本文選題：異常軌跡檢測 + 地圖網(wǎng)格化�。� 參考：《武漢理工大學(xué)》2015年碩士論文

【摘要】：隨著傳感技術(shù)、通訊技術(shù)、儲存技術(shù)和計(jì)算能力的發(fā)展,越來越多出租車裝配了GPS傳感儀,在日常運(yùn)營中產(chǎn)生大量位置數(shù)據(jù),為我們提供了很好的機(jī)會去分析和挖掘有價(jià)值的信息。本文主要將數(shù)據(jù)應(yīng)用于探測出租車異常行為,目標(biāo)是自動識別車輛異常行駛軌跡,判斷司機(jī)是否存在故意繞路行為。既能保障乘客利益也有助于規(guī)范出租車服務(wù),具有現(xiàn)實(shí)意義。本文主要研究工作如下:(1)為實(shí)現(xiàn)檢測出租車異常軌跡的目標(biāo),本文先給出軌跡等相關(guān)定義,設(shè)計(jì)了檢測系統(tǒng)總體框架,按模塊說明各節(jié)點(diǎn)作用,并從離線和在線處理階段分析了數(shù)據(jù)處理流程。(2)為解決軌跡網(wǎng)格化后存在的不連續(xù)問題,本文提出AE-AUG(Augmented method of angle and edge)軌跡補(bǔ)全算法,可快速求出一條通路連接兩不相鄰網(wǎng)格。(3)對異常軌跡檢測核心問題,本文提出s-iBOAT(iBOAT with State)算法,該算法通過為軌跡加入最新檢測點(diǎn)位置下標(biāo),改進(jìn)基于孤立特性的在線異常軌跡檢測算法iBOAT(Isolation-Based Online Anomalous Trajectory Detection),簡化查找相似軌跡處理步驟,提升算法效率。(4)利用Hadoop平臺處理出租車GPS記錄生成歷史軌跡數(shù)據(jù),結(jié)合Bing Maps Tile System中的地圖網(wǎng)格算法和本文提出的AE-AUG、s-iBOAT算法,實(shí)現(xiàn)了一個(gè)基于Web前端技術(shù)的異常軌跡檢測系統(tǒng)。通過檢測系統(tǒng)測試了s-iBOAT算法異常子軌跡識別效果,實(shí)驗(yàn)結(jié)果良好符合理論分析。對相同起終點(diǎn)所有運(yùn)營軌跡進(jìn)行檢測,從總體檢測情況分析兩種異常軌跡形成的原因。其一是部分出租車司機(jī)經(jīng)驗(yàn)豐富,對該區(qū)域熟悉,選取了少數(shù)的便捷路徑導(dǎo)致識別異常。其二是司機(jī)為了攢取更多的運(yùn)營收益,載客時(shí)故意繞遠(yuǎn)路導(dǎo)致異常。研究異常判別閾值和網(wǎng)格大小對檢測靈敏度、誤判率、準(zhǔn)確率的影響,在測試實(shí)驗(yàn)條件下得出兩者最佳取值為0.1和153米。此外利用異常軌跡長度與總體歷史軌跡集平均長度對比情況修正檢測結(jié)果,實(shí)驗(yàn)表明可提高檢測準(zhǔn)度,更適用于探測現(xiàn)實(shí)出租車?yán)@路行為。對比檢測算法改進(jìn)前后的執(zhí)行效果,結(jié)果顯示s-iBOAT能夠保持異常子軌跡識別效果基本不變、整體軌跡檢測準(zhǔn)度相同的情況下提高運(yùn)行速度,減少響應(yīng)時(shí)間。
[Abstract]:With the development of sensing technology, communication technology, storage technology and computing ability, more and more taxis are equipped with GPS sensor, which produces a lot of position data in daily operation. It provides us with a good opportunity to analyze and mine valuable information. This paper mainly applies the data to detect the abnormal behavior of the taxi. The goal is to automatically identify the abnormal track of the vehicle and judge whether the driver has the behavior of deliberately detour. It is of practical significance to protect the interests of passengers as well as to standardize taxi service. The main research work of this paper is as follows: (1) in order to realize the goal of detecting the abnormal track of taxi, this paper first gives the definition of track and other related definitions, designs the overall framework of the detection system, and explains the role of each node according to the module. The data processing flow is analyzed from off-line and on-line processing stages. (2) in order to solve the discontinuous problem of trajectory gridding, an AE-AUG (Augmented method of angle and edge) trajectory complement algorithm is proposed in this paper. Two nonadjacent meshes can be quickly solved by a single path. (3) for the core problem of abnormal trajectory detection, this paper proposes s-iBOAT (iBOAT with State) algorithm, which subscribes the position of the latest detection point to the trajectory. The algorithm iBOAT (Isolation-Based online Anomalous Trajectory Detection) is improved to simplify the process of finding similar tracks and improve the efficiency of the algorithm. (4) the Hadoop platform is used to process taxi GPS records to generate historical track data. Combined with the map grid algorithm in Bing Maps Tile system and the AE-AUGCS-iBOAT algorithm proposed in this paper, an anomaly track detection system based on Web front-end technology is implemented. The detection system is used to test the detection effect of s-iBOAT algorithm. The experimental results are in good agreement with the theoretical analysis. The causes of the formation of the two abnormal trajectories are analyzed from the overall detection of all the operation tracks of the same starting and ending points. One is that some taxi drivers are experienced, familiar with the area, and select a few convenient paths to identify anomalies. The other is the driver in order to save more operating income, when carrying passengers deliberately detour caused abnormal. The effects of abnormal threshold and mesh size on detection sensitivity, error rate and accuracy are studied. The optimum values of them are 0.1 and 153 meters under the test conditions. In addition, by comparing the abnormal track length with the average length of the total historical track set, the experimental results show that the detection accuracy can be improved, and it is more suitable to detect the actual taxi detour behavior. The results show that s-iBOAT can keep the recognition effect of abnormal sub-trajectory unchanged and improve the running speed and reduce the response time when the whole track detection accuracy is the same.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U495

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