駕駛?cè)俗⒁夥稚顟B(tài)建模與交通仿真研究
發(fā)布時(shí)間：2018-05-05 20:48
本文選題：交通仿真 + 駕駛?cè)四Ｐ?/strong>��； 參考：《長(zhǎng)安大學(xué)》2017年碩士論文
【摘要】：作為“人-車-路”復(fù)雜系統(tǒng)的重要參與者,駕駛?cè)说母兄�、判斷、操縱都會(huì)對(duì)其駕駛狀態(tài)產(chǎn)生影響,造成交通擁堵乃至誘發(fā)交通事故,駕駛?cè)艘蛩貙?duì)于交通運(yùn)行至關(guān)重要。以往交通仿真模型對(duì)于駕駛?cè)诵睦頎顟B(tài)差異缺乏表現(xiàn),論文分別基于STCA元胞自動(dòng)機(jī)與Multi-Agent兩種交通仿真方法,以駕駛?cè)俗⒁夥稚槔剿餍睦頎顟B(tài)變化情況下的駕駛?cè)四Ｐ蜆?gòu)建方法,并對(duì)一定比例駕駛?cè)顺霈F(xiàn)注意分散時(shí)的交通流運(yùn)行狀態(tài)進(jìn)行仿真。為研究車內(nèi)次任務(wù)條件下駕駛?cè)藸顟B(tài)變化及其對(duì)交通流造成的影響,首先,論文基于ACT-R認(rèn)知結(jié)構(gòu)與Distract-R平臺(tái)對(duì)駕駛?cè)俗⒁夥稚顟B(tài)進(jìn)行了認(rèn)知模擬,獲得了執(zhí)行4類不同次任務(wù)時(shí)的時(shí)間消耗與注意分散比,并以該數(shù)據(jù)作為駕駛?cè)俗⒁夥稚顟B(tài)庫(kù),接著,在STCA元胞自動(dòng)機(jī)交通流模型的基礎(chǔ)上,建立了考慮車內(nèi)次任務(wù)影響的交通流模型,該模型修正了原有的元胞自動(dòng)機(jī)減速規(guī)則,且能夠通過(guò)調(diào)用駕駛?cè)俗⒁夥稚顟B(tài)庫(kù)獲取部分模型參數(shù),并在Matlab中進(jìn)行交通流仿真實(shí)驗(yàn),模擬0、10%、20%比例的駕駛?cè)嗽谛旭傊袌?zhí)行車內(nèi)次任務(wù)時(shí)的交通流狀況,次任務(wù)類型在上述4種類型中隨機(jī)抽取;另外,論文基于Multi-Agent仿真方法,依次建立路段Agent、車輛Agent及信號(hào)燈Agent,并為各個(gè)Agent設(shè)計(jì)運(yùn)行和交互規(guī)則,利用NetLogo平臺(tái)編程建立Multi-Agent考慮駕駛?cè)俗⒁夥稚顟B(tài)的交通仿真系統(tǒng)并進(jìn)行仿真實(shí)驗(yàn),從而實(shí)現(xiàn)不同實(shí)驗(yàn)結(jié)果相互驗(yàn)證及補(bǔ)充。實(shí)驗(yàn)數(shù)據(jù)表明,車內(nèi)次任務(wù)會(huì)對(duì)交通流造成明顯影響:基于元胞自動(dòng)機(jī)交通流仿真實(shí)驗(yàn)的結(jié)果數(shù)據(jù)顯示,當(dāng)10%、20%的駕駛?cè)藞?zhí)行次任務(wù)時(shí),最大流量降低21.4%、36.2%,最大車速約降低11.1%、22.2%,同時(shí),隨著執(zhí)行次任務(wù)駕駛?cè)吮壤脑黾?流量和車速峰值所對(duì)應(yīng)的密度逐漸減小;基于Multi-Agent編程方法在路段模型仿真實(shí)驗(yàn)中的結(jié)果顯示當(dāng)10%、20%的駕駛?cè)藞?zhí)行次任務(wù)時(shí),最大流量降低20.3%、37.6%,最大車速約降低15.6%、29.3%,在路網(wǎng)模型仿真實(shí)驗(yàn)中,車輛集聚峰值變大,并且消散趨勢(shì)變緩,同時(shí)交叉口處車輛排隊(duì)現(xiàn)象加劇。上述實(shí)驗(yàn)結(jié)果表明:兩種仿真方法獲得的結(jié)果基本一致,符合以往文獻(xiàn)中的經(jīng)典趨勢(shì),能在一定程度上反映駕駛?cè)俗⒁夥稚顟B(tài)下交通流的變化情況。通過(guò)實(shí)驗(yàn)同時(shí)發(fā)現(xiàn),當(dāng)一定比例駕駛?cè)颂幱谧⒁夥稚顟B(tài)時(shí),路段內(nèi)交通流整體狀態(tài)會(huì)受到顯著影響,出現(xiàn)實(shí)際通行能力降低、飽和交通密度降低等情況。
[Abstract]:As an important participant in the complex system of "man-vehicle-road", the perception, judgment and manipulation of the driver will have an impact on the driving state, causing traffic congestion and even causing traffic accidents. The driver factor is very important to the traffic operation. The previous traffic simulation model is lack of performance to the difference of the driver's psychological state. This paper is based on STCA cellular automata and Multi-Agent traffic simulation methods respectively. This paper takes the driver's attention dispersion as an example to explore the construction method of the driver's model under the condition of the change of the psychological state, and simulates the traffic flow running state of a certain proportion of the drivers when the attention is dispersed. In order to study the change of driver's state and its influence on traffic flow under the condition of in-vehicle secondary task, firstly, based on the cognitive structure of ACT-R and Distract-R platform, the cognitive simulation of the driver's attention dispersive state is carried out. The time