Online Co-movement Pattern Prediction in Mobility Data
This work addresses the challenge of predicting co-movement patterns for applications like traffic management or collision avoidance, but it appears incremental as it builds on existing sub-problems without introducing a fundamentally new approach.
The paper tackles the problem of predicting collective behavioral patterns in mobility data, specifically co-movement patterns, by splitting it into future location prediction and evolving cluster detection, and demonstrates its accuracy experimentally on a real maritime dataset.
Predictive analytics over mobility data are of great importance since they can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example of such analytics is future location prediction, where the goal is to predict the future location of a moving object,given a look-ahead time. What is even more challenging is being able to accurately predict collective behavioural patterns of movement, such as co-movement patterns. In this paper, we provide an accurate solution to the problem of Online Prediction of Co-movement Patterns. In more detail, we split the original problem into two sub-problems, namely Future Location Prediction and Evolving Cluster Detection. Furthermore, in order to be able to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure, which facilitates us to match the predicted clusters with the actual ones. Finally, the accuracy of our solution is demonstrated experimentally over a real dataset from the maritime domain.