基於速度保留的軌跡簡化方式

Abstract

由於行動裝置的普及，軌跡資料能輕易地蒐集和使用在不同應用上，如地點預測、大眾運輸最佳化和旅遊路徑推薦等。然而，由於軌跡資料的物理限制，經常包含多餘的資料。這項發現促使軌跡資料簡化的研究，可在保留原始特徵如位置或方位的情形下降低資料大小。然而多數的方法卻忽略的速度特徵的重要性。因此，我們提出了自適應性的軌跡簡化方式 (ATS) 以同時保留軌跡的位置和速度特徵。ATS演算法首先將速度分群成各個範圍，並以此將軌跡分段成不同的速度保留段落，最後對各速度保留段落分別進行簡化，且不須預先設定簡化參數。在實驗上我們使用西班牙波多的計程車資料進行實驗，結果表示ATS演算法能在保留速度和位置特徵的情況下有效的簡化軌跡，並維持原本的簡化速度。

By the rise of mobile device, trajectory data could be easily collected and used in several applications, like destination prediction, public transportation optimization, and travel route recommendation. However, due to the spatio-temporal nature, raw trajectory data usually contain redundant movement information. This observation motivates the trajectory simplification approaches which discard some points with preserving some specific features, such as position features, direction features, and so on. Most of existing simplification ignore the importance of velocity feature. This paper proposes an adaptive trajectory approaches while taking the velocity feature into account. Specifically, the Adaptive Trajectory Simplification (ATS) algorithm is proposed, which not only preserves the position feature, but the velocity features from the given trajectories. ATS algorithm groups the velocity values into several intervals, which are used to partition trajectories into velocity-preserving segments. The simplified trajectory could be derived by applying the position-preserving simplification approach on each segment, where the threshold in a position-preserving approach could be determined without manual setting. Extensive experiments are conducted by using a real trajectory dataset in Porto. The experimental results show ATS algorithms could simplify trajectories effectively while preserving the velocity feature and the position feature at the same time.