乘客行為系統(tǒng)在地鐵仿真培訓(xùn)中的研究與應(yīng)用
發(fā)布時(shí)間：2018-09-07 16:03
【摘要】：人群仿真是利用人工智能、計(jì)算機(jī)圖形處理等技術(shù),建立人群行為模型,在虛擬環(huán)境中模擬人群行為,現(xiàn)已廣泛用于公共交通仿真、大型場所人流分析、影視游戲作品等領(lǐng)域。乘客智能行為仿真是利用人群仿真技術(shù),模擬乘客正常狀況下進(jìn)出站、乘離車等過程及緊急狀況下疏散逃生過程。現(xiàn)有的乘客行為仿真大多集中于應(yīng)急情況下公共交通場所的通過能力仿真,多采取離線、非實(shí)時(shí)計(jì)算方式進(jìn)行,并不能很好地應(yīng)用到對實(shí)時(shí)性要求很高的地鐵仿真培訓(xùn)系統(tǒng)中。本文針地鐵仿真培訓(xùn)系統(tǒng)對于乘客行為仿真在真實(shí)性、沉浸感、實(shí)時(shí)計(jì)算能力等方面的要求,提出一種基于地鐵站臺場景下的乘客智能行為模擬方案,建立乘客行為模型,實(shí)現(xiàn)單線路多車運(yùn)行及多線路同時(shí)發(fā)車狀況下小客流、中客流、大客流的乘客行為模擬,并應(yīng)用到地鐵仿真培訓(xùn)系統(tǒng)中。本文首先對人群感知模型進(jìn)行研究。個(gè)體的感知由聽覺、視覺、區(qū)域感知三個(gè)部分組成,為了表現(xiàn)個(gè)體在現(xiàn)實(shí)環(huán)境中的感知過程,本文建立由這三種感知方式共同組成的人群感知模型,對不同的感知信息分級處理,體現(xiàn)人群在不同感知效果下做出反應(yīng)的差異性,較為真實(shí)地反應(yīng)人類感知的特點(diǎn)。其次,本文研究了全局最優(yōu)靜態(tài)路徑搜索算法和動(dòng)態(tài)障礙避碰算法。采用基于導(dǎo)航網(wǎng)格的A*算法,實(shí)現(xiàn)個(gè)體最優(yōu)靜態(tài)路徑搜索功能。在地鐵站臺場景中動(dòng)態(tài)障礙物主要是其他乘客,根據(jù)該情況,提出一種基于權(quán)重因子的RVO避碰算法,實(shí)現(xiàn)乘客在運(yùn)動(dòng)過程對動(dòng)態(tài)障礙物避碰的功能,以及遇到隊(duì)列時(shí)的穿插和繞行兩種行為。第三,本文研究了各種人群行為模型。對于基于全局和基于個(gè)體的人群模型,進(jìn)行分析比較,其中基于全局的模型通過組、群將人群分類,可以模擬大規(guī)模的人群,但對個(gè)體的差異性表現(xiàn)不足。該模型廣泛應(yīng)用于公共場所大規(guī)模人群疏散及影視游戲作品中數(shù)萬級別的人群效果演示。基于個(gè)體的人群模型中,個(gè)體具有特定的屬性,每個(gè)個(gè)體的行為都單獨(dú)計(jì)算,著重體現(xiàn)個(gè)體在狀況處理和行為選擇上的差異性,可用于復(fù)雜環(huán)境及多樣化行為模擬。根據(jù)需求,提出基于行為特征、運(yùn)動(dòng)規(guī)律的乘客仿真模型,可以實(shí)現(xiàn)在不同目的及不同客流量下的人群行為模擬。第四,為了更真實(shí)地反應(yīng)現(xiàn)實(shí)場景中乘客的行為,通過實(shí)地調(diào)查,對不同站臺、不同時(shí)段、不同客流量下的乘客行為進(jìn)行分析研究,總結(jié)乘客在行為選擇過程中的規(guī)律,對影響乘客行為的因子進(jìn)行量化分析,得到由隨機(jī)因素、個(gè)體特征、環(huán)境信息組成的乘客行為選擇公式,并提出基于該類規(guī)則和行為特征的乘客智能行為模擬方案。最后,本文研究了乘客緊急狀況下的疏散行為。根據(jù)恐慌狀態(tài)下的人群疏散特征,進(jìn)行疏散模型驗(yàn)證,并在避碰計(jì)算中添加停止規(guī)則,解決了由于人群擁擠導(dǎo)致的模型抖動(dòng)和身體穿插的問題。
[Abstract]:Crowd simulation is to use artificial intelligence, computer graphics processing technology, to establish crowd behavior model, in the virtual environment to simulate crowd behavior, has been widely used in public transport simulation, large-scale place flow analysis, video games and other fields. Passenger intelligent behavior simulation is to use crowd simulation technology to simulate the process of passengers entering and leaving station under normal condition and evacuation process in emergency. Most of the existing passenger behavior simulation is focused on the transit capacity simulation of public transport places in emergency situations, mostly using off-line, non-real-time computing method, which can not be well applied to the subway simulation training system with high real-time requirements. This paper presents a passenger intelligent behavior simulation scheme based on subway platform scene, which is based on the requirement of authenticity, immersion and real-time computing ability of passenger behavior simulation system in subway simulation training system, and establishes passenger behavior model. The passenger behavior simulation of small, medium and large passenger flow is realized under the condition of single line multi-vehicle operation and multi-line simultaneous departure, and it is applied to the subway simulation training system. This paper first studies the crowd perception model. Individual perception consists of three parts: hearing, vision and region perception. In order to express the perception process of individual in the real environment, this paper establishes a crowd perception model which is composed of these three ways of perception. The classification of different perceptual information reflects the difference of the response of the crowd under different perceptual effects, and reflects the characteristics of human perception more truthfully. Secondly, the global optimal static path search algorithm and dynamic obstacle avoidance algorithm are studied. The algorithm A * based on navigation grid is used to realize the individual optimal static path search function. In the scene of subway platform, dynamic obstacles are mainly other passengers. According to this situation, a RVO collision avoidance algorithm based on weight factor is proposed to realize the function of passengers avoiding dynamic obstacles in the course of motion. As well as encounter queue interlude and detour two kinds of behavior. Thirdly, this paper studies various crowd behavior models. For the global and individual-based population models, the analysis and comparison, which based on the global model through groups, groups will be classified, can simulate large populations, but the differences of individual performance is not enough. The model is widely used in mass evacuation of public places and tens of thousands of crowd effect demonstrations in video games. In the individual-based population model, the individual has a specific attribute, and each individual's behavior is calculated separately, which focuses on the difference of the individual's situation treatment and behavior choice, and can be used to simulate the complex environment and diversified behavior. According to the demand, a passenger simulation model based on behavior characteristics and motion rules is proposed, which can be used to simulate crowd behavior under different objectives and different passenger flow. Fourthly, in order to reflect the behavior of the passengers in the real scene more realistically, through the field investigation, the passenger behavior under different platforms, different time periods and different passenger flow is analyzed and studied, and the rules of passengers' behavior selection are summarized. Based on the quantitative analysis of the factors affecting passenger behavior, a passenger behavior selection formula composed of random factors, individual characteristics and environmental information is obtained, and a passenger intelligent behavior simulation scheme based on this kind of rules and behavior characteristics is proposed. Finally, the evacuation behavior of passengers in an emergency is studied. According to the characteristics of crowd evacuation in panic state, the evacuation model is verified and the stopping rule is added to the calculation of collision avoidance, which solves the problem of model jitter and body insertion caused by crowd congestion.