本文關(guān)鍵詞： 耦合映射 跟馳模型 優(yōu)化速度 多車(chē)效應(yīng) 穩(wěn)定性 擁堵抑制　出處：《重慶大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：車(chē)輛跟馳模型(Car-Following Model)描述了單車(chē)道無(wú)超車(chē)行駛車(chē)隊(duì)中后車(chē)跟隨前車(chē)的動(dòng)力學(xué)過(guò)程，從微觀(guān)層面刻畫(huà)了單車(chē)道上交通流的演化特性，對(duì)現(xiàn)代交通的模擬、評(píng)價(jià)及管理控制有著重要的理論價(jià)值和實(shí)際意義。耦合映射(CoupledMap)跟馳模型是車(chē)輛跟馳模型的一種離散版本，適于描述復(fù)雜的交通控制問(wèn)題，且算法簡(jiǎn)潔，便于計(jì)算機(jī)仿真實(shí)現(xiàn)，但是傳統(tǒng)CM模型考慮的駕駛因素較少，對(duì)實(shí)際交通的刻畫(huà)比較簡(jiǎn)單。 隨著交通系統(tǒng)內(nèi)車(chē)輛密度的增加，車(chē)與車(chē)之間的關(guān)系越來(lái)越緊密，當(dāng)前車(chē)的行駛狀態(tài)不僅受其直接前導(dǎo)車(chē)的影響，而且還受前方多輛車(chē)和后方車(chē)輛狀態(tài)的影響，而傳統(tǒng)的耦合映射模型沒(méi)有考慮這種多車(chē)效應(yīng)，為此，本文從前后多車(chē)效應(yīng)出發(fā)，對(duì)CM模型進(jìn)行改進(jìn)，并研究了基于改進(jìn)模型的交通擁堵抑制方法，為如何提供信息引導(dǎo)駕駛行為的改變以抑制交通擁堵提供了一定參考。論文的主要工作如下： 首先，考慮多前車(chē)車(chē)頭間距信息，對(duì)CM模型中的優(yōu)化速度(OV)函數(shù)進(jìn)行改進(jìn)，提出了基于多前車(chē)效應(yīng)的耦合映射(MPVCM)跟馳模型，利用線(xiàn)性系統(tǒng)理論導(dǎo)出了其穩(wěn)定性條件，在開(kāi)放邊界條件下對(duì)模型進(jìn)行數(shù)值模擬，，結(jié)果表明：對(duì)比傳統(tǒng)CM模型，MPVCM模型明顯改善了小擾動(dòng)引起的車(chē)流失穩(wěn)現(xiàn)象，提高了車(chē)流穩(wěn)定性，并且得到考慮前方車(chē)輛的最佳數(shù)目為3輛。 然后，由于實(shí)際交通中駕駛員會(huì)通過(guò)后視鏡或借助ITS觀(guān)察后車(chē)狀態(tài)變化，為此在MPVCM模型上進(jìn)一步考慮后車(chē)效應(yīng)，設(shè)計(jì)相應(yīng)的后視優(yōu)化速度函數(shù)，提出了考慮前后多車(chē)綜合效應(yīng)的CECM模型，對(duì)其進(jìn)行了穩(wěn)定性分析與數(shù)值模擬，結(jié)果顯示：相比MPVCM模型，CECM模型中的穩(wěn)態(tài)車(chē)頭間距增大，車(chē)速波動(dòng)幅度更小，更加有效地改善了小擾動(dòng)引起的車(chē)流失穩(wěn)現(xiàn)象，進(jìn)一步提高車(chē)流穩(wěn)定性。 最后，考慮到鄰近前車(chē)與當(dāng)前車(chē)速度差和安全間距是駕駛員在跟隨行駛中最直接關(guān)注的信息因素，提出了基于本文CECM模型的速度差-安全間距(VDSH)綜合效應(yīng)擁堵抑制方法，理論推導(dǎo)了不產(chǎn)生交通擁堵的條件，給出了反饋增益取值的一般方法。理論分析和模擬結(jié)果表明：VDSH-CECM模型對(duì)比VDSH-CM模型的擁堵抑制效果更好，反饋增益取值范圍更大。同時(shí)，統(tǒng)計(jì)不同車(chē)輛參數(shù)對(duì)應(yīng)反饋增益范圍發(fā)現(xiàn)其取值區(qū)間與駕駛員敏感系數(shù)和車(chē)輛最大速度之間存在線(xiàn)性相關(guān)性，且這種相關(guān)性與實(shí)際交通情況吻合。
[Abstract]:Car-following Model describes the dynamic process of the rear car following the front car in a single-lane no-overtaking vehicle fleet. The evolutionary characteristics of traffic flow in a single lane are described from the microscopic level, and the simulation of modern traffic is presented. Evaluation and management control have important theoretical and practical significance. Coupled map coupled Map) car-following model is a discrete version of car-following model. It is suitable to describe complex traffic control problems, and the algorithm is simple and convenient for computer simulation. However, the traditional CM model takes less driving factors into account, and the description of actual traffic is relatively simple. With the increase of vehicle density in traffic system, the relationship between vehicle and vehicle is more and more close. The current driving state of vehicle is not only affected by its direct leading vehicle. But the traditional coupling mapping model does not take this multi-vehicle effect into account. Therefore, this paper improves the CM model based on the multi-vehicle effect. And the traffic congestion suppression method based on the improved model is studied, which provides a certain reference for how to provide information to guide driving behavior change to curb traffic congestion. The main work of this paper is as follows: Firstly, considering the headspace information of multi-front vehicle, the optimized speed and OV) function in CM model is improved, and a coupled mapping (MPVCM) car-following model based on multi-front vehicle effect is proposed. The stability conditions are derived by using the linear system theory and the numerical simulation of the model is carried out under the open boundary condition. The results show that the pair is better than the traditional CM model. The MPVCM model obviously improves the instability of vehicle flow caused by small disturbance and improves the stability of vehicle flow. The optimal number of vehicles considered in front is 3. Then, because the driver will observe the change of the vehicle state through the rearview mirror or with the help of ITS in the actual traffic, this paper further considers the rear vehicle effect in the MPVCM model, and designs the corresponding optimized speed function of the rear view. The stability analysis and numerical simulation of the CECM model considering the comprehensive effect of front and rear vehicles are presented. The results show that the steady headway spacing in the MPVCM model is larger than that in the MPVCM model. The fluctuation of vehicle speed is smaller, which can effectively improve the instability of vehicle flow caused by small disturbance and further improve the stability of vehicle flow. Finally, considering that the speed difference and safety distance between the adjacent front car and the current vehicle are the most direct information factors that the driver pays close attention to in following the driving. Based on the CECM model of this paper, a new method of congestion suppression based on the CECM model is proposed, and the condition of no traffic congestion is derived theoretically. The theoretical analysis and simulation results show that the VDSH-CECM model is more effective than the VDSH-CM model in congestion suppression. The feedback gain range is larger. At the same time, according to the feedback gain range of different vehicle parameters, it is found that there is a linear correlation between the range of feedback gain and the driver sensitivity coefficient and the maximum vehicle speed. And this correlation is consistent with the actual traffic situation.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：U491

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 秦麗輝;田國(guó)旺;蔣天恩;;車(chē)輛跟馳模型研究綜述[J];長(zhǎng)春工程學(xué)院學(xué)報(bào)(自然科學(xué)版);2007年03期

2 何民,榮建,任福田;判定跟馳狀態(tài)的研究[J];公路交通科技;2001年04期

3 賈洪飛,雋志才,曹鵬;跟馳過(guò)程中駕駛員認(rèn)知結(jié)構(gòu)模型的建立[J];公路交通科技;2005年11期

4 余寒梅;程榮軍;葛紅霞;;Considering Backward Effect in Coupled Map Car-Following Model[J];Communications in Theoretical Physics;2010年07期

5 孫棣華;張建廠(chǎng);廖孝勇;田川;李永福;劉衛(wèi)寧;;非鄰近車(chē)輛最優(yōu)速度差模型[J];交通運(yùn)輸工程學(xué)報(bào);2011年06期

6 陳剛;易建華;;考慮車(chē)輛加速度和次鄰近車(chē)輛速度差的跟馳模型及仿真研究[J];科學(xué)技術(shù)與工程;2012年15期

7 孫棣華;張建廠(chǎng);趙敏;田川;;考慮后視效應(yīng)和速度差信息的跟馳模型[J];四川大學(xué)學(xué)報(bào)(自然科學(xué)版);2012年01期

8 薛郁,董力耘,袁以武,戴世強(qiáng);考慮車(chē)輛相對(duì)運(yùn)動(dòng)速度的交通流演化過(guò)程的數(shù)值模擬[J];物理學(xué)報(bào);2002年03期

9 陳漩;高自友;趙小梅;賈斌;;反饋控制雙車(chē)道跟馳模型研究[J];物理學(xué)報(bào);2007年04期

10 韓祥臨;姜長(zhǎng)元;葛紅霞;戴世強(qiáng);;基于智能交通系統(tǒng)的耦合映射跟馳模型和交通擁堵控制[J];物理學(xué)報(bào);2007年08期

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