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

【摘要】：車輛跟馳模型(Car-Following Model)描述了單車道無超車行駛車隊中后車跟隨前車的動力學(xué)過程，從微觀層面刻畫了單車道上交通流的演化特性，對現(xiàn)代交通的模擬、評價及管理控制有著重要的理論價值和實際意義。耦合映射(CoupledMap)跟馳模型是車輛跟馳模型的一種離散版本，適于描述復(fù)雜的交通控制問題，且算法簡潔，便于計算機仿真實現(xiàn)，但是傳統(tǒng)CM模型考慮的駕駛因素較少，對實際交通的刻畫比較簡單。 隨著交通系統(tǒng)內(nèi)車輛密度的增加，車與車之間的關(guān)系越來越緊密，當(dāng)前車的行駛狀態(tài)不僅受其直接前導(dǎo)車的影響，而且還受前方多輛車和后方車輛狀態(tài)的影響，而傳統(tǒng)的耦合映射模型沒有考慮這種多車效應(yīng)，為此，本文從前后多車效應(yīng)出發(fā)，對CM模型進行改進，并研究了基于改進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，考慮多前車車頭間距信息，對CM模型中的優(yōu)化速度(OV)函數(shù)進行改進，提出了基于多前車效應(yīng)的耦合映射(MPVCM)跟馳模型，利用線性系統(tǒng)理論導(dǎo)出了其穩(wěn)定性條件，在開放邊界條件下對模型進行數(shù)值模擬，，結(jié)果表明：對比傳統(tǒng)CM模型，MPVCM模型明顯改善了小擾動引起的車流失穩(wěn)現(xiàn)象，提高了車流穩(wěn)定性，并且得到考慮前方車輛的最佳數(shù)目為3輛。 然后，由于實際交通中駕駛員會通過后視鏡或借助ITS觀察后車狀態(tài)變化，為此在MPVCM模型上進一步考慮后車效應(yīng)，設(shè)計相應(yīng)的后視優(yōu)化速度函數(shù)，提出了考慮前后多車綜合效應(yīng)的CECM模型，對其進行了穩(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)車頭間距增大，車速波動幅度更小，更加有效地改善了小擾動引起的車流失穩(wěn)現(xiàn)象，進一步提高車流穩(wěn)定性。 最后，考慮到鄰近前車與當(dāng)前車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，統(tǒng)計不同車輛參數(shù)對應(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)和車輛最大速度之間存在線性相關(guān)性，且這種相關(guān)性與實際交通情況吻合。
[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)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U491

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