Our work focuses on developing and evaluating enhanced inverse reinforcement learning and imitation learning methods regarding 4D trajectories, although not only for those trajectories. Thus, we follow a supervised leaning approach, and treat historical data provided by “experts” as demonstrations that a machine learning algorithm should exploit to learn the corresponding models of costs/rewards and action policies. Then, learned models can be exploited to plan or predict trajectories.
The delta big data architecture for mobility analytics Conference
2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService), IEEE IEEE, Oxford, UK, 2020.
Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods Journal Article
arXiv, cs.MA , 2019.
The datAcron Ontology for the Specification of Semantic Trajectories: Specification of Semantic Trajectories for Data Transformations Supporting Visual Analytics Journal Article
Journal Of Data Semantics, 8 , 2019.
ARGO: A Big Data Framework for Online Trajectory Prediction Conference
SSTD 2019, 2019.
Guest Editorial: Special issue on mobility analytics for spatio-temporal and social data. Journal Article
GEOINFORMATICA, 23 , 2019.