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Modeling and Predicting Future Trajectories of Moving Objects in a Constrained Network

Jidong Cheny 1 Xiaofeng Meng 1 Yanyan Guo 1 Stéphane Grumbach 2 Hui Suny 1
2 NETQUEST - NETQUEST
Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Inria Paris-Rocquencourt, INRA - Institut National de la Recherche Agronomique, CAS - Chinese Academy of Sciences [Changchun Branch], Institute of Automation - Chinese Academy of Sciences, CNRS - Centre National de la Recherche Scientifique
Abstract : Advances in wireless sensor networks and positioning technologies enable traffic management (e.g. routing traffic) that uses real-time data monitored by GPS-enabled cars. Location management has become an enabling technology in such application. The location modeling and trajectory prediction of moving objects are the fundamental components of location management in mobile locationaware applications. In this paper, