本文選題：地圖匹配 + 交通流��； 參考：《東南大學》2015年碩士論文

【摘要】：智慧城市是充分利用城市各行各業(yè)的數(shù)據(jù),采用信息技術綜合處理分析,為城市各行各業(yè)提供智能服務,而城市移動模式是智慧城市的基礎性課題。本文基于南京七千多輛出租車兩個月的GPS數(shù)據(jù),采用數(shù)據(jù)分析的方法對南京城區(qū)移動對象的移動模式進行了研究。文章主要在地圖匹配(Map Matching, MM)、城市車流模式、城市人群移動模式三個方面展開工作。城市車流模式聚焦在日常交通流量和常發(fā)擁堵路段的時空分布,而城市人群移動模式則重點關注城市人群出行的距離分布、人群出行熱點以及人群流向的時空變化。文中本地化的結論為城市交通規(guī)劃、區(qū)域規(guī)劃和公共衛(wèi)生建設提供了指導性知識。文中首先綜述了城市移動模式的研究背景和研究現(xiàn)狀,然后簡介了開放街道地圖(Open Street Map, OSM),并詳細分析了出租車GPS數(shù)據(jù)的噪聲。過濾噪聲之后,本文使用局部ST-Matching算法將出租車GPS數(shù)據(jù)匹配到OSM電子地圖上。之后對南京不同等級道路在工作日和非工作日的交通流量和行車速度進行統(tǒng)計分析,發(fā)現(xiàn)單一道路等級在不同時間段上行車速度服從正態(tài)分布。本文以此為基礎,建立路段擁堵分值模型,引入常發(fā)擁堵指標之后完成對南京常發(fā)擁堵點的提取。發(fā)現(xiàn)市中心的中山南路、內環(huán)線、二橋南路和大橋高速等路段是城市路網的瓶頸,經常發(fā)生擁堵。其次,本文對南京城市人群打車出行的距離和不同時間段打車出行的人數(shù)進行統(tǒng)計分析,驗證了打車出行,人群移動距離服從冪率的結論。并使用基于模塊度的層次聚類算法對南京城市人群出行熱點進行分析,發(fā)現(xiàn)在早晚高峰人群的出行熱點主要集中在中華門、安德門、邁皋橋地鐵站等主要的交通換乘點和大型住宅區(qū),而南京火車站和江蘇省人民醫(yī)院等大型醫(yī)院在一天的大多數(shù)時間內都是人們出行的熱點,凌晨時分1912街區(qū)是人們主要的出行熱點。最后,本文采用柵格統(tǒng)計的方法,對城市人流動向進行分析,發(fā)現(xiàn)在不同時段人群流向表現(xiàn)出很大不同,早高峰主要是住宅區(qū)到車站,晚高峰則剛好相反,表現(xiàn)出較好的對稱性。在其他時間段,則語義呈現(xiàn)多樣性。
[Abstract]:The intelligent city is to make full use of the data of various industries in the city, and to adopt the information technology to deal with the analysis synthetically, to provide the intelligent service for the various industries of the city, and the urban mobile mode is the basic subject of the intelligent city. Based on the GPS data of more than 7,000 taxis in Nanjing for two months, this paper studies the moving pattern of moving objects in Nanjing urban area by using the method of data analysis. This paper mainly focuses on map matching, MMX, urban traffic flow and urban crowd movement. The urban traffic flow mode focuses on the spatial and temporal distribution of daily traffic flow and common congested sections, while the urban crowd movement mode focuses on the distance distribution of urban population travel, crowd travel hot spots and the spatio-temporal change of crowd flow direction. The conclusion of localization provides guiding knowledge for urban transportation planning, regional planning and public health construction. In this paper, the research background and present situation of urban mobile mode are summarized, then the open Street map is introduced, and the noise of taxi GPS data is analyzed in detail. After filtering noise, this paper uses local ST-Matching algorithm to match taxi GPS data to OSM electronic map. Then the traffic flow and driving speed of different grades of roads in Nanjing on weekdays and non-workdays are statistically analyzed and it is found that the single grade of roads in different periods of time from normal distribution of driving speed. Based on this model, the model of traffic congestion score is established, and the normal congestion index is introduced to complete the extraction of normal congestion points in Nanjing. It is found that Zhongshan South Road, Inner Ring Road, second Bridge South Road and Bridge Expressway are the bottleneck of urban road network, and congestion often occurs. Secondly, this paper makes a statistical analysis on the distance of the urban population in Nanjing and the number of people traveling in different time periods, and verifies the conclusion that the power ratio of the driving distance of the crowd is the same as that of the ride-hailing trip and the crowd moving distance. And using the hierarchical clustering algorithm based on modular degree to analyze the travel hot spots of Nanjing city crowd, it is found that the hot spots in the morning and evening rush crowd mainly focus on the Zhonghua Gate, the Anderman Gate. Megaoqiao subway stations and other major transportation and residential areas, while Nanjing Railway Station and Jiangsu Provincial people's Hospital and other large hospitals during most of the day is a hot spot for people to travel. The 1912 block in the early hours of the morning is the main hot spot for people to travel. Finally, by using the method of grid statistics, this paper analyzes the trend of urban passenger flow. It is found that the flow of people in different periods is very different. The early rush hour is mainly from residential area to the station, while the late rush hour is just the opposite. Show good symmetry. In other time periods, semantic diversity is present.
【學位授予單位】：東南大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TU984.191;TP311.13

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本文編號：1878274