Sequential Formal Concepts over Time for Trajectory Analysis

Abstract

Tracking technologies and location-acquisition have led to the increase of the availability of trajectory data. Many efforts are devoted to develop methods for mining and analysing trajectories due to its importance in lots of applications such as traffic control, urban planning etc. In this paper, we present a new trajectory analysis and visualisation framework for massive movement data. This framework leverages formal concepts, sequential patterns, emerging patterns, and analyses the evolution of mobility patterns through time. Tagged city maps are generated to display the resulting evolution analysis and directions at different spatio-temporal granularity values. Experiments on real-world dataset show the relevance of the proposition and the usefulness of the resulting tagged city maps.

Concept Emergence from Complex Sensory Data: A Connectionist Model

“…When dealing with continuous data such as spatiotemporal values, some of the most interesting possibilities may arise in the way attributes are 'selected' according to perhaps not just relevance but rather, modelling strategy. For example, in the traffic trajectories data modeled in (Almuhisen et al, 2019), constraints are imposed through the aforementioned GrC, but also by creating attributes only from the sequential transitions of the observations rather than looking at all the points of a trajectory as a whole. This heuristic method of mapping or 'grounding' attributes to spatiotemporal data is a constraint strategy based on attribute selection that simplifies the model substantially while successfully extracting useful knowledge.…”

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.…”