【摘要】：高速公路合流區(qū)是高速公路的“瓶頸”路段。進(jìn)入合流區(qū)的車(chē)輛主要是上游主線車(chē)流量和相連入口匝道車(chē)流量，兩部分車(chē)輛合流時(shí)，由于主線和匝道流量變化、速度不一致，會(huì)引起主線交通流紊亂，易導(dǎo)致安全問(wèn)題發(fā)生，使得通行效率下降。由于合流區(qū)的流量與主線和匝道流量具有相關(guān)性，通過(guò)分析合流區(qū)上下游的流量特性，掌握合流區(qū)的流量變化規(guī)律，判斷其發(fā)展的趨勢(shì)，對(duì)保證車(chē)輛暢通運(yùn)行，提高合流區(qū)的通行效率和交通安全具有重要的意義。 為此，本文依據(jù)高速公路合流區(qū)上下游檢測(cè)器獲取的交通流數(shù)據(jù)，分析合流區(qū)的流量與主線和匝道流量的時(shí)空相關(guān)性，并將時(shí)空關(guān)聯(lián)分析與支持向量回歸機(jī)結(jié)合用于實(shí)現(xiàn)多斷面影響下高速公路合流區(qū)流量的短時(shí)預(yù)測(cè)，研究建立了適合高速公路合流區(qū)短時(shí)流量預(yù)測(cè)的回歸預(yù)測(cè)模型。主要研究?jī)?nèi)容包括： ①高速公路合流區(qū)上下游交通流量特性分析。首先，將高速公路合流區(qū)交通數(shù)據(jù)時(shí)間序列分為環(huán)比時(shí)間序列和同比時(shí)間序列；然后，引入相似性度量函數(shù)來(lái)測(cè)度時(shí)間關(guān)聯(lián)性和空間關(guān)聯(lián)性，同時(shí)，通過(guò)“分時(shí)段”的相似性測(cè)度來(lái)對(duì)比分析高速公路合流區(qū)交通流量變化特性；最后，利用重慶渝武高速公路G75中環(huán)快速干道互通立交合流區(qū)的實(shí)測(cè)數(shù)據(jù)進(jìn)行分析與驗(yàn)證。 ②基于時(shí)空關(guān)聯(lián)性分析與支持向量機(jī)回歸結(jié)合的預(yù)測(cè)模型的建立。首先，，分析傳統(tǒng)采用相鄰前n個(gè)時(shí)間段的交通流數(shù)據(jù)作為輸入建立的支持向量機(jī)回歸（SVR）預(yù)測(cè)模型的不足；然后，利用合流區(qū)時(shí)空關(guān)聯(lián)分析結(jié)果，改進(jìn)了傳統(tǒng)支持向量機(jī)回歸模型，建立了基于時(shí)空關(guān)聯(lián)性分析與支持向量機(jī)回歸結(jié)合的合流區(qū)流量預(yù)測(cè)模型；最后，通過(guò)網(wǎng)格搜索、遺傳算法和粒子群算法來(lái)獲取SVR的參數(shù)，并利用實(shí)測(cè)數(shù)據(jù)來(lái)分析改進(jìn)模型的預(yù)測(cè)效果。 ③合流區(qū)流量時(shí)序峰值預(yù)測(cè)的加權(quán)最小二乘支持向量機(jī)回歸模型的建立。首先，針對(duì)流量時(shí)序峰值樣本擬合預(yù)測(cè)誤差偏大的問(wèn)題，基于已有研究成果并結(jié)合加權(quán)最小二乘思想，設(shè)計(jì)了基于自信息的擬合誤差加權(quán)修正系數(shù)；然后，利用設(shè)計(jì)的擬合誤差加權(quán)修正系數(shù)來(lái)增大峰值樣本擬合誤差權(quán)重來(lái)實(shí)現(xiàn)對(duì)流量時(shí)序峰值擬合回歸預(yù)測(cè)；最后，利用重慶市渝武高速公路的實(shí)測(cè)流量峰值數(shù)據(jù)對(duì)模型進(jìn)行了分析和驗(yàn)證。
[Abstract]:The confluence zone of expressway is the bottleneck section of expressway. The vehicles entering the confluence zone are mainly the upstream main line traffic flow and the connected on-ramp traffic flow. When the two parts of the vehicle converge, due to the variation of the main line and ramp flow, the speed is inconsistent, which will cause the main line traffic flow disorder, which will easily lead to safety problems. Reduce the efficiency of traffic. Because the flow of the confluence zone is related to the main line and the ramp flow, by analyzing the flow characteristics of the upstream and downstream of the confluence zone, we can master the law of the flow change in the confluence zone, judge its developing trend, and ensure the smooth operation of the vehicle. It is of great significance to improve the traffic efficiency and traffic safety in the confluence area. Therefore, based on the traffic flow data obtained by the upstream and downstream detectors in the freeway confluence area, this paper analyzes the space-time correlation between the traffic flow in the confluence area and the main line and ramp flow. The combination of time and space connection analysis and support vector regression machine is used to realize the short time prediction of the flow in the confluence area of freeway under the influence of multi-section, and a regression forecasting model suitable for the prediction of short time flow in the confluence area of expressway is established. The main research contents are as follows: 1 Analysis of traffic flow characteristics of upstream and downstream in freeway confluence area. First, the time series of traffic data in freeway confluence area is divided into the time series of ring comparison and the time series of year on year. Then, the similarity measure function is introduced to measure the temporal and spatial correlation. At the same time, the characteristics of the traffic flow in the freeway confluence area are compared and analyzed by the similarity measure of "divided time". Finally, the measured data of G75 Central Expressway Interchange and Interchange area of Chongqing Yu-Wu Expressway are analyzed and verified. 2 the establishment of prediction model based on the combination of spatiotemporal correlation analysis and support vector machine regression. Firstly, the shortcomings of the traditional support vector machine (SVM) regression (SVR) prediction model based on the traffic flow data of the first n adjacent time periods are analyzed. Then, the traditional support vector machine regression model is improved, and the flow prediction model based on the combination of spatio-temporal correlation analysis and support vector machine regression is established. Finally, the parameters of SVR are obtained by grid search, genetic algorithm and particle swarm optimization algorithm, and the prediction effect of the improved model is analyzed by using the measured data. (3) the establishment of weighted least squares support vector machine regression model for predicting the peak value of flow time series in the confluence region. Firstly, aiming at the problem that the prediction error of peak sample fitting of flow time series is too large, based on the existing research results and the idea of weighted least squares, the weighted correction coefficient of fitting error based on self-information is designed. Then, the weight of the peak sample fitting error is increased by using the weighted correction coefficient of the designed fitting error to realize the prediction of the peak fitting regression of the flow time series. Finally, the model is analyzed and verified by using the measured peak flow data of Chongqing Yuwu Expressway.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U491.1

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