本文關(guān)鍵詞：基于出租車軌跡數(shù)據(jù)的載客情況可視化分析　出處：《浙江工業(yè)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 出租車軌跡 上下客熱點(diǎn) 軌跡聚類 推薦軌跡 可視化原型

【摘要】：城市化促進(jìn)了社會(huì)經(jīng)濟(jì)的發(fā)展,改善了人們的生活水平,同時(shí)也帶來了諸如道路擁堵、出行耗時(shí)長等交通問題。為了能夠了解出行情況,緩解交通問題,交通管理部門將越來越多諸如GPS的設(shè)備安裝在出租車上。通過這些設(shè)備的信息采集,形成大量的出租車軌跡數(shù)據(jù)。出租車軌跡數(shù)據(jù)隱含了大量知識(shí)能夠幫助我們分析人們出行信息,達(dá)到優(yōu)化交通、改善路況的目的。然而出租車軌跡數(shù)據(jù)本身量大且相對(duì)復(fù)雜,僅僅依靠數(shù)據(jù)本身很難讓人直觀了解,可視分析技術(shù)為我們提供了一種有效展示和分析數(shù)據(jù)的方法。本文中我們利用出租車軌跡數(shù)據(jù)來分析人們出行的上下客熱點(diǎn),幫助出租車司機(jī)結(jié)合道路交通情況找出較優(yōu)的載客行駛軌跡,同時(shí)開發(fā)一個(gè)可視化原型系統(tǒng)來支持可視化分析。本文的研究內(nèi)容主要包括以下幾個(gè)方面：(1)出租車軌跡數(shù)據(jù)預(yù)處理。通過設(shè)定軌跡提取的規(guī)則和方法,對(duì)軌跡數(shù)據(jù)中存在的跳變點(diǎn)軌跡、停車軌跡、過短軌跡進(jìn)行過濾,提取得到出租車軌跡。(2)上下客熱點(diǎn)提取和分析。根據(jù)出租車軌跡提取得到上下客點(diǎn),在此基礎(chǔ)之上設(shè)計(jì)了一種改進(jìn)的上下客熱點(diǎn)生成聚類算法-—GBADBSCAN來生成上下客熱點(diǎn)。采用基于上下客熱點(diǎn)聚類圖標(biāo)方法對(duì)不同時(shí)段上下客熱點(diǎn)的分布進(jìn)行可視化分析。(3)出租車推薦行駛軌跡的獲取。本文首先通過基于起終點(diǎn)的相似軌跡方法來將所有軌跡劃分成具有相近起點(diǎn)和終點(diǎn)的出租車軌跡子集,接著采用基于密度的ε距離軌跡聚類算法來對(duì)軌跡子集聚類,找出不同上下客熱點(diǎn)間的候選行駛軌跡。最后結(jié)合候選軌跡的行駛時(shí)間、速度、距離以及載到乘客的可能性來設(shè)置權(quán)值,根據(jù)帶權(quán)軌跡樹來尋找空載出租車到就近上客熱點(diǎn)載客的最優(yōu)行駛軌跡。(4)構(gòu)建可視化原型系統(tǒng)。本文開發(fā)了地圖、時(shí)間、平行坐標(biāo)、控制臺(tái)以及路徑導(dǎo)航描述組件這5種組件來構(gòu)建可視化原型系統(tǒng),實(shí)現(xiàn)出租車軌跡數(shù)據(jù)的可視化分析。
[Abstract]:Urbanization has promoted the development of social economy, improved people's living standards, but also brought traffic problems such as road congestion, long travel time, in order to understand the travel situation and alleviate the traffic problems. More and more devices, such as GPS, are being installed in taxis by traffic authorities. A large number of taxi trajectory data are formed. Taxi trajectory data implied a lot of knowledge can help us to analyze people travel information to achieve traffic optimization. The purpose of improving road conditions. However, taxi track data itself is large and relatively complex, relying on the data itself is difficult to intuitively understand. Visual analysis technology provides us with an effective method to display and analyze data. In this paper, we use taxi trajectory data to analyze the hot spots of people travelling. Help taxi drivers to find out the best path to carry passengers in combination with road traffic conditions. At the same time, a visual prototype system is developed to support visual analysis. The research content of this paper mainly includes the following aspects: 1) the pretreatment of taxi trajectory data. By setting the rules and methods of trajectory extraction. The jump point locus, parking track, too short track in the track data are filtered, the taxi track is extracted and the hot spot is extracted and analyzed. According to the taxi trajectory extraction, the upper and lower passenger points are obtained. On this basis, an improved clustering algorithm named GBADBSCAN is designed to generate hot spots. For visual analysis. 3) the acquisition of taxi recommended trajectory. Firstly, this paper divides all the tracks into subsets of taxi tracks with similar starting point and end point by using the similar trajectory method based on the starting end point. Then the 蔚 distance trajectory clustering algorithm based on density is used to cluster the trajectory subset to find out the candidate trajectory between different hot spots. Finally, combining the travel time and speed of the candidate trajectory. Distance and the possibility of carrying a passenger to set the weight value. Based on the weighted track tree to find the optimal driving path of no-load taxi to the nearest hot spot, a visual prototype system is constructed. In this paper, map, time, parallel coordinates are developed. The 5 components of the console and the path navigation description component are used to construct the visual prototype system to realize the visual analysis of the taxi track data.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495;TP311.13

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本文編號(hào)：1386566