本文選題：模糊神經(jīng)網(wǎng)絡(luò) + 平均車速預(yù)測�。� 參考：《中南大學(xué)》2014年碩士論文

【摘要】：摘要：車輛路徑規(guī)劃技術(shù)是解決城市交通擁堵的有效手段之一。傳統(tǒng)的路徑規(guī)劃算法,通常只給出最優(yōu)路徑,難以避免車輛所經(jīng)過路段偶然癱瘓導(dǎo)致沒有可選路徑的問題。引入多路徑規(guī)劃技術(shù),可保證車輛在任何情況下都有可選路徑,提高路徑規(guī)劃穩(wěn)定性。但是目前的多路徑規(guī)劃技術(shù)實時性不高,且算法效率較低,設(shè)計高效實時的多路徑規(guī)劃算法頗具挑戰(zhàn)性。本文以提高多路徑規(guī)劃的實時性和穩(wěn)定性為目標,對預(yù)測機制和多路徑規(guī)劃這兩個關(guān)鍵技術(shù)進行研究。 首先,為了給路徑規(guī)劃提供實時可靠的數(shù)據(jù),本文擬采用模糊神經(jīng)網(wǎng)絡(luò)預(yù)測機制,精確預(yù)測下一時刻交通路網(wǎng)狀況。由道路傳感器收集平均車速數(shù)據(jù),建立模糊神經(jīng)網(wǎng)絡(luò)模型預(yù)測未來車速,從而計算每條路段的未來平均通過時間,為路徑規(guī)劃提供未來的道路狀況信息。模糊神經(jīng)網(wǎng)絡(luò)能真實反應(yīng)交通信息的非線性特性,且預(yù)測精度很高。此外,本文擬引用Taguchi方法,在一定預(yù)測精度要求下,使用盡可能少的傳感器數(shù)據(jù),提高預(yù)測效率。 其次,在預(yù)測數(shù)據(jù)基礎(chǔ)上,為了提高多路徑規(guī)劃的算法效率和穩(wěn)定性,提出了基于Q學(xué)習(xí)的多路徑規(guī)劃算法。根據(jù)Q學(xué)習(xí)思想對復(fù)雜城市路網(wǎng)進行建模。利用了Q值能反映當(dāng)前路口距離目的地的長期反饋特性,推導(dǎo)出最優(yōu)路徑。然后確定合適的參考值,選取滿足條件的次優(yōu)Q值,實現(xiàn)多候選路徑的選取。此外引入多路徑集的穩(wěn)定性約束,保證任何情況下至少存在一條候選路徑可供車輛選擇；引入?yún)f(xié)同機制,均衡未來路網(wǎng)負載。 最后,利用MATLAB仿真工具分析了模糊神經(jīng)網(wǎng)絡(luò)預(yù)測機制的精度,基于增強學(xué)習(xí)的多候選路徑算法的效率和穩(wěn)定性,驗證了所提的路徑規(guī)劃方案。圖24幅,表8個,參考文獻60篇。
[Abstract]:Abstract: vehicle path planning is one of the effective means to solve urban traffic congestion. The traditional path planning algorithm usually only gives the optimal path, and it is difficult to avoid the problem that there is no alternative path caused by the accidental paralysis of the road sections. The introduction of the multi-path planning technique ensures that the vehicle has an optional path in any case. The stability of path planning is improved. However, the current multipath planning technology is not very real-time, and the efficiency of the algorithm is low. It is very challenging to design the efficient and real-time multi-path planning algorithm. This paper aims at improving the real-time and stability of the multipath planning, and studies the two key technologies of the prediction mechanism and the path planning.
First, in order to provide real-time and reliable data for path planning, this paper uses the fuzzy neural network prediction mechanism to accurately predict the state of traffic network at the next moment. The road sensor collects the average speed data and establishes a fuzzy neural network model to predict the future speed, thus calculating the future average passing time of each section, as the path. The planning provides the future road information. Fuzzy neural network can truly reflect the nonlinear characteristics of traffic information, and the prediction accuracy is very high. In addition, this paper is proposed to use the Taguchi method to use as little sensor data as possible to improve the prediction efficiency under the requirements of certain prediction accuracy.
Secondly, on the basis of the prediction data, in order to improve the efficiency and stability of the algorithm for multipath planning, a multi-path planning algorithm based on Q learning is proposed. Based on the Q learning idea, the complex urban road network is modeled. The Q value can reflect the long-term feedback characteristics of the destination of the intersection distance, and the optimal path is derived. The suboptimal Q value satisfying the condition is selected to select the multi candidate path. In addition, the stability constraint of the multipath set is introduced to ensure that at least one candidate path is available for vehicle selection in any case, and the cooperative mechanism is introduced to balance the load of the road network.
Finally, the accuracy of the fuzzy neural network prediction mechanism is analyzed by using the MATLAB simulation tool. Based on the efficiency and stability of the enhanced learning multi candidate path algorithm, the proposed path planning scheme is verified. Figure 24, table 8, and 60 references.

【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495;U492.22

【參考文獻】

相關(guān)期刊論文 前1條

1 金茂菁;;中國智能交通發(fā)展歷程淺談[J];交通科技;2013年02期

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本文編號：1868014