Efficient mining of regional movement patterns in semantic trajectories

Abstract

Semantic trajectory pattern mining is becoming more and more important with the rapidly growing volumes of semantically rich trajectory data. Extracting sequential patterns in semantic trajectories plays a key role in understanding semantic behaviour of human movement, which can widely be used in many applications such as location-based advertising, road capacity optimisation, and urban planning. However, most of existing works on semantic trajectory pattern mining focus on the entire spatial area, leading to missing some locally significant patterns within a region. Based on this motivation, this paper studies a regional semantic trajectory pattern mining problem, aiming at identifying all the regional sequential patterns in semantic trajectories. Specifically, we propose a new density scheme to quantify the frequency of a particular pattern in space, and thereby formulate a new mining problem of finding all the regions in which such a pattern densely occurs. For the proposed problem, we develop an e cient mining algorithm, called RegMiner (Regional Semantic Trajectory Pattern Miner), which e↵ec-tively reveals movement patterns that are locally frequent in such a region but not necessarily dominant in the entire space. Our empirical study using real trajectory data shows that RegMiner finds many interesting local patterns that are hard to find by a state-of-the-art global pattern mining scheme, and it also runs several orders of magnitude faster than the global pattern mining algorithm.

A Framework to Support Continuous Range Queries over Multi-Attribute Trajectories

“…Frequent sequential patterns can be found to reflect movement regularity by considering spatial compactness, semantic consistency and temporal continuity simultaneously [32]. A regional semantic trajectory pattern mining problem is studied in [4], the aim of which is to identify all the regional sequential patterns in semantic trajectories including global and local frequent patterns. A detailed discussion on semantic trajectories can be found elsewhere [15] [31].…”

“…Recently, trajectories featuring multiple attributes have received increasing attention [30] [34] [4] [25] [23]. Such data opens door to understand trajectories along different dimensions simultaneously.…”

Segmented Trajectory Clustering-Based Destination Prediction in IoVs

“…This phenomenon doesn't make too much sense to our problem, and it will generate too many redundant data in trajectories. Some works such as [22] and [23] have studied this problem with movement pattern mining. However, they just keep the movement pattern information rather than location information in trajectories.…”

Detecting regional dominant movement patterns in trajectory data with a convolutional neural network

“…The method is still restricted in discovering more complicated movement patterns such as converging and diverging pattern. In Choi et al (2017), sequential pattern mining and clustering are performed to extract regions where a particular sequential pattern densely appears in space. That type of regional semantic trajectory pattern identifies regularities in the semantics of movement, which is different from the pattern studied in this paper.…”