consumption and attention dispersion ratio of four different tasks are obtained, and the data is used as the driver's attention dispersion state library. Then, based on the STCA cellular automata traffic flow model, A traffic flow model considering the effect of in-vehicle secondary tasks is established. The model modifies the original cellular automata deceleration rules and can obtain some parameters of the model by calling the driver's attention decentralized state library. The traffic flow simulation experiment is carried out in Matlab to simulate the traffic flow situation of 20% of the drivers in the vehicle, and the secondary task types are randomly selected from the above four types. In addition, the paper is based on the Multi-Agent simulation method. In turn, the agents of road sections, vehicle Agent and signal lights are set up, and the operation and interaction rules are designed for each Agent. The traffic simulation system of Multi-Agent considering the decentralized state of driver is established by using NetLogo platform, and the simulation experiment is carried out. Thus, different experimental results can be verified and supplemented. Experimental data show that the secondary tasks in the vehicle have a significant impact on the traffic flow: the results of the traffic flow simulation experiment based on cellular automata show that, when 10 or 20 percent of the drivers perform secondary tasks, the maximum flow rate is reduced by 21.40.36.2and the maximum speed is reduced by about 11.1and 22.2. at the same time, With the increase of the proportion of drivers performing sub-tasks, the density corresponding to the peak value of flow and speed decreases gradually. The results of simulation experiments on road model based on Multi-Agent programming method show that when 10% of the drivers perform sub-tasks, 20% of the drivers perform sub-tasks. In the simulation experiment of road network model, the peak value of vehicle agglomeration becomes larger, the trend of dissipation becomes slow, and the phenomenon of vehicle queuing at intersection becomes more serious, while the maximum flow is reduced by 20.3and 37.6and the maximum speed is reduced by 15.6and 29.3. in the simulation experiment of road network model, the peak value of vehicle agglomeration becomes larger, and the trend of dissipation becomes slower. The experimental results show that the results obtained by the two simulation methods are basically consistent with the classical trend in previous literature and can reflect the traffic flow changes in the condition of the driver's attention to a certain extent. At the same time, it is found that when a certain proportion of drivers are in the state of attention dispersion, the overall state of the traffic flow in the road section will be significantly affected, and the actual traffic capacity will be reduced, and the saturated traffic density will be reduced.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491.254
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