本文選題：短時交通流預(yù)測 + 時空依賴性��； 參考：《蘭州交通大學(xué)》2014年碩士論文

【摘要】：城市交通問題早已升級為城市可持續(xù)發(fā)展的最大制約。智能交通系統(tǒng)ITS恰是能夠解決這一問題的對癥方法，，實時準(zhǔn)確的流量預(yù)測信息是實現(xiàn)有關(guān)于ITS中動態(tài)路徑誘導(dǎo)系統(tǒng)的基礎(chǔ)和關(guān)鍵，而交叉口是道路網(wǎng)中道路通行能力的咽喉、交通阻塞和事故的多發(fā)地，因此對交叉口的交通流量的預(yù)測顯得越發(fā)重要。目前交通流誘導(dǎo)控制的時間跨度變短，使得交通流量變化的隨機性、混沌性、非線性和不確定性加強，導(dǎo)致早期的有檢測器交叉口的短時交通流量預(yù)測模型開始不能夠很好的契合交通流量變化時的種種特性，也不能夠較好的規(guī)避隨機因素對流量的預(yù)測產(chǎn)生較大的影響，得到的結(jié)果不能令人滿意；而在大多數(shù)城市有檢測器的交叉口數(shù)量還達不到全部交叉口的十分之一，如此一來對于沒有安裝檢測器的交叉口的交通流信息就很難取得，以上這些均成為了城市交通系統(tǒng)早日實現(xiàn)智能化的障礙與瓶頸，因此，針對兩類交叉口實時準(zhǔn)確的短時交通流量預(yù)測的研究顯得尤為急切與重要。 在本文中首先分析和研究了國內(nèi)外學(xué)者針對兩類交叉口短時交通流量預(yù)測的現(xiàn)狀、未來的發(fā)展趨勢、存在的問題。共歸結(jié)了三大問題以便在文章進行研究解決。 其次，利用遞歸圖和Lyapunov指數(shù)對費家營交叉口東進口的交通流數(shù)據(jù)進行了可預(yù)測性和混沌性分析。在此基礎(chǔ)上，再對該交叉口短時交通流的時空依賴性進行研究。第一，在時間維度上利用相似及波動系數(shù)進行了交通流的周相似性研究，確定了工作日、休息日、天氣（如晴天、雨天）等時間因素在交通流預(yù)測中的重要作用。第二，在空間維度上確定了交叉口進出口處預(yù)測斷面與周邊交叉口及路段之間流量的相互影響關(guān)系，并針對鄰接和非鄰接路段將其空間依賴性量化，得到空間上的影響波及范圍。通過以上研究提出了基于多維時空參數(shù)的短時交通流預(yù)測模型及框架，為后面交叉口短時交通流量預(yù)測提供依據(jù)。 然后，針對有檢測器交叉口交通流量預(yù)測從組合模型的搭配模式和單項模型的權(quán)重參數(shù)選取方面著手對模型進行改進和優(yōu)化。依照各單項模型的優(yōu)缺點，選取三大子模塊并將其進行改造以便能夠利用多維時空因素；由于預(yù)測誤差為隨機誤差，則利用正態(tài)分布的良好特性，提出了基于反饋機制的德爾塔變權(quán)重法，即利用各子模塊的預(yù)測誤差加權(quán)平均的方法，對于預(yù)測精度較高的預(yù)測值賦予較大的權(quán)重，由于各時段的交通狀態(tài)關(guān)系誤差不斷的變化進行反饋，從而權(quán)重可及時進行更新調(diào)整，不會造成過大的預(yù)測偏差，從而建立起了基于時空關(guān)聯(lián)狀態(tài)組合預(yù)測模型，并以安寧區(qū)區(qū)域路網(wǎng)部分交叉口為例，驗證了模型和算法的可行性。 最后，針對無檢測器交叉口交通流的預(yù)測問題，通過建立無檢測器交叉口與有檢測器交叉口之間的聯(lián)系，從而利用有檢測器交叉口流量來進行預(yù)測的角度出發(fā)。第一，介紹了常用的幾類歸類方法，引入了基于貝葉斯最小風(fēng)險準(zhǔn)則的PNN概率神經(jīng)網(wǎng)絡(luò)這一概念，并首次將其應(yīng)用在無檢測器交叉口與有檢測器交叉口的歸類中，提出了基于PNN概率神經(jīng)網(wǎng)絡(luò)的交叉口分類模式的預(yù)測方法。第二，介紹了歸類后的預(yù)測手段，即有線性回歸和非線性擬合，并引出了BP神經(jīng)網(wǎng)絡(luò)和遺傳GA優(yōu)化后的BP神經(jīng)網(wǎng)絡(luò)的兩類非線性擬合思路。建立了有檢測器交叉口和無檢測器交叉口動態(tài)聯(lián)系，實現(xiàn)無檢測器交叉口在時間空間上的歸類與短時交通流量數(shù)據(jù)的預(yù)測，并以安寧區(qū)區(qū)域路網(wǎng)部分交叉口為例，驗證了模型和算法的可行性。
[Abstract]:The urban traffic problem has been upgraded to the biggest restriction of urban sustainable development. The intelligent transportation system ITS is the right way to solve this problem. Real-time accurate traffic prediction information is the basis and key to realize the dynamic path induction system in ITS, and the intersection is the throat of road traffic capacity in the road network. It is more important to predict the traffic flow of the intersection, so the time span of the traffic flow induced control is shorter, which makes the traffic flow change randomness, chaos, nonlinearity and uncertainty strengthened, which leads to the inability to predict the short time traffic flow prediction model at the early stage of the detector intersection. It is good enough to fit the characteristics of the change of traffic flow, and it can not be better to avoid the large impact of random factors on the prediction of flow, and the results can not be satisfactory. In most cities, the number of intersections of the detector is not up to 1/10 of the entire intersection, so that there is no installation inspection. The traffic flow information at the intersections of the measuring apparatus is difficult to obtain. These all become the obstacles and bottlenecks of the early realization of the intelligent urban traffic system. Therefore, the research on real-time and accurate short-term traffic flow prediction for the two types of intersections is particularly urgent and important.
In this paper, we first analyze and study the current situation of short-term traffic flow forecast at the two types of intersections at home and abroad, the future development trend and the existing problems. Three major problems are summed up in order to solve the problem in this article.
