發(fā)布時(shí)間：2018-05-15 08:59

  本文選題：車載網(wǎng)自組織網(wǎng)絡(luò) + 分簇?cái)?shù)據(jù)收集　； 參考：《中南大學(xué)》2014年碩士論文

【摘要】：隨著世界各國城市化進(jìn)程的加速,城市交通擁堵問題已經(jīng)成為當(dāng)今世界許多城市所面臨的難題。利用基于車載自組織網(wǎng)絡(luò)(VANET)收集實(shí)時(shí)交通數(shù)據(jù),對(duì)交叉路口信號(hào)燈進(jìn)行配時(shí)優(yōu)化,為用戶提供便捷交通引導(dǎo)服務(wù)具有重要研究意義。針對(duì)VANET網(wǎng)絡(luò)拓?fù)淇焖賱?dòng)態(tài)變換和交通信號(hào)燈配時(shí)優(yōu)化問題,本文以減少城市擁堵,提高道路利用率為目標(biāo),對(duì)VANET中分簇交通數(shù)據(jù)收集和信號(hào)燈配時(shí)優(yōu)化這兩個(gè)關(guān)鍵問題進(jìn)行研究。 首先,以增強(qiáng)網(wǎng)絡(luò)拓?fù)浞�(wěn)定性,提高數(shù)據(jù)傳輸率,降低通信開銷為目標(biāo),提出一種動(dòng)態(tài)分簇的交通數(shù)據(jù)收集算法。為適應(yīng)VANET網(wǎng)絡(luò)中車輛節(jié)點(diǎn)的動(dòng)態(tài)特性,在車對(duì)車通信模式(V2V)下,采用近鄰傳播簇頭選擇算法,將鄰居節(jié)點(diǎn)集、車輛速度、節(jié)點(diǎn)間距離和車道權(quán)重值作為簇頭選擇判據(jù),對(duì)簇內(nèi)節(jié)點(diǎn)進(jìn)行評(píng)估,建立適應(yīng)VANET網(wǎng)絡(luò)的分簇結(jié)構(gòu)；采用車與基礎(chǔ)設(shè)施通信模式(V2I),簇頭節(jié)點(diǎn)實(shí)時(shí)收集交通數(shù)據(jù)并發(fā)送至交叉路口智能體,為交叉路口信號(hào)燈進(jìn)行配時(shí)優(yōu)化提供實(shí)時(shí)的交通狀態(tài)信息。 其次,針對(duì)大規(guī)模城市交通系統(tǒng)中車流非連續(xù)性、時(shí)變性、隨機(jī)性等特點(diǎn),提出一種快速梯度下降的協(xié)同分布式Q學(xué)習(xí)信號(hào)配時(shí)優(yōu)化算法。建立交通信號(hào)配時(shí)優(yōu)化中的Q學(xué)習(xí)模型,利用VANET網(wǎng)絡(luò)收集的實(shí)時(shí)交通數(shù)據(jù),對(duì)交叉路口各車道車輛排隊(duì)長度進(jìn)行估計(jì)；通過交換相鄰路口的交通狀態(tài)信息,根據(jù)交叉路口間協(xié)同行為,設(shè)計(jì)無需中央監(jiān)控代理的優(yōu)化策略。為提高信號(hào)配時(shí)優(yōu)化算法的實(shí)時(shí)性,引入快速梯度下降因子,設(shè)計(jì)函數(shù)逼近方法,解決協(xié)同分布式Q學(xué)習(xí)中動(dòng)作行為對(duì)呈指數(shù)增長的維數(shù)災(zāi)難問題；并對(duì)傳統(tǒng)Q學(xué)習(xí)中的ε-貪婪策略進(jìn)行改進(jìn),尋求搜索和利用平衡策略,加快算法收斂速度。 利用VanetMobiSim和NS-2對(duì)交通數(shù)據(jù)分簇收集算法聯(lián)合仿真,使用GLD和MATLAB對(duì)交通信號(hào)配時(shí)優(yōu)化方案進(jìn)行仿真,驗(yàn)證論文所提算法的有效性。圖27幅,表2個(gè),參考文獻(xiàn)71篇。
[Abstract]:With the acceleration of the process of urbanization in the world, the problem of urban traffic congestion has become a difficult problem in many cities in the world. It is of great significance to use the vehicle based auto organization network (VANET) to collect real-time traffic data, optimize the timing of intersection signals and provide convenient traffic guidance services for users. For the fast dynamic transformation of VANET network topology and the optimization of traffic signal timing, this paper aims at reducing urban congestion and improving road utilization, and studies the two key problems of cluster traffic data collection and signal timing optimization in VANET.
First, in order to enhance the network topology stability, improve the data transmission rate and reduce the communication overhead, a dynamic clustering algorithm for traffic data collection is proposed. In order to adapt to the dynamic characteristics of the vehicle node in the VANET network, the neighbor transmission cluster head selection algorithm is adopted under the vehicle to vehicle communication mode (V2V), and the neighbor node set, vehicle speed and node are used. The interval and lane weight value are used as cluster head selection criteria to evaluate the cluster nodes and establish the cluster structure adapted to the VANET network. Using the vehicle and infrastructure communication mode (V2I), the cluster head nodes collect traffic data in real time and send to the intersection agent to provide real-time traffic shape for the intersection signal optimization. State information.
Secondly, in view of the characteristics of discontinuity, time variability and randomness in large-scale urban traffic system, a fast gradient descending cooperative distributed time optimization algorithm for cooperative distributed Q learning signal is proposed. The Q learning model of traffic signal timing optimization is set up, and the real-time traffic data collected by VANET network is used to arrange vehicles in each lane of intersection. In order to improve the real-time performance of the signal timing optimization algorithm, the fast gradient descent factor is introduced and the function approximation method is designed to solve the action behavior of the cooperative distributed Q learning. The dimension disaster problem is exponential growth, and the epsilon greedy strategy in the traditional Q learning is improved to search for and use the balance strategy to speed up the convergence speed of the algorithm.
The traffic data clustering algorithm is simulated by VanetMobiSim and NS-2. The traffic signal timing optimization scheme is simulated by GLD and MATLAB, and the validity of the proposed algorithm is verified. Figure 27, table 2, and 71 references.

【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U491.54

【參考文獻(xiàn)】

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本文編號(hào)：1891811