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Large-Scale Trajectory Prediction

The widespread use of global positioning system (GPS) navigation systems and wireless communication technology-enabled vehicles have resulted in huge volumes of spatio-temporal data, especially in the form of trajectories. These data often contain a great deal of information, which give rise to many location-based services (LBSs) and applications such as vehicle navigation, traffic management, and location-based recommendations. One key operation in such applications is the route prediction of moving objects. Vehicle route prediction allows certain services to improve their quality, e.g., if the route of vehicles is known in advance, intelligent transportation systems (ITSs) can provide route-specific traffic information to drivers such as forecasting traffic conditions and routing the driver to avoid traffic jams.

Most trajectory prediction approaches in the literature use only synthetic or small to medium size real trajectory datasets because they are not scalable. The aim is to develop a framework for large-scale trajectory prediction which can be used for road networks of major cities. A scalable framework was developed for short-term and long-term trajectory prediction, based on our novel big data clustering algorithm and Markov models, which can utilize a large number (in millions) of trajectories in a dense road network. The developed framework was tested on two real-life, large-scale, taxi trajectory datasets from the Beijing and Singapore road networks in our experiments.

    Research Outcome:
  • Rathore P., Kumar D., Rajasegarar S., Palaniswami M, Bezdek J. C. (2019) ,"A Scalable Framework for Trajectory Prediction," in IEEE Transactions on Intelligent Transportation Systems.