A Flexible Trajectory Compression Algorithm for Multi-Modal Transportation

Abstract

Abstract. Continuous progress in navigation, sensor-based, and GPS technologies have made smart devices essential to our daily lives and many location-based applications. However, the trajectory datasets generated by these applications require the management of large data volumes while preserving their main properties and semantics. One of the most popular methods for compressing trajectory data offline is the Douglas–Peucker (DP) algorithm, but its principles should be applied to a diverse range of contexts when considering real-time trajectory data. This paper introduces a Flexible Douglas-Peucker algorithm (FDP) that takes into account the data’s diversity, underlying properties, and semantics. The proposed framework is applied to the Geolife benchmark dataset with a series of different thresholds that reflects different contexts and constraints when performing a trajectory compression process. The results show that the proposed algorithm achieves a significant compression rate while preserving trajectory data points that have a semantic role concerning different modes of transportation.

Trajectory Compression with Spatio-Temporal Semantic Constraints

“…Richter et al proposed semantic trajectory compression (STC), which identifies and describes relevant events along a trajectory, combining them into event blocks to form the compressed trajectory [21]. Mirvahabi et al introduced constraints on compression, comprehensively considering data diversity, underlying attributes, and semantic information [22]. Zhang et al compressed trajectories by identifying vehicle movement modes, reflecting semantic information [23].…”