本文選題：交通流量預(yù)測(cè) + 智能交通　； 參考：《沈陽(yáng)工業(yè)大學(xué)》2014年碩士論文

【摘要】：隨著社會(huì)的進(jìn)步,交通問(wèn)題己日益受到人們的關(guān)注。為了優(yōu)化道路環(huán)境，保證交通暢通，減少空氣污染、汽車噪聲的危害，許多國(guó)家都在開(kāi)展智能交通（ITS）的研究，作為ITS的重要研究領(lǐng)域——交通控制與誘導(dǎo)系統(tǒng)是智能交通系統(tǒng)建設(shè)的核心課題，而實(shí)現(xiàn)交通流誘導(dǎo)系統(tǒng)的關(guān)鍵問(wèn)題是準(zhǔn)確的短時(shí)交通流量預(yù)測(cè)，即如何有效地利用實(shí)時(shí)交通數(shù)據(jù)信息去滾動(dòng)預(yù)測(cè)未來(lái)幾分鐘內(nèi)的交通狀況。 早在20世紀(jì)六七十年代，國(guó)外就開(kāi)始將預(yù)測(cè)模型用于短時(shí)交通流量預(yù)測(cè)領(lǐng)域。交通流預(yù)測(cè)的研究模型有很多種，如：神經(jīng)網(wǎng)絡(luò)模型、多元線性回歸模型、時(shí)間序列模型、歷史趨勢(shì)模型、Kalman濾波模型等。而本文則著重研究Kalman濾波在交通流預(yù)測(cè)中的應(yīng)用。 本文研究了交通流的靜態(tài)穩(wěn)定性以及突變性，，對(duì)交通流的可預(yù)測(cè)性進(jìn)行判別。結(jié)合灰色關(guān)聯(lián)分析方法建立Kalman濾波交通流預(yù)測(cè)模型。本文對(duì)交通流在空間上分布的特點(diǎn)進(jìn)行分析，利用灰色關(guān)聯(lián)分析方法，分析被測(cè)路段會(huì)受到哪些參數(shù)的影響。此外,本文為了改善Kalman濾波模型預(yù)測(cè)效果，提出了利用相鄰數(shù)周中相對(duì)應(yīng)時(shí)間的交通流比值代替原始數(shù)據(jù)，建立基于歷史數(shù)據(jù)的Kalman濾波交通流預(yù)測(cè)模型。本文將所建立預(yù)測(cè)模型與其他基于kalman濾波的交通流預(yù)測(cè)模型作對(duì)比，研究表明本文算法的計(jì)算模型性能指標(biāo)要優(yōu)于其他預(yù)測(cè)模型。 本文利用模擬數(shù)據(jù)對(duì)上述預(yù)測(cè)模型及算法進(jìn)行了驗(yàn)證。實(shí)驗(yàn)結(jié)果表明：灰色關(guān)聯(lián)分析能夠有效地分析出各項(xiàng)影響交通流的參數(shù)，提高預(yù)測(cè)模型的適應(yīng)性；以歷史數(shù)據(jù)、實(shí)時(shí)數(shù)據(jù)為基礎(chǔ)的預(yù)測(cè)模型，其預(yù)測(cè)效果要優(yōu)于只運(yùn)用實(shí)時(shí)數(shù)據(jù)的交通流量預(yù)測(cè)模型，從而證明了該模型的適應(yīng)性強(qiáng)，預(yù)測(cè)精度高。
[Abstract]:With the development of the society, people pay more and more attention to the traffic problem. In order to optimize the road environment, ensure the smooth flow of traffic, reduce air pollution and the harm of automobile noise, many countries are carrying out research on Intelligent Transportation (its). As an important research field of its, traffic control and guidance system is the core of its construction, and the key problem of realizing traffic flow guidance system is accurate short-term traffic flow forecasting. That is, how to use real-time traffic data effectively to predict traffic situation in the next few minutes. As early as 1960s and 1970s, foreign countries began to use forecasting models in the field of short-term traffic flow forecasting. There are many research models for traffic flow prediction, such as neural network model, multivariate linear regression model, time series model, historical trend model and Kalman filter model. This paper focuses on the application of Kalman filter in traffic flow prediction. In this paper, the static stability and catastrophe of traffic flow are studied, and the predictability of traffic flow is judged. The traffic flow prediction model of Kalman filter is established by using grey correlation analysis method. In this paper, the characteristics of traffic flow distribution in space are analyzed, and the influence of the parameters on the measured road sections is analyzed by using the grey correlation analysis method. In addition, in order to improve the prediction effect of Kalman filter model, a Kalman filtering traffic flow prediction model based on historical data is established by using the traffic flow ratio corresponding to the corresponding time in adjacent weeks instead of the original data. In this paper, the proposed prediction model is compared with other traffic flow prediction models based on kalman filter. The results show that the performance of the proposed algorithm is better than that of other models. In this paper, the simulation data are used to verify the above prediction model and algorithm. The experimental results show that the grey correlation analysis can effectively analyze the parameters that affect the traffic flow and improve the adaptability of the forecasting model, which is based on historical data and real-time data. The forecasting effect is better than the traffic flow forecasting model which only uses real time data, which proves that the model has strong adaptability and high prediction accuracy.
【學(xué)位授予單位】：沈陽(yáng)工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495;U491.1

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