CLEAN:Frequent Pattern-Based Trajectory Compression and Computation on Road Networks

The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road network. By treating the mined patterns as dictionary items, the long trajectories have the chance to be encoded by shorter paths, thus leading to smaller space cost. And an error-bounded temporal compression is carefully designed on top of the identified spatial patterns for much low space cost. Meanwhile, the patterns are also utilized to improve the performance of two trajectory applications, range query and clustering, without decompression overhead. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of spatial-temporal compression and trajectory applications.

Time Synchronized Velocity Error for Trajectory Compression

Nowadays,distance is usually used to evaluate the error of trajectory compression.These methods can effectively indicate the level of geometric similarity between the compressed and the raw trajectory,but it ignores the velocity error in the compression.To fill the gap of these methods,assuming the velocity changes linearly,a mathematical model called SVE(Time Synchronized Velocity Error)for evaluating compression error is designed,which can evaluate the velocity error effectively,conveniently and accurately.Based on this model,an innovative algorithm called SW-MSVE(Minimum Time Synchronized Velocity Error Based on Sliding Window)is proposed,which can minimize the velocity error in trajectory compression under the premise of local optimization.Two elaborate experiments are designed to demonstrate the advancements of the SVE and the SW-MSVE respectively.In the first experiment,we use the PED,the SED and the SVE to evaluate the error under four compression algorithms,one of which is the SW-MSVE algorithm.The results show that the SVE is less influenced by noise with stronger performance and more applicability.In the second experiment,by marking the raw trajectory,we compare the SW-MSVE algorithm with three others algorithms at information retention.The results show that the SW-MSVE algorithm can take into account both velocity and geometric structure constraints and retains more information of the raw trajectory at the same compression ratio.

Trajectory Time Series Compression Algorithm Based on Unsupervised Segmentation

Aiming at the problem of ignoring the importance of starting point features of trajecory segmentation in existing trajectory compression algorithms,a study was conducted on the preprocessing process of trajectory time series.Firstly,an algorithm improvement was proposed based on the segmentation algorithm GRASP-UTS(Greedy Randomized Adaptive Search Procedure for Unsupervised Trajectory Segmentation).On the basis of considering trajectory coverage,this algorithm designs an adaptive parameter adjustment to segment long-term trajectory data reasonably and the identification of an optimal starting point for segmentation.Then the compression efficiency of typical offline and online algorithms,such as the Douglas-Peucker algorithm,the Sliding Window algorithm and its enhancements,was compared before and after segmentation.The experimental findings highlight that the Adaptive Parameters GRASP-UTS segmentation approach leads to higher fitting precision in trajectory time series compression and improved algorithm efficiency post-segmentation.Additionally,the compression performance of the Improved Sliding Window algorithm post-segmentation showcases its suitability for trajectories of varying scales,providing reasonable compression accuracy.