ParPredict: A partially-ordered sequential rules based framework for mobility prediction

Abstract

Predicting the future movement of mobile users has emerged as an important technology topic in many applications related to intelligent transportation systems (ITS) and Location-based services (LBS). Numerous prediction models were proposed relying on probabilistic models (e.g. Markov Chain) or data mining techniques (e.g. neural network, sequential patterns mining). Mining sequential patterns and rules is one of the data mining techniques used. Mining sequential rules from sequence databases is an active research topic that is broadly applied for many real-world scenarios. In this paper, we propose to adapt a novel kind of sequential rules called partially order sequential rules for route prediction problem. We aim to further compare this kind with standard sequential rule for the task of mobility prediction. An experimental evaluation conducted on real and synthetic datasets show that the proposed model outperforms a state-of-the-art sequential model in terms of accuracy and prediction coverage.