Abstract
Raw GPS trajectory data are often very large and use up excessive storage space. The efficiency and accuracy of activity patterns analysis or individual–environment interaction modeling using such data may be compromised due to data size and computational needs. Line generalization algorithms may be used to simplify GPS trajectories. However, traditional algorithms focus on geometric characteristics of linear features. Trajectory data may record information beyond location. Examples include time and elevation, and inferred information such as speed, transportation mode, and activities. Effective trajectory simplification should preserve these characteristics in addition to location and orientation of spatial-temporal movement. This paper proposes an Enhanced Douglas–Peucker (EDP) algorithm that implements a set of Enhanced Spatial-Temporal Constraints (ESTC) when simplifying trajectory data. These constraints ensure that the essential properties of a trajectory be preserved through preserving critical points. Further, this study argues that speed profile can uniquely identify a trajectory and thus it can be used to evaluate the effectiveness of a trajectory simplification. The proposed ESTC-EDP simplification method is applied to two examples of GPS trajectory. The results of trajectory simplification are reported and compared with that from traditional DP algorithm. The effectiveness of simplification is evaluated.
Highlights
As GPS-enabled portable devices become available [1], trajectory data with continuously recorded spatiotemporal footprints receive unprecedented attention from studies examining the moving patterns of subjects and their interaction with environment [2]
This paper contributes to GPS trajectory simplification by developing an Enhanced Douglas–Peucker (EDP) algorithm that considers both geometry properties of linear features and movement and contextual information of a trajectory
The traditional line generalization algorithms may be augmented by a set of enhanced spatial-temporal constraints (ESTC) that are tailored to preserve the critical context information of a trajectory
Summary
As GPS-enabled portable devices become available [1], trajectory data with continuously recorded spatiotemporal footprints receive unprecedented attention from studies examining the moving patterns of subjects and their interaction with environment [2]. GPS trajectory data contain critical locations for a subject’s activities, such as point locations along a trajectory that indicates particular activities or routine [26] (e.g., breakfast taco pick-up place along morning commuting route) or change of travel mode or transportation situation (e.g., a significant speed change that may indicate a change from walking to commuting train riding) These locations are activity nodes along a subject’s spatial-temporal trajectories and should not be treated as ordinary location points and be dropped by an automatic algorithm that is designed to preserve geometry of a line. This paper contributes to GPS trajectory simplification by developing an Enhanced Douglas–Peucker (EDP) algorithm that considers both geometry properties of linear features and movement and contextual information of a trajectory.
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