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.

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.

No abstract

This paper proposes a method for predicting the traffic status of a city within time windows. The method takes advantage of spacepartitioning, closed sequential pattern extraction, emerging pattern detection, and Markov chain modeling. From trajectories, we identify active regions in which moving objects mostly visit. The traffic status of each region is detected based on continuous tracking of closed sequential patterns evolution over time. Based on the proposed Markov model, the near-future status of traffic is then predicted. The traffic status is reported on maps and can be used to enhance future city transportation. The experiments on realworld data sets show that the proposed method provides promising results.