【摘要】：軌跡數(shù)據(jù)中蘊含著豐富的空間信息及時間信息,具有進一步分析、處理及利用的價值。隨著定位技術的迅速發(fā)展和日漸普及,使得采集移動對象的軌跡數(shù)據(jù)變得容易。然而,隨著軌跡采集終端的大量使用,原始軌跡數(shù)據(jù)量急劇增長,且冗余嚴重,給軌跡數(shù)據(jù)的存儲、傳輸及進一步分析處理帶來了極大的壓力。因此,有效剔除原始軌跡中的冗余信息,實現(xiàn)軌跡數(shù)據(jù)壓縮具有重要的意義。本文針對離線軌跡數(shù)據(jù),綜合考慮軌跡數(shù)據(jù)的速度、方向及位置特征,在保證原有軌跡重要信息不丟失的基礎上,為實現(xiàn)各級別壓縮率下對復雜軌跡數(shù)據(jù)的壓縮,提出三種離線軌跡數(shù)據(jù)壓縮算法:(1)基于速度特性的角度偏移量軌跡數(shù)據(jù)壓縮算法。該算法根據(jù)軌跡數(shù)據(jù)的速度特征和方向特征,通過比較預設的速度閾值和角度閾值刪除冗余,較好地保留了原始軌跡中速度變化較大及方向發(fā)生改變的軌跡點;(2)基于網格的軌跡數(shù)據(jù)壓縮算法。該算法使用網格覆蓋整條軌跡,通過計算各網格內最早軌跡點與其他軌跡點的時間差,剔除小于預設時間閾值的軌跡點,并利用弗里曼鏈碼的編碼方式進一步壓縮相鄰網格內方向信息冗余的軌跡點;(3)基于局部加權線性回歸的軌跡數(shù)據(jù)壓縮算法。該算法利用線性變化的曲線模擬非線性變化的軌跡,采用局部加權線性回歸算法對原始軌跡進行曲線擬合,去除原始軌跡上偏離擬合曲線小于給定距離閾值的軌跡點。本文最后對三個算法分別進行了驗證及誤差比較與分析,結果表明,本文算法是可行的、有效的。
[Abstract]:The trace data contains abundant spatial information and time information, which has the value of further analysis, processing and utilization. With the rapid development and popularity of positioning technology, it is easy to collect track data of moving objects. However, with the extensive use of trajectory acquisition terminals, the original trajectory data volume increases rapidly, and the redundancy is serious, which brings great pressure to the storage, transmission and further analysis and processing of trajectory data. Therefore, it is of great significance to effectively eliminate redundant information from the original trajectory and achieve trajectory data compression. In this paper, we consider the velocity, direction and position characteristics of the off-line trajectory data synthetically, on the basis of ensuring that the important information of the original trajectory is not lost, in order to realize the compression of the complex trajectory data under the compression ratio of each level. Three off-line trajectory data compression algorithms are proposed: (1) angular offset trajectory data compression algorithm based on velocity characteristics. According to the velocity and direction characteristics of the trajectory data, the redundancy is removed by comparing the preset velocity threshold and the angle threshold. The trajectory points in the original trajectory are well preserved. (2) the grid-based trajectory data compression algorithm. The algorithm uses mesh to cover the whole track, and by calculating the time difference between the earliest trace points and other locus points in each grid, the trajectory points which are less than the preset time threshold are eliminated. The Freeman chain code is used to further compress the redundant locus points of direction information in adjacent grids. (3) the locus data compression algorithm based on local weighted linear regression. The algorithm uses the curve of linear variation to simulate the trajectory of nonlinear change, and uses the local weighted linear regression algorithm to fit the original trajectory, and removes the trajectory points on the original trajectory that deviate from the fitting curve less than the threshold of the given distance. Finally, the three algorithms are verified and the error is compared and analyzed. The results show that the algorithm is feasible and effective.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P208

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本文編號：2172002