本文關(guān)鍵詞： 出租車(chē)GPS數(shù)據(jù) 交通擁堵 基于距離聚類(lèi) 馬爾可夫鏈預(yù)測(cè)　出處：《哈爾濱工業(yè)大學(xué)》2015年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：從全國(guó)乃至全世界的交通情況來(lái)看,隨著各個(gè)國(guó)家的機(jī)動(dòng)車(chē)數(shù)目的不斷增加,現(xiàn)有的公共交通條件將越來(lái)越不能達(dá)到機(jī)動(dòng)車(chē)容量對(duì)其的要求,從而將會(huì)導(dǎo)致各種交通系統(tǒng)的問(wèn)題,現(xiàn)如今交通擁堵這個(gè)問(wèn)題的普遍存在已經(jīng)是人們和社會(huì)都不能忽視的一個(gè)嚴(yán)峻問(wèn)題,而解決此問(wèn)題最好的辦法便是預(yù)防,預(yù)防交通擁堵問(wèn)題的出現(xiàn)并將交通擁堵扼殺在搖籃中。另外,本文通過(guò)分析交通系統(tǒng)中所存在的不足得知ITS系統(tǒng)中存在數(shù)據(jù)資源浪費(fèi)等問(wèn)題。針對(duì)交通擁堵問(wèn)題以及智能交通系統(tǒng)的數(shù)據(jù)資源浪費(fèi)問(wèn)題,本文首先利用智能交通系統(tǒng)所采集到的時(shí)空數(shù)據(jù)找出交通擁堵區(qū)域,其次根據(jù)交通擁堵區(qū)域的發(fā)現(xiàn)結(jié)果預(yù)測(cè)各個(gè)區(qū)域在之后的時(shí)間出現(xiàn)交通擁堵情況的幾率。本文利用的是智能交通系統(tǒng)中所采集到的數(shù)據(jù),選取了北京市出租車(chē)系統(tǒng)中的12,000輛出租車(chē)于2012年11月所反饋的GPS數(shù)據(jù)作為數(shù)據(jù)源。本文首先根據(jù)GPS定位系統(tǒng)原理所造成的數(shù)據(jù)噪聲以及本文實(shí)情將數(shù)據(jù)集進(jìn)行了清洗及時(shí)間片分割的操作。其次本文根據(jù)擁堵區(qū)域車(chē)輛密集的特點(diǎn)針對(duì)時(shí)間片數(shù)據(jù)集進(jìn)行基于距離聚類(lèi)分析(K-means、DBSCAN聚類(lèi)算法),并且將兩種聚類(lèi)方法的結(jié)果及性能作比較,本文最終根據(jù)比較結(jié)果的分析選取了DBSCAN聚類(lèi)算法來(lái)分析各個(gè)時(shí)間片的數(shù)據(jù)集。時(shí)間片數(shù)據(jù)集在進(jìn)行聚類(lèi)后,將算法得到聚類(lèi)結(jié)果與分割后的網(wǎng)格區(qū)域相匹配,并將區(qū)域分為聚類(lèi)數(shù)據(jù)簇內(nèi)部、聚類(lèi)數(shù)據(jù)簇邊緣、聚類(lèi)數(shù)據(jù)簇外部。然后本文將各個(gè)區(qū)域的車(chē)輛平均時(shí)速進(jìn)行計(jì)算,其中將聚類(lèi)數(shù)據(jù)簇外部車(chē)輛時(shí)速記為零。本文根據(jù)擁堵區(qū)域判定規(guī)則得到每個(gè)時(shí)間片上各個(gè)區(qū)域的交通擁堵情況,并將交通情況細(xì)分為“嚴(yán)重?fù)矶隆薄ⅰ爸卸葥矶隆�、“輕度擁堵”、“暢通”,最后將最終結(jié)果以矩陣的形式存儲(chǔ)于文本文件當(dāng)中。由于導(dǎo)致交通擁堵的原因較為復(fù)雜多變,所以出現(xiàn)交通擁堵的情況有一定的隨機(jī)性。交通擁堵?tīng)顩r可以看做是當(dāng)前時(shí)刻的狀態(tài)只依賴(lài)于上一個(gè)時(shí)刻的狀態(tài),所以本文根據(jù)馬爾可夫鏈鏈預(yù)測(cè)模型建立交通擁堵情況預(yù)測(cè)模型,將交通擁堵情況的發(fā)現(xiàn)結(jié)果分為訓(xùn)練集和驗(yàn)證集。其中利用訓(xùn)練集進(jìn)行基于Markov鏈的交通擁堵預(yù)測(cè),利用驗(yàn)證集來(lái)驗(yàn)證該模型的正確率。最后,本文將K-means、DBSCAN聚類(lèi)、基于Markov鏈預(yù)測(cè)模型作了實(shí)驗(yàn)及對(duì)比,并對(duì)預(yù)測(cè)模型正確率進(jìn)行驗(yàn)證統(tǒng)計(jì)。
[Abstract]:From the point of view of the traffic situation of the whole country and even the whole world, with the increasing number of motor vehicles in each country, the existing public transportation conditions will be more and more unable to meet the requirements for the capacity of motor vehicles. This will lead to a variety of traffic system problems. Nowadays, the prevalence of traffic congestion is a serious problem that people and society cannot ignore, and the best way to solve this problem is to prevent it. Prevent traffic congestion and stifle it in its cradle. In addition, By analyzing the shortcomings of traffic system, this paper finds out that there are some problems in ITS system, such as waste of data resources, traffic congestion problem and data resource waste problem of intelligent transportation system. In this paper, we first use the space-time data collected by the Intelligent Transportation system to find out the traffic congestion area. Secondly, the probability of traffic congestion in each area is predicted according to the results of the traffic congestion area. This paper uses the data collected in the intelligent transportation system. The GPS data of 12,000 taxis in Beijing taxi system on November 2012 is selected as the data source. Firstly, the data set is carried out according to the data noise caused by the principle of GPS positioning system and the fact of this paper. Secondly, based on the characteristics of vehicle density in congested areas, this paper makes a distance based clustering analysis of time slice data sets, and compares the results and performance of the two clustering methods. Finally, DBSCAN clustering algorithm is selected to analyze the data sets of each time slice according to the analysis of comparison results. After clustering, the clustering result is matched with the segmented grid area. The region is divided into cluster data cluster interior, cluster data cluster edge, cluster data cluster outside. Then, the average speed of vehicles in each region is calculated. In this paper, the traffic congestion of each region on each time slice is obtained according to the decision rules of congestion area. The traffic situation is subdivided into "severe congestion", "moderate congestion", "mild congestion", "smooth flow", and the final result is stored in a text file in matrix form. So traffic jams have a certain randomness. Traffic jams can be seen as the state of the current moment that only depends on the state of the previous moment. In this paper, the traffic congestion prediction model is established according to Markov chain forecasting model, and the traffic congestion detection results are divided into training set and verification set, in which traffic congestion prediction based on Markov chain is carried out by using training set. Finally, the K-means-DBSCAN clustering, based on the Markov chain prediction model, is tested and compared, and the accuracy of the prediction model is verified and counted.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：U491;O211.62

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本文編號(hào)：1536294