Detecting behavior types of moving object trajectories

Abstract

Trajectory mining is a challenging and crucial problem especially in the context of smart cities where many applications depend on human behaviors. In this paper, we characterize such behaviors by patterns, where each pattern type represents a particular behavior, e.g. emerging, latent, lost, etc. From GPS raw data, we introduce algorithms that allow computing a formal concept lattice which encodes optimal correspondences between hidden patterns and trajectories. In order to detect behaviors, we propose an algorithm that analyses the evolution of the discovered formal concepts over time. The method generates tagged city maps to easily visualize the resulting behaviors at different spatio-temporal granularity values. Refined or coarse analysis can thus be performed for a given situation. Experimental results using real-world GPS trajectory data show the relevance of the proposed method and the usefulness of the resulting tagged city maps.

Sequential Formal Concepts over Time for Trajectory Analysis

“…To the best of our knowledge, there is no research work based on these three notions for analysing moving objects. This work extends our previous work [13] by considering sequences and movement directions visualisation. We propose a method to build a spatial-temporal trajectory discrete representation in order to extract hidden closed sequential patterns using uniform grids.…”

Sequential Patterns for Spatio-Temporal Traffic Prediction

“…To train the proposed Markov model, we determine the traffic status by detecting the evolution types (Emerging, Decreasing, etc.) of sequential formal concepts (containing closed sequential patterns) extracted from trajectory data [2] [3]. The prediction results are visualized in geotagged maps.…”

Relative space-based GIS data model to analyze the group dynamics of moving objects

“…Moving objects are the most common and important component in a diverse range of phenomena, such as human mobility (Fang et al, 2017;Jiang et al, 2017;AlMuhisen et al, 2018), urban transportation , Tang et al, 2015Tu et al, 2017), ship logistics in the ocean (Yu et al, 2017b;Fang et al, 2018) and even animal migrations (Bastille-Rousseau et al, 2017). Many research projects have been driven and improved by moving-object data analysis, such as individual/group behavior analysis, path discovery and behavior prediction.…”