發(fā)布時間：2018-03-10 21:08

  本文選題：城市道路　切入點：多源數(shù)據(jù)融合　出處：《浙江工業(yè)大學》2014年碩士論文　論文類型：學位論文

【摘要】：隨著城市機動車保有量的迅猛增加,國內大部分城市尤其是特大城市的交通擁堵狀況日益嚴峻。城市道路的擁堵情況嚴重影響著居民的日常工作和生活。基礎道路建設受到諸多條件限制發(fā)展緩慢,智慧交通作為一種治理交通擁堵的新方法,已成為交通管理部門的工作重點。城市道路上安裝的各種傳感器每天能夠采集大量交通數(shù)據(jù)。如何利用這些數(shù)據(jù)制定有針對性的交通策略從而緩解城市交通擁堵,成為了研究者們關注的重點。交通擁堵評價不僅是道路交通服務水平的重要依據(jù),也是交通管理與控制的前提。準確地評價交通擁堵狀態(tài),對道路擁堵預測、交通誘導以及最佳路徑規(guī)劃均有非常重要的意義。但由于實際道路交通數(shù)據(jù)獲取難度較大,信息共享程度較低,大多數(shù)研究工作還停留在理論建模與仿真階段。因此對實際道路的交通數(shù)據(jù)分析還有較大的提升空間。本文基于實際道路的大量交通數(shù)據(jù)(卡口數(shù)據(jù)、微波數(shù)據(jù)、GPS數(shù)據(jù))對交通擁堵狀態(tài)的評價與預測方法進行了創(chuàng)新性研究,主要取得了以下三方面的研究成果：(1)對道路上常見的多種傳感器采集到的數(shù)據(jù)進行詳細分析,設計了一種車牌Hash算法用于去除冗余數(shù)據(jù)。針對各種傳感器的優(yōu)缺點,同時結合實際道路上采集的數(shù)據(jù)質量,提出了一種多源交通數(shù)據(jù)融合的方法,有效的修正了原始數(shù)據(jù)中的奇異數(shù)據(jù)。(2)對實際交通數(shù)據(jù)進行擁堵狀態(tài)評價。首先使用數(shù)據(jù)挖掘中常用的K-means聚類方法,依據(jù)聚類結果得出交通擁堵狀態(tài)評價方法。擁堵狀態(tài)是交通狀態(tài)分析的重點,但由于擁堵狀態(tài)在交通狀態(tài)中所占比重較低,K-means聚類方法不能有效的將擁堵狀態(tài)劃分出來,針對這一問題提出了一種基于密度的交通擁堵評價方法,該方法可以較好的劃分出擁堵狀態(tài)。(3)對短時交通擁堵狀態(tài)進行預測,針對一階馬爾可夫模型預測交通擁堵狀態(tài),存在預測擁堵狀態(tài)滯后的問題,提出了一種高階馬爾可夫模型的短時交通擁堵狀態(tài)預測方法。該方法對交通擁堵狀態(tài)的預測準確度有了一定提升達到92.7%,并且有效的消除了預測滯后問題。
[Abstract]:With the rapid increase in the number of motor vehicles in cities, The traffic jams in most cities in China, especially in mega-cities, are becoming increasingly serious. The congestion of urban roads seriously affects the daily work and daily life of residents, and the construction of basic roads is restricted by many conditions and develops slowly. Intelligent traffic as a new way to deal with traffic congestion, The sensors installed on urban roads can collect a large amount of traffic data every day. How to use these data to develop targeted traffic strategies to ease urban traffic congestion, Traffic congestion evaluation is not only the important basis of road traffic service level, but also the premise of traffic management and control. Traffic guidance and optimal path planning are of great significance. However, because of the difficulty of obtaining the actual road traffic data, the degree of information sharing is low. Most of the research work is still in the stage of theoretical modeling and simulation. Therefore, there is still much room for improving the traffic data analysis of the actual road. This paper is based on a large number of traffic data (bayonet data) of the actual road. Microwave data and GPS data) has carried on the innovative research to the traffic jam condition appraisal and the forecast method, has obtained the following three research achievements mainly: 1) to carry on the detailed analysis to the road common many kinds of sensors to collect the data, A license plate Hash algorithm is designed to remove redundant data. According to the advantages and disadvantages of various sensors and the quality of the data collected on the actual road, a multi-source traffic data fusion method is proposed. It effectively corrects the singular data in the original data and evaluates the traffic congestion. Firstly, K-means clustering method is used in data mining. According to the result of clustering, the evaluation method of traffic congestion state is obtained. Congestion state is the key point of traffic state analysis, but the K-means clustering method can not effectively divide congestion state because of the low proportion of congestion state in traffic state. In order to solve this problem, a density-based traffic congestion evaluation method is proposed, which can be used to predict the short-term traffic congestion, and the first-order Markov model can be used to predict the traffic congestion. There is a problem of predicting the lag of congestion, This paper presents a high order Markov model for short time traffic congestion prediction, which improves the accuracy of traffic congestion prediction to 92.7%, and effectively eliminates the problem of prediction lag.
【學位授予單位】：浙江工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U495

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本文編號：1595077