Review and classification of trajectory summarisation algorithms: From compression to segmentation

Abstract

With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.

Ecological dynamic regimes: Identification, characterization, and comparison

“…A key element of our contribution is the computation of representative trajectories based on the most common dynamics of EDRs (RETRA-EDR, Figure 4). Although multiple approaches have been developed for clustering trajectory data (Amigo et al, 2021), the algorithms to define representative trajectories in a cluster are, to our knowledge, much less abundant and diverse and mainly focused on moving objects in low-dimensional spaces. RETRA-EDR was specifically designed to be applied to EDRs defined in high-dimensional resemblance spaces.…”

“…The definition of trajectories representing the main dynamical patterns of an EDR is essential to identify and understand the changes of the state variables along the regime, summarize its geometric characteristics (e.g., length, directionality), and set the core of the regime to be compared with the remaining individual trajectories in the same or different EDRs. Despite the increasing interest in clustering trajectory data, most algorithms focus on low‐dimensional spaces generated from common features of moving objects (e.g., geographical coordinates, time, speed, and direction) (Amigo et al, 2021). As a consequence, most methods summarizing trajectory clusters into representative trajectories cannot be applied to high‐dimensional spaces (Bermingham & Lee, 2015).…”

Batch Simplification Algorithm for Trajectories over Road Networks

“…Reducing the size of the data in a trajectory facilitates the acceleration of the information extraction process [12,21]. There are several path simplification methods and algorithms that are suitable for different types of data and yield different results [22]; but they all have the same principle in common: simplify the data by removing the redundancy of the data in the source file [23][24][25][26]. Meratnia et al [27] define data compression as substantially reducing the amount of data without significant loss.…”