本文選題：位置　切入點：乘車信息　出處：《山東大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在交通擁擠日趨嚴(yán)重、交通污染與交通事故等問題日益突出的背景下,優(yōu)先發(fā)展公共交通是緩解大中城市交通問題最有效的手段之一。公共交通的發(fā)展同時可以促進城市經(jīng)濟的發(fā)展,公共交通已成為城市客運交通系統(tǒng)的主體,成為國家在基礎(chǔ)建設(shè)領(lǐng)域中重點支持發(fā)展的產(chǎn)業(yè)之一。傳統(tǒng)的公交客流統(tǒng)計方式存在調(diào)查步驟繁瑣、成本高、精確度低等一種或多種弊端,本文通過對地面常規(guī)公交自動售票系統(tǒng)收集的數(shù)據(jù)進行數(shù)據(jù)挖掘,對刷卡信息進行統(tǒng)計分析,得到公交客流出行特性和規(guī)律,更加高效、精確地預(yù)測公交客流,并有效地利用了現(xiàn)有交通信息資源。本文在查閱相關(guān)研究文獻的基礎(chǔ)上,從站點客流估計、客流特性分析和交通短時預(yù)測方法三個方面對國內(nèi)外研究成果進行綜述,總結(jié)對比現(xiàn)有研究成果,指出本論文在此基礎(chǔ)上的研究重點。首先,對公交自動售票系統(tǒng)進行數(shù)據(jù)特征分析,根據(jù)數(shù)據(jù)特征和客流估計需求對原始數(shù)據(jù)進行數(shù)據(jù)篩選和數(shù)據(jù)剔除預(yù)處理,對刷卡記錄進行聚類分析,融合自動售票系統(tǒng)數(shù)據(jù)、GPS數(shù)據(jù)、靜態(tài)路網(wǎng)和公交調(diào)度信息等多源數(shù)據(jù),進行位置和乘車信息的時間匹配,得到匹配站點,估計站點客流。其次,基于多源數(shù)據(jù)融合的站點匹配算法,以濟南市智能公共交通系統(tǒng)數(shù)據(jù)為例,從客流的方向不均衡性和供需平衡性對線路客流進行特性分析,從客流的通勤特性和時間不均衡性對站點客流進行特性分析,針對站點的通勤和時間特性選取線路的特征站點,為客流預(yù)測提供案例站點。最后,對影響站點客流的因素進行變量分析,選取時間序列模型和改進BP神經(jīng)網(wǎng)絡(luò)模型對特征站點進行客流預(yù)測,比較不同預(yù)測模型對不同通勤類型站點客流預(yù)測的精度,得出站點客流預(yù)測的結(jié)論。
[Abstract]:Against the background of increasingly serious traffic congestion, traffic pollution and traffic accidents, Giving priority to the development of public transport is one of the most effective means to alleviate the traffic problems in large and medium-sized cities. The development of public transport can also promote the development of urban economy, and public transport has become the main body of urban passenger transport system. It has become one of the industries that support the development of the country in the field of infrastructure construction. The traditional way of bus passenger flow statistics has one or more disadvantages, such as tedious investigation steps, high cost, low precision and so on. In this paper, the data collected by the ground bus automatic ticket selling system are mined, and the information of credit card is statistically analyzed, and the travel characteristics and rules of public transport flow are obtained, so as to predict the bus passenger flow more efficiently and accurately. On the basis of consulting relevant research literature, this paper summarizes the domestic and foreign research achievements from three aspects: station passenger flow estimation, passenger flow characteristic analysis and traffic short-term forecasting method. This paper summarizes and compares the existing research results, and points out the key points of this paper. Firstly, the paper analyzes the data characteristics of the bus automatic ticket system. According to the characteristics of data and the demand of passenger flow estimation, the original data are filtered and pre-processed, the credit card records are clustered and analyzed, and the GPS data, static road network and bus dispatch information are fused, and the multi-source data, such as GPS data, static road network and bus dispatch information, are fused. Matching the location with the time of the ride information, getting the matching station, estimating the passenger flow of the station. Secondly, the station matching algorithm based on multi-source data fusion, taking Jinan intelligent public transportation system data as an example, The characteristics of line passenger flow are analyzed from the disequilibrium of passenger flow direction and the balance of supply and demand, the characteristic analysis of station passenger flow is carried out from the characteristics of commuting and time imbalance of passenger flow, and the characteristic station of line is selected according to the commuting and time characteristics of the station. Finally, the factors influencing the passenger flow are analyzed, and the time series model and the improved BP neural network model are selected to forecast the passenger flow of the characteristic stations. This paper compares the accuracy of different forecasting models for passenger flow of different commuting stations, and draws the conclusion of forecasting passenger flow at different stations.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U491.17

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