情况下,DL_RBFT将共识时延降低了两个数量级,吞吐量也提升了一个数量级,与其余改进算法相比也有着明显优势。因此DL_RBFT共识算法拥有良好的可扩展性,可以广泛应用于联盟链的各种场景中。     

A virtual service placement approach based on improved quantum genetic algorithm

Despite the critical role that middleboxes play in introducing new network functionality, management and innovation of them are still severe challenges for network operators, since traditional middleboxes based on hardware lack service flexibility and scalability. Recently, though new networking technologies, such as network function virtualization (NFV) and software-defined networking (SDN), are considered as very promising drivers to design cost-efficient middlebox service architectures, how to guarantee transmission efficiency has drawn little attention under the condition of adding virtual service process for traffic. Therefore, we focus on the service deployment problem to reduce the transport delay in the network with a combination of NFV and SDN. First, a framework is designed for service placement decision, and an integer linear programming model is proposed to resolve the service placement and minimize the network transport delay. Then a heuristic solution is designed based on the improved quantum genetic algorithm. Experimental results show that our proposed method can calculate automatically the optimal placement schemes. Our scheme can achieve lower overall transport delay for a network compared with other schemes and reduce 30% of the average traffic transport delay compared with the random placement scheme.     

结合最短路径改进的社会力人群疏散仿真模型

社会力模型广泛应用于人群疏散仿真,针对该模型在仿真过程中存在行人停滞不前、无法通过非凸边形障碍物和疏散路径与行人实际选择的路径不相符等问题,提出了一种社会力改进模型。该模型基于场景中的障碍物生成路径节点,利用这些节点生成无向图,同时考虑了节点的安全系数和拥挤系数对节点通行性的影响生成最短疏散路径。通过改进后的社会力模型进行了多种场景的仿真实验,实验结果显示行人在复杂障碍物场景中能有效绕过障碍物,生成合理的疏散路径,表明该模型有效改善社会力模型,使人群疏散仿真更加真实。     

Simulation-based evaluation of evacuation effectiveness using driving behavior sensitivity analysis

Worldwide, man-made or natural phenomena occasionally occur that create emergency conditions and require the evacuation of areas of different sizes and characteristics. Drivers’ behavior becomes a very important factor for the evacuation operations. This paper provides an analytical study of the effectiveness of evacuation according to drivers’ behavior, using the sensitivity analysis method. Collecting real-time data about this factor is a difficult to impossible task for large scale cases; therefore, traffic simulation is the most appropriate method for analysis. Our goal is to investigate how drivers’ aggressiveness affects the evacuation effectiveness. In this case, we used the AIMSUN traffic simulation model; the parameters of the driver behavior models are chosen through all-at-once sensitivity analysis of the parameters. This model is applied to different demand scenarios for well-defined parameters’ value ranges. This investigation produces estimated ranges of the evacuation duration and the number of evacuated people, both for a baseline “do-nothing” scenario, as well as the outcome of improvement actions. The sensitivity analysis results suggest that evacuation time can be significantly reduced by reversing the most congested links; furthermore, the use of a bus fleet would allow many more people to evacuate the danger zone timely, albeit with a small increase in minimum evacuation time. This methodology could be applicable to other emergency response scenarios, as it obviates the need for real-time data.     

SENSITIZING SOCIAL AGENTS FOR VIRTUAL TRAINING

ABSTRACT

Virtual training allows the learning and rehearsal of implicit cues, e.g., trustworthy leading action in an emergency evacuation, that cannot be easily understood through merely reading about situations, while mitigating the danger and expense of live rehearsals. We have focused our efforts on designing social agents that can engage in and help to train humans to generate the trustworthy behaviors that help to ensure a successful evacuation. Drawing upon social science research and using a “role-reversal method,” we successfully constructed agents that can perceive trustworthiness as humans do. The agents first collect human responses to their own nonverbal cues in controlled experimental training scenarios. Using these results, we obtain optimal parameters for nonverbal cues of trustworthiness, and then can use them to guide agents who evaluate human performance in the same training scenarios. The method enables us to convert social psychological findings into computational mechanisms.     

Group Elevator Scheduling With Advance Information for Normal and Emergency Modes

Group elevator scheduling has long been recognized as an important problem for building transportation efficiency, since unsatisfactory elevator service is one of the major complaints of building tenants. It now has a new significance driven by homeland security concerns. The problem, however, is difficult because of complicated elevator dynamics, uncertain traffic in various patterns, and the combinatorial nature of discrete optimization. With the advent of technologies, one important trend is to use advance information collected from devices such as destination entry, radio frequency identification, and sensor networks to reduce uncertainties and improve efficiency. How to effectively utilize such information remains an open and challenging issue. This paper presents the optimized scheduling of a group of elevators with destination entry and future traffic information for normal operations and coordinated emergency evacuation. Key problem characteristics are abstracted to establish a two-level separable formulation. A decomposition and coordination approach is then developed, where subproblems are solved by ordinal optimization-based local search, and top ranked nodes are selectively optimized by using dynamic programming. The approach is then extended to handle up-peak with little or no future traffic information, elevator parking for low intensity traffic, and coordinated emergency evacuation. Numerical testing results demonstrate near-optimal solution quality, computational efficiency, the value of future traffic information, and the potential of using elevators for emergency evacuation.     

一种新的适用于车载Ad-hoc网络的位置服务机制

提出了一种新的适用于车载Ad-hoc网络的位置服务机制ILSV(道路交叉路口位置服务),该机制充分利用了车载网络环境的特点如存在十字路口,受交通规则约束等,将网络中节点的位置信息分布地存储在位于十字路口的位置服务器上。在大规模城市仿真场景下的仿真结果表明:通过与GLS和HLS相比,ILSV获得了相当高的查询成功率,并且在网络半径很大的情况下具有较优的可缩放性。