本文選題：城市交通 + 出行鏈　； 參考：《大連交通大學》2015年碩士論文

【摘要】：互聯(lián)網(wǎng)技術的變革、移動通信技術的應用、智能交通技術的成熟,為傳統(tǒng)的出行信息調(diào)查、出行行為研究、出行需求預測提供了新的思路。本文的研究目的,是希望建立一種方法,能夠有效地從利用智能手機定位及相關位置信息所采集到的出行軌跡數(shù)據(jù)中,提取出出行方式、活動類型等信息,從而提升居民出行調(diào)查的效率、降低調(diào)查過程中的主觀性、減少調(diào)查周期和費用,為城市交通規(guī)劃與管理提供數(shù)據(jù)支撐和決策支持。本文的研究結合了出行鏈、模式識別、被動式居民出行信息調(diào)查和手機位置服務(LBS)等理論和技術基礎。在研究活動鏈和出行鏈結構的基礎上,建立了出行活動鏈模式,劃分了出行過程子模式和活動過程子模式,并分析了其模式特征,研究了出行活動鏈模式和出行軌跡之間的對應關系;利用手機定位和傳感器模塊,結合基于LBS的豐富位置信息的采集思想,構建了出行軌跡數(shù)據(jù)的采集方法,并且應用了軌跡插值來補全軌跡中的缺失點,采用Kalman濾波來實現(xiàn)軌跡降噪,提出滑窗判別的方法將軌跡劃分成出行段和活動段;建立了出行過程子模式和活動過程子模式的特征向量,并給出了從出行軌跡參數(shù)向量中提取子模式特征向量的方法,采用頻率分布圖和F-score的方法對特征向量在兩兩分類間的可分性進行了定性和定量的分析,進而采用了決策樹、BP網(wǎng)絡、RBF網(wǎng)絡和支持向量機等分類器對樣本數(shù)據(jù)進行識別。最后以大連市為背景實地采集了出行軌跡數(shù)據(jù),并利用這些數(shù)據(jù)應用上述方法進行了實證研究,對于數(shù)據(jù)補全、濾波、分段、識別等方法的效果進行了評價,結果表明本文所應用的方法對于利用LBS軌跡來進行出行活動鏈模式識別能夠取得較好的效果。
[Abstract]:The innovation of Internet technology, the application of mobile communication technology and the maturity of intelligent transportation technology provide a new way of thinking for traditional travel information investigation, travel behavior research and travel demand prediction. The purpose of this paper is to establish a method, which can extract the information of travel mode, activity type and so on from the travel path data collected by using the location information of smart phone and related location information. In order to improve the efficiency of residents' travel survey, reduce the subjectivity of the survey process, reduce the investigation cycle and costs, and provide data support and decision support for urban traffic planning and management. This paper combines the theory and technology of trip chain, pattern recognition, passive travel information survey and mobile location service (LBS). On the basis of studying the structure of activity chain and trip chain, this paper establishes the travel activity chain pattern, divides the travel process sub-pattern and the activity process sub-pattern, and analyzes its pattern characteristics. The corresponding relationship between trip activity chain mode and trip trajectory is studied, and the acquisition method of trip trajectory data is constructed by using mobile phone location and sensor module, combined with the idea of collecting abundant location information based on LBS. The path interpolation is used to compensate the missing points in the whole trajectory, and the Kalman filter is used to reduce the trajectory noise. A sliding window discriminating method is proposed to divide the trajectory into travel segment and active segment. The Eigenvectors of travel process subpattern and activity process subpattern are established, and the method of extracting subpattern eigenvector from trip path parameter vector is given. The qualitative and quantitative analysis of the separability of feature vectors between pairwise classification is carried out by means of frequency distribution map and F-score, and then the classifiers such as decision tree BP neural network and support vector machine are used to identify the sample data. Finally, taking Dalian as the background, we collect the travel track data in the field, and use these data to carry on the empirical research with the above methods, and evaluate the effect of the data complement, filtering, segmentation, recognition and so on. The results show that the method proposed in this paper can achieve good results for pattern recognition of trip activity chain using LBS locus.
【學位授予單位】：大連交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U495

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本文編號：1953849