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.9
本文編號：2228735
[Abstract]:Crowd simulation is to use artificial intelligence, computer graphics processing technology, to establish crowd behavior model, in the virtual environment to simulate crowd behavior, has been widely used in public transport simulation, large-scale place flow analysis, video games and other fields. Passenger intelligent behavior simulation is to use crowd simulation technology to simulate the process of passengers entering and leaving station under normal condition and evacuation process in emergency. Most of the existing passenger behavior simulation is focused on the transit capacity simulation of public transport places in emergency situations, mostly using off-line, non-real-time computing method, which can not be well applied to the subway simulation training system with high real-time requirements. This paper presents a passenger intelligent behavior simulation scheme based on subway platform scene, which is based on the requirement of authenticity, immersion and real-time computing ability of passenger behavior simulation system in subway simulation training system, and establishes passenger behavior model. The passenger behavior simulation of small, medium and large passenger flow is realized under the condition of single line multi-vehicle operation and multi-line simultaneous departure, and it is applied to the subway simulation training system. This paper first studies the crowd perception model. Individual perception consists of three parts: hearing, vision and region perception. In order to express the perception process of individual in the real environment, this paper establishes a crowd perception model which is composed of these three ways of perception. The classification of different perceptual information reflects the difference of the response of the crowd under different perceptual effects, and reflects the characteristics of human perception more truthfully. Secondly, the global optimal static path search algorithm and dynamic obstacle avoidance algorithm are studied. The algorithm A * based on navigation grid is used to realize the individual optimal static path search function. In the scene of subway platform, dynamic obstacles are mainly other passengers. According to this situation, a RVO collision avoidance algorithm based on weight factor is proposed to realize the function of passengers avoiding dynamic obstacles in the course of motion. As well as encounter queue interlude and detour two kinds of behavior. Thirdly, this paper studies various crowd behavior models. For the global and individual-based population models, the analysis and comparison, which based on the global model through groups, groups will be classified, can simulate large populations, but the differences of individual performance is not enough. The model is widely used in mass evacuation of public places and tens of thousands of crowd effect demonstrations in video games. In the individual-based population model, the individual has a specific attribute, and each individual's behavior is calculated separately, which focuses on the difference of the individual's situation treatment and behavior choice, and can be used to simulate the complex environment and diversified behavior. According to the demand, a passenger simulation model based on behavior characteristics and motion rules is proposed, which can be used to simulate crowd behavior under different objectives and different passenger flow. Fourthly, in order to reflect the behavior of the passengers in the real scene more realistically, through the field investigation, the passenger behavior under different platforms, different time periods and different passenger flow is analyzed and studied, and the rules of passengers' behavior selection are summarized. Based on the quantitative analysis of the factors affecting passenger behavior, a passenger behavior selection formula composed of random factors, individual characteristics and environmental information is obtained, and a passenger intelligent behavior simulation scheme based on this kind of rules and behavior characteristics is proposed. Finally, the evacuation behavior of passengers in an emergency is studied. According to the characteristics of crowd evacuation in panic state, the evacuation model is verified and the stopping rule is added to the calculation of collision avoidance, which solves the problem of model jitter and body insertion caused by crowd congestion.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.9
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