本文選題：智能交通　切入點(diǎn)：交通流量　出處：《合肥工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著智能交通系統(tǒng)的快速發(fā)展,智能交通的實(shí)時(shí)、準(zhǔn)確、高效的控制儼然已成為智能交通系統(tǒng)中的重要部分。交通流量預(yù)測(cè)作為智能交通系統(tǒng)的基礎(chǔ),可對(duì)交通誘導(dǎo),道路擁堵預(yù)警以及最優(yōu)路徑的選擇提供有效的參考數(shù)據(jù)。預(yù)測(cè)的精確性直接影響交通管理和控制的成效。同時(shí),由于道路擁堵現(xiàn)象的存在,選擇出合適的駕駛路徑,節(jié)約駕駛時(shí)間也顯得格外重要。目前,汽車導(dǎo)航越來越普及,但并不能為人類智能的選擇出耗時(shí)最短的路徑,因此,提供最優(yōu)行駛路徑對(duì)交通誘導(dǎo)意義重大。針對(duì)提高短時(shí)交通流量預(yù)測(cè)模型的精度以及準(zhǔn)確性,本文首先對(duì)采集的原始交通流量進(jìn)行預(yù)處理,完成原始交通流量數(shù)據(jù)歸一化、簡(jiǎn)約、修正和補(bǔ)全后,在小波分析的基礎(chǔ)上,把交通流量序列信號(hào)分解為低頻序列信號(hào)和高頻序列信號(hào);接著依據(jù)卡爾曼濾波模型在處理平穩(wěn)數(shù)據(jù)上的優(yōu)勢(shì)對(duì)原始交通流量的低頻序列信號(hào)進(jìn)行預(yù)測(cè)和RBF神經(jīng)網(wǎng)絡(luò)的高動(dòng)態(tài)非線性映射性對(duì)高頻序列信號(hào)進(jìn)行預(yù)測(cè),建立了一種變結(jié)構(gòu)式組合交通流量預(yù)測(cè)模型,得出預(yù)測(cè)結(jié)果,并對(duì)預(yù)測(cè)誤差進(jìn)行分析;最后在預(yù)測(cè)結(jié)果以及車輛導(dǎo)航系統(tǒng)數(shù)據(jù)處理算法分析的基礎(chǔ)上,運(yùn)用Vissim交通控制軟件賦值在模擬路網(wǎng),采用Floyd算法選擇出基于時(shí)間權(quán)重的路徑,結(jié)合變結(jié)構(gòu)式組合交通流量預(yù)測(cè)模型的預(yù)測(cè)算法,利用VS(Microsoft Visual Studio)實(shí)現(xiàn)軟件功能的開發(fā)。
[Abstract]:With the rapid development of intelligent transportation system, real-time, accurate and efficient control of intelligent transportation has become an important part of intelligent transportation system. As the basis of intelligent transportation system, traffic flow forecasting can guide traffic. The prediction accuracy directly affects the effectiveness of traffic management and control. At the same time, due to the existence of traffic congestion, the appropriate driving path is chosen. It is especially important to save driving time. At present, car navigation is becoming more and more popular, but it can not choose the shortest path for human intelligence, so, In order to improve the accuracy and accuracy of the short-term traffic flow prediction model, this paper first preprocesses the collected original traffic flow and completes the normalization of the original traffic flow data. On the basis of wavelet analysis, the traffic flow sequence signal is decomposed into low frequency sequence signal and high frequency sequence signal. Then according to the advantage of Kalman filter model in processing stationary data, the low frequency sequence signal of original traffic flow is predicted and the high dynamic nonlinear mapping of RBF neural network is used to predict the high frequency sequence signal. A variable structure integrated traffic flow forecasting model is established, the prediction results are obtained, and the prediction error is analyzed. Finally, based on the analysis of the prediction results and the data processing algorithm of the vehicle navigation system, The Vissim traffic control software assignment is used to simulate the road network, the Floyd algorithm is used to select the path based on time weight, the variable structure combined traffic flow forecasting algorithm is combined, and the software function is developed by VS(Microsoft Visual Studio.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491.14

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