A conceptual view on trajectories

Abstract

Analysis of trajectory data is the key to a growing number of applications aiming at global understanding and management of complex phenomena that involve moving objects (e.g. worldwide courier distribution, city traffic management, bird migration monitoring). Current DBMS support for such data is limited to the ability to store and query raw movement (i.e. the spatio-temporal position of an object). This paper explores how conceptual modeling could provide applications with direct support of trajectories (i.e. movement data that is structured into countable semantic units) as a first class concept. A specific concern is to allow enriching trajectories with semantic annotations allowing users to attach semantic data to specific parts of the trajectory. Building on a preliminary requirement analysis and an application example, the paper proposes two modeling approaches, one based on a design pattern, the other based on dedicated data types, and illustrates their differences in terms of implementation in an extended-relational context.

Context‐aware movement analytics: implications, taxonomy, and design framework

“…Movement context refers to the entities' quantitative parameters (e.g., speed and acceleration) that are generated as the movement commences. Spaccapietra et al 73 termed these parameters movement characteristics whereas Dodge et al 62 called such descriptors primary and secondary parameters. Hereafter, we refer to these dynamic physical descriptors as movement context.…”

Conflict in Pedestrian Networks

“…Considering a large number of entities at the level of the collectives instead of the level of the individuals can also be computationally more efficient. Pedestrians can be grouped according to their travel purposes [8], origin and destination points [9], or shared movement characteristics [6,10]. This paper compares the proposed method when considering random flows versus collective motion of the network.…”

SeTraStream: Semantic-Aware Trajectory Construction over Streaming Movement Data

“…sharing similar velocity, direction etc.) [3] and thus expresses unchanged movement pattern; semantic computation can further extract high-level trajectory concepts like stops/moves [29], and even provide additional tagging support like the activity for stops (e.g. home, office, shopping) and the transportation mode (e.g.…”

“…Recently, semantic trajectory computation has attracted the research interest [1][3] [29] [30][31] [32]. The focus of semantic trajectory construction is initially on the extraction of meaningful trajectories from the raw positioning data like GPS feeds.…”

“…1) assigned to different time intervals throughout their lifespan. Current methods of such kind of trajectory construction are mainly offline [1][3] [29][30] [31][32], which is not enough for modern, real life applications, because positioning data of moving objects are continuously generated as streams and corresponding querying operations often demand result delivery in an online and continuous fashion. Motivating Examples.…”