面向人群的并行多目标疏散模型研究
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摘要
在大规模人群疏散过程中,由于疏散对象所处空间位置各异,因此对应急情况下各自时空路径的判断和选择也各不相同,如何优化分配大规模疏散对象各自的时空路径,从整体上提高系统的应急性能,是一个非常重要的科学问题。在这方面,传统的交通流分配模型往往集中于针对单一疏散指标的整体上的路径分配效果,并没有兼顾到系统效率和疏散对象的个体疏散需求。本文通过并行时空协同疏散的多目标路径分配模型,围绕紧急情况下大型体育场及建筑物与路网集成环境下人群疏散行为的时空规律、时间效率以及并行时空协同疏散的多目标路径分配模型进行研究,主要研究内容与结果包含以下几个方面：
1.建立了大型体育场典型应急下人群疏散性能多目标优化模型。考虑到大型体育场应急情况下人员的最快撤离心理,本文围绕大型体育场及其周边路网环境下人群疏散问题,以路网可达区域和建筑物结构为依据建立了混合疏散网络,设计了疏散时间最短、最大拥挤程度最低和总路径长度最短3个优化指标,并建立了兼顾系统效率与个体需求的多目标时空路径演化模型。传统的疏散优化方法往往只注重单方面的优化结果,例如整体上的疏散效能,而对微观层面上的行人之间的时空冲突考虑不足,在用户均衡和系统均衡的多项疏散需求的有效整合上存在着严重缺陷。本文基于演化算法理论,着重研究大型体育场典型应急疏散中的群体搜索多目标疏散路径演化模型,并以此为基础,从人的疏散路径角度来构建体育场典型应急情况下的人群疏散性能评估体系,使之兼顾个体的路径特征和系统的流量特征,实现疏散性能的完整刻画。
2.提出了基于多目标HEMO模型的CPU+GPU异构并行体育场疏散撤离模型以及相应的IPHEMO算法。对于大规模人员疏散的路径分配,CPU单线程算法在时间效率方面存在严重的瓶颈,鉴于此,本文提出了按粒度划分计算任务,从而利用CPU处理复杂逻辑运算,同时采用CUDA GPU作为协处理器处理大量数据运算的异构并行疏散撤离模型,以提高演化模型运用于体育场疏散撤离问题上的时间性能。与此同时,对所提出的异构并行疏散撤离模型的GPU带宽传输模式、演化算子并行化方面进行了改进,实验结果表明：改进的异构架构在时间性能、带宽占用上比改进前的异构架构有相当程度的提高。此外,相比于CPU单线程算法,本文提出的IPHEMO算法在体育场疏散路径分配的总体时间性能、单位时间内的搜索性能上也有较大的提升,验证了所提出的异构模型在计算时间、收敛性、资源占用上的可行性和有效性。
3.通过对多目标疏散路径分配算法的通用演化结构的分析与提炼,建立了能够抽象描述多种不同类型多目标优化算法的软件工程设计模式。该模式面向软件工程泛型抽象与提取设计模式的要求,针对多类演化算法进行了统一接口设计,能够具体应用于体育场人群疏散撤离问题。与常规策略模式不同,该模式定义了不同种类的体育场疏散路径分配多目标优化算法的共同接口,以及不同的数据抽象对象,能够为建立疏散路径分配算法库提供一定的理论指导。本文对疏散算法抽象设计原则的建立与具体实现进行了有益的尝试,同时也探索了应用于现实优化问题的设计模式以及算法实现方法。
4.建立了应用于体育场疏散路径分配的开源多目标演化算法库。该算法库基于C++Ox标准和演化算法理论跨平台构建,能够进行最优算法／参数组合的统计分析检验同时自动生成实验结果的统计、绘图存档,其中包含的并行化实验任务运行功能节省了大批量实验任务的运行、统计、分析对比时间,减少甚至消除了人为实验误差的可能,为疏散撤离算法大规模实验的自动化和对比检验提供了基于实验理论支撑的新集成工具与有效方案。算法库在武汉沌口体育场多目标疏散路径分配方案中的实验结果与数据分析说明：算法库中的IPHEMO算法不仅能够同时提供一组高效、折衷、安全的时空路径疏散方案,并且其收敛性、多样性上均优于NSGA2算法,同时能够提供比NSGA2算法更好的疏散过程平均拥挤程度、更低的节点平均流量,为完善人群疏散模型的实证研究进行了有益的探索。
During large-scale evacuation process, different locations of the evacuating object create distinct spatial-temporal route selection circumstances and judgments', thus optimizing the allocation of large-scale spatial-temporal routes of respective evacuating objects so as to improve the emergency performance of the overall system, is a crucial scientific problem. Most of the existed evacuation researches model solely on pedestrian or vehicle object and generally focus only on single-objective optimization. They lack the characterization and depiction of evacuation process in the angle of pedestrian spatial-temporal evacuation, which can hardly effectively analyze the whole emergency process from such aspects as the spatial-temporal rationality of routing selection for each evacuating object, the feasibility and performance in the whole system for all selected routes. Consequently, it create an adverse condition to generate a scientific evacuation solution to carry out systematic emergency polices and contingency measures. This dissertation focus on multi-objective evacuation model under emergency situation in large public places, the spatial-temporal transmission law of pedestrian behaviors, traffic features and multi-objective spatial-temporal evacuation routing algorithm in the environment involved buildings and road net. Major research contents in this dissertation include:
Taking into account the individuals'psychology of panic and evacuating at the fastest rate under emergency situation in a large stadium, this dissertation, centering on problems of pedestrian evacuation in a large stadium and its surrounding road network, create three evaluation indexes including the shortest evacuation time, the lowest of the maximum crowding degree and the shortest total path length. This dissertation develops a multi-objective evacuation model, and by combined with evolution optimization theory, multi-objective evacuation route assignment optimization algorithm based on HEMO model is also presented. Compared to NSGA2algorithm, the experimental results show that the proposed multi-objective evacuation model and its associated heterogeneous multi-objective evacuation route assignment algorithm can provide a spatial-temporal evacuation solution with better convergence, diversity and lower crowding degree, which is a beneficial attempt for the perfection of the pedestrian evacuation model.
To overcome the time efficiency disadvantage of single thread CPU HEMO algorithms in large scale pedestrian evacuation route assignment, a heterogeneous evacuation model, which utilizes CPU to handle complicated logic operation while adopts CUDA GPU as co-processor to handle large amounts of parallel data operation is designed to provide an alternative theoretical analysis method and technology for both route assignment research and evacuation models which based on heterogeneous computing and MOP. Based on HEMO, the multi-population cooperative evolution method offers a new way to improve the diversity of route assignment, and a feasible and efficient implementation for this model is provided by an explicit parallelization of evolutionary operators and its multi-population computing pattern.
Considering the shortcomings of present multi-object evolutionary algorithm in large scale stadium evacuation and satisfying the demands of generic abstraction to extract design pattern, a new software engineering design pattern aiming at stadium evacuation and establishing interface for multi-category evolutionary algorithms is proposed in this dissertation. Different from traditional strategy pattern, the proposed pattern defines a common interface for various multi-object route optimizations in stadium evacuation as well as large amounts of data abstraction objects, which provides system abstraction and theory guidance for building evacuation route distribution library. Therefore, this dissertation does a beneficial attempt in setting up and realizing evacuation algorithm abstract rules whilst designing paradigm for practical optimization problem. Algorithm implementation methods under this pattern are also explored.
By studying the deficiencies of multi-objective evolution algorithms in addressing stadium evacuation of large-scale pedestrian and the demands of generic programming and extracting design patterns in software engineering, this dissertation create an open-source evolution algorithm library StupidAlgorithm which is based on the proposed design pattern. The proposed library is cross-platform, C++Ox conformant, and provides the ability to automatically generate basic statistical files and plotting outputs of batched experimental results for further optimal parameter combinations comparison and analysis.
1.建立了大型体育场典型应急下人群疏散性能多目标优化模型。考虑到大型体育场应急情况下人员的最快撤离心理,本文围绕大型体育场及其周边路网环境下人群疏散问题,以路网可达区域和建筑物结构为依据建立了混合疏散网络,设计了疏散时间最短、最大拥挤程度最低和总路径长度最短3个优化指标,并建立了兼顾系统效率与个体需求的多目标时空路径演化模型。传统的疏散优化方法往往只注重单方面的优化结果,例如整体上的疏散效能,而对微观层面上的行人之间的时空冲突考虑不足,在用户均衡和系统均衡的多项疏散需求的有效整合上存在着严重缺陷。本文基于演化算法理论,着重研究大型体育场典型应急疏散中的群体搜索多目标疏散路径演化模型,并以此为基础,从人的疏散路径角度来构建体育场典型应急情况下的人群疏散性能评估体系,使之兼顾个体的路径特征和系统的流量特征,实现疏散性能的完整刻画。
2.提出了基于多目标HEMO模型的CPU+GPU异构并行体育场疏散撤离模型以及相应的IPHEMO算法。对于大规模人员疏散的路径分配,CPU单线程算法在时间效率方面存在严重的瓶颈,鉴于此,本文提出了按粒度划分计算任务,从而利用CPU处理复杂逻辑运算,同时采用CUDA GPU作为协处理器处理大量数据运算的异构并行疏散撤离模型,以提高演化模型运用于体育场疏散撤离问题上的时间性能。与此同时,对所提出的异构并行疏散撤离模型的GPU带宽传输模式、演化算子并行化方面进行了改进,实验结果表明：改进的异构架构在时间性能、带宽占用上比改进前的异构架构有相当程度的提高。此外,相比于CPU单线程算法,本文提出的IPHEMO算法在体育场疏散路径分配的总体时间性能、单位时间内的搜索性能上也有较大的提升,验证了所提出的异构模型在计算时间、收敛性、资源占用上的可行性和有效性。
3.通过对多目标疏散路径分配算法的通用演化结构的分析与提炼,建立了能够抽象描述多种不同类型多目标优化算法的软件工程设计模式。该模式面向软件工程泛型抽象与提取设计模式的要求,针对多类演化算法进行了统一接口设计,能够具体应用于体育场人群疏散撤离问题。与常规策略模式不同,该模式定义了不同种类的体育场疏散路径分配多目标优化算法的共同接口,以及不同的数据抽象对象,能够为建立疏散路径分配算法库提供一定的理论指导。本文对疏散算法抽象设计原则的建立与具体实现进行了有益的尝试,同时也探索了应用于现实优化问题的设计模式以及算法实现方法。
4.建立了应用于体育场疏散路径分配的开源多目标演化算法库。该算法库基于C++Ox标准和演化算法理论跨平台构建,能够进行最优算法／参数组合的统计分析检验同时自动生成实验结果的统计、绘图存档,其中包含的并行化实验任务运行功能节省了大批量实验任务的运行、统计、分析对比时间,减少甚至消除了人为实验误差的可能,为疏散撤离算法大规模实验的自动化和对比检验提供了基于实验理论支撑的新集成工具与有效方案。算法库在武汉沌口体育场多目标疏散路径分配方案中的实验结果与数据分析说明：算法库中的IPHEMO算法不仅能够同时提供一组高效、折衷、安全的时空路径疏散方案,并且其收敛性、多样性上均优于NSGA2算法,同时能够提供比NSGA2算法更好的疏散过程平均拥挤程度、更低的节点平均流量,为完善人群疏散模型的实证研究进行了有益的探索。
During large-scale evacuation process, different locations of the evacuating object create distinct spatial-temporal route selection circumstances and judgments', thus optimizing the allocation of large-scale spatial-temporal routes of respective evacuating objects so as to improve the emergency performance of the overall system, is a crucial scientific problem. Most of the existed evacuation researches model solely on pedestrian or vehicle object and generally focus only on single-objective optimization. They lack the characterization and depiction of evacuation process in the angle of pedestrian spatial-temporal evacuation, which can hardly effectively analyze the whole emergency process from such aspects as the spatial-temporal rationality of routing selection for each evacuating object, the feasibility and performance in the whole system for all selected routes. Consequently, it create an adverse condition to generate a scientific evacuation solution to carry out systematic emergency polices and contingency measures. This dissertation focus on multi-objective evacuation model under emergency situation in large public places, the spatial-temporal transmission law of pedestrian behaviors, traffic features and multi-objective spatial-temporal evacuation routing algorithm in the environment involved buildings and road net. Major research contents in this dissertation include:
Taking into account the individuals'psychology of panic and evacuating at the fastest rate under emergency situation in a large stadium, this dissertation, centering on problems of pedestrian evacuation in a large stadium and its surrounding road network, create three evaluation indexes including the shortest evacuation time, the lowest of the maximum crowding degree and the shortest total path length. This dissertation develops a multi-objective evacuation model, and by combined with evolution optimization theory, multi-objective evacuation route assignment optimization algorithm based on HEMO model is also presented. Compared to NSGA2algorithm, the experimental results show that the proposed multi-objective evacuation model and its associated heterogeneous multi-objective evacuation route assignment algorithm can provide a spatial-temporal evacuation solution with better convergence, diversity and lower crowding degree, which is a beneficial attempt for the perfection of the pedestrian evacuation model.
To overcome the time efficiency disadvantage of single thread CPU HEMO algorithms in large scale pedestrian evacuation route assignment, a heterogeneous evacuation model, which utilizes CPU to handle complicated logic operation while adopts CUDA GPU as co-processor to handle large amounts of parallel data operation is designed to provide an alternative theoretical analysis method and technology for both route assignment research and evacuation models which based on heterogeneous computing and MOP. Based on HEMO, the multi-population cooperative evolution method offers a new way to improve the diversity of route assignment, and a feasible and efficient implementation for this model is provided by an explicit parallelization of evolutionary operators and its multi-population computing pattern.
Considering the shortcomings of present multi-object evolutionary algorithm in large scale stadium evacuation and satisfying the demands of generic abstraction to extract design pattern, a new software engineering design pattern aiming at stadium evacuation and establishing interface for multi-category evolutionary algorithms is proposed in this dissertation. Different from traditional strategy pattern, the proposed pattern defines a common interface for various multi-object route optimizations in stadium evacuation as well as large amounts of data abstraction objects, which provides system abstraction and theory guidance for building evacuation route distribution library. Therefore, this dissertation does a beneficial attempt in setting up and realizing evacuation algorithm abstract rules whilst designing paradigm for practical optimization problem. Algorithm implementation methods under this pattern are also explored.
By studying the deficiencies of multi-objective evolution algorithms in addressing stadium evacuation of large-scale pedestrian and the demands of generic programming and extracting design patterns in software engineering, this dissertation create an open-source evolution algorithm library StupidAlgorithm which is based on the proposed design pattern. The proposed library is cross-platform, C++Ox conformant, and provides the ability to automatically generate basic statistical files and plotting outputs of batched experimental results for further optimal parameter combinations comparison and analysis.
引文
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