在這資訊科技爆炸的時代，近年來隨著有GPS記錄的裝置增加，例如智慧型手機、智慧手錶，軌跡數據日益增加，引起儲存、傳輸、分析的問題，移動物體軌跡無庸置疑成為一個非常火熱的議題，而其中如何透過軌跡壓縮演算法去保留有用的軌跡點，除去不必要的軌跡點，是個需要去探討的重要問題。
在壓縮軌跡的問題中，原始軌跡和壓縮後的軌跡，兩者間有誤差率與壓縮率的關係，可以去評測出壓縮演算法的性能。本篇我們提出一種壓縮軌跡方法稱為TP，改進原有的SQUISH-E演算法，使用三階段去過濾原始軌跡，第一階段使用滑動視窗做首次的過濾，留下比目標壓縮率還多的點，第二階段利用第一階段過濾後的點，計算出候選轉折點的值，用以找出重要特徵的轉折點，而計算值當中，使用值近似角度值的功能，以利加快計算速度，第三階段有兩個可選擇的計算值，分別以值或SED值，用於兩種不同方向的應用層面。
為了證明在應用層面上，改善原有方法的效能比有更好的效能，分別使用四項誤差評測值去實驗，第一是SED Error，考慮時空性的改進，應用在動物的遷徙狀況，第二是Heading Error，應用在對應地圖中交通運輸分析，第三是Speed Error，應用在監控城市中的物體速度的行為，第四是ILDSED，代表壓縮軌跡演算法可以讓原始軌跡平均壓縮的程度。
最後，過本篇提出的軌跡壓縮演算法，在高壓縮率的前提下，所壓縮出來的軌跡，不但省下大量的儲存和傳輸成本，也能因應更全面的分析應用，提供相對資訊的軌跡，以便軌跡資料挖掘處裡流程上可以更快且明確分析預測軌跡行為。

With the increase of devices with GPS records, such as smart phones, smart watches, the problems of storage, transmission and analysis become more and more important. Since compression algorithm is an effective way to preserve useful trajectory points and remove unnecessary points, it has become a significant issue to explore.
In the compression trajectory problem, the error rate and the compression rate that can be computed by the original trajectory and the compressed trajectory are used to evaluate the performance of the compression algorithm. In this paper, we propose a compression trajectory method called TP, which improves the original SQUISH-E algorithm by using three stages to filter the original trajectory. The first stage uses a bigger sliding window to remain more points than the number that indicated by the target compression rate. In the second stage, the filtered points after the first stage are used to calculate the values μ and find out the promising turning points. For the computation in this stage, we propose the value ϕ to accelerate the process of calculation. In the third stage, there are two options, the values ϕ and SED values, according to the analysis of different goals.
Finally, the trajectory compression algorithm proposed in this article, under the high compression ratio, not only saves a lot of storages and transmission costs, but also provides relative information trajectory in response to more comprehensive analysis and application which enable the process of trajectory mining more quickly and clearly.

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