發(fā)布時間：2019-06-13 21:28

【摘要】：高速公路交通狀態(tài)的準(zhǔn)確估計是把握高速公路交通運行情況的關(guān)鍵。單一交通流參數(shù)只能間接、局部的反映實際的交通運行狀況,依據(jù)多個不同交通流參數(shù)進(jìn)行聚類分析是地點交通狀態(tài)估計的典型方法,但聚類結(jié)果對樣本數(shù)量非常敏感;另一方面,目前對路段交通狀態(tài)的估計一般只考慮行程時間或行程車速,因數(shù)據(jù)采集手段的限制,估計的交通狀態(tài)存在一定不確定性。針對這些問題展開研究,對改善高速公路交通狀態(tài)估計系統(tǒng)應(yīng)用效果具有實際意義。論文以高速公路基本路段為研究對象,運用不確定性方法來估計高速公路基本路段的地點和路段的交通狀態(tài)。在地點交通狀態(tài)估計中,以交通流參數(shù)樣本點空間分布的不均衡性分析為突破口,重點解決樣本數(shù)量不均衡性對交通狀態(tài)聚類結(jié)果影響;在路段交通狀態(tài)估計中,重點針對交通狀態(tài)估計的不確定性問題,采用多源數(shù)據(jù)融合的方法加以解決。主要研究內(nèi)容包括:①高速公路基本路段交通流參數(shù)特性的分析。首先,對地點交通流參數(shù)的時間相關(guān)性和樣本點空間分布的不均衡性進(jìn)行了分析;然后,對路段交通流參數(shù)估計交通狀態(tài)時存在的不確定性進(jìn)行了分析,為后面地點和路段交通狀態(tài)估計模型的建立奠定了基礎(chǔ)。②基于特征參數(shù)加權(quán)GEFCM算法的高速公路地點交通狀態(tài)估計模型的建立。針對傳統(tǒng)模糊聚類算法在交通狀態(tài)估計時存在的不足,結(jié)合樣本分布的不均衡性以及不同特征參數(shù)對于聚類影響權(quán)重的差異性,建立特征參數(shù)加權(quán)GEFCM算法的地點交通狀態(tài)估計模型,并通過主成分分析法確定了不同特征參數(shù)在模型中的權(quán)重值,實驗表明本文方法具有更好可靠性與適應(yīng)性。③基于動態(tài)貝葉斯網(wǎng)絡(luò)多源數(shù)據(jù)融合的高速公路路段交通狀態(tài)估計模型的建立。針對采用路段相對密度和路段平均行程時間估計交通狀態(tài)時存在的不確定性問題,引入動態(tài)貝葉斯網(wǎng)絡(luò),在研究選取相對密度、平均行程時間和交通狀態(tài)為節(jié)點變量的基礎(chǔ)上,確定了網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu),并最終研究建立了用于狀態(tài)估計的動態(tài)貝葉斯網(wǎng)絡(luò)模型,實驗表明本文方法具有更好的可靠性。最后,對系統(tǒng)進(jìn)行了設(shè)計與實現(xiàn),并應(yīng)用于渝武高速公路部分路段的地點和路段交通狀態(tài)估計。結(jié)果表明:本文方法對地點和路段交通狀態(tài)的估計結(jié)果相比較對比的方法均具有更高的擁擠判別率和更低的擁擠誤判率。
[Abstract]:The accurate estimation of highway traffic condition is the key to grasp the traffic operation of expressway. The single traffic flow parameter can only indirectly reflect the actual traffic operation condition. Cluster analysis according to many different traffic flow parameters is a typical method of location traffic state estimation, but the clustering results are very sensitive to the number of samples. On the other hand, at present, the estimation of the traffic state of the road section generally only considers the travel time or the travel speed, because of the limitation of the data acquisition means, there is a certain uncertainty in the estimated traffic state. The research on these problems is of practical significance to improve the application effect of highway traffic state estimation system. In this paper, the basic section of expressway is taken as the research object, and the uncertainty method is used to estimate the location and traffic state of the basic section of expressway. In the traffic state estimation of the location, the uneven analysis of the spatial distribution of the sample points of the traffic flow parameters is taken as the breakthrough point, and the influence of the imbalance of the number of samples on the clustering results of the traffic state is solved. In the traffic state estimation of the road section, the uncertainty problem of the traffic state estimation is solved by the method of multi-source data fusion. The main research contents are as follows: 1. Analysis of traffic flow parameters in basic sections of expressway. Firstly, the temporal correlation of location traffic flow parameters and the imbalance of spatial distribution of sample points are analyzed. Then, the uncertainty of road section traffic flow parameters estimation traffic state is analyzed, which lays a foundation for the establishment of traffic state estimation model of rear location and road section. (2) the establishment of highway location traffic state estimation model based on characteristic parameter weighted GEFCM algorithm. In view of the shortcomings of traditional fuzzy clustering algorithm in traffic state estimation, combined with the imbalance of sample distribution and the difference of influence weight of different characteristic parameters on clustering, the location traffic state estimation model of feature parameter weighted GEFCM algorithm is established, and the weight values of different characteristic parameters in the model are determined by principal component analysis (PCA). The experimental results show that the proposed method has better reliability and adaptability. (3) the traffic state estimation model of expressway section based on dynamic Bayesian network multi-source data fusion is established. In order to solve the uncertainty problem of using relative density of road section and average travel time of road section to estimate traffic state, dynamic Bayesian network is introduced. On the basis of selecting relative density, average travel time and traffic state as node variables, the topological structure of the network is determined, and finally a dynamic Bayesian network model for state estimation is established. The experimental results show that the proposed method has better reliability. Finally, the system is designed and implemented and applied to the location and traffic state estimation of some sections of Yuwu Expressway. The results show that the proposed method has higher congestion discrimination rate and lower congestion misjudgment rate compared with the estimation results of location and road traffic state.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U491

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