Secondly, the predictability and chaos of traffic flow data imported from the east of Fisher intersection are analyzed by using recursion and Lyapunov index. On this basis, the temporal and spatial dependence of short time traffic flow at the intersection is studied. Firstly, the cycle similarity of traffic flow is studied by using similarity and wave coefficient in time dimension. The important role of time factors such as working days, rest days, weather (such as sunny days, rainy days) and other factors in traffic flow prediction is determined. Second, the interaction relationship between the prediction section of the inlet and exit of the intersection and the flow between the surrounding intersections and the sections is determined on the spatial dimension, and the spatial dependence of the adjacent and non adjacent sections is quantified. Through the above study, a short-term traffic flow prediction model and framework based on multidimensional space-time parameters is proposed, which provides the basis for the short-term traffic flow prediction in the rear intersection.
Then, the model is improved and optimized for the combination model of the detector intersection and the selection of the weight parameters of the single model. According to the advantages and disadvantages of each single model, three modules are selected and modified to make use of the multidimensional time and space factors. According to the good characteristics of the normal distribution, the Del tower variable weight method based on the feedback mechanism is proposed, that is, the weighted average of the prediction error of each sub module is used to give greater weight to the predicted value with higher prediction accuracy. The renewal and adjustment can be carried out in time, which will not cause excessive prediction deviation, thus the combined prediction model based on spatio-temporal correlation state is established, and the feasibility of the model and algorithm is verified by the example of the part intersection of the regional road network in the Anning area.
Finally, in view of the prediction problem of traffic flow without detector intersection, by establishing the connection between the detector intersection and the intersections of the detector, the prediction angle is made using the traffic of the detector intersection. First, several commonly used classification methods are introduced, and the PNN based on the Bayes minimum risk criterion is introduced. The concept of rate neural network is used for the first time in the classification of intersections without detector intersection and detector. The prediction method of intersection classification based on PNN probabilistic neural network is proposed. Second, the prediction method after classification is introduced, that is, linear regression and nonlinear fitting, and leads to BP neural network and heredity. The two kind of nonlinear fitting method of BP neural network after GA optimization is proposed. The dynamic connection between the detector intersection and the non detector intersection is established to realize the classification of the non detector intersection in time and space and the prediction of the short time traffic data, and the feasibility of the model and the algorithm is verified by the example of the part intersection of the regional road network in the Anning area. Sex.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U491.112

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