Pythia: Distributed Pattern-based Future Location Prediction of Moving Objects


Panagiotis Tampakis and Nikos Pelekis


Predictive analytics over mobility data is a domain that has received a lot of attention by the research community the past few years and encapsulates a wide range of sub-problems aiming to predict e.g. the future location of a moving object, the future trajectory of a moving object, the traffic flow, the expected time of arrival of a moving object to its destination etc.. These are all quite challenging problems from their nature and what makes them even more challenging is the massive production of mobility data, which sets some limitations over training such predictive models. In this paper we propose Pythia, a framework able to predict simultaneously, the exact future location of an extremely large set of moving objects, given a look-ahead time, by employing massive historical mobility patterns. In order to achieve this we build a predictor for each moving object, in the form of a directed acyclic graph, by taking into account not only its past movement but also collective historical patterns. Our experimental study shows that our approach can predict accurately the future location of moving objects in an efficient way.

Other Publications
BMDA Workshop EDBT Conference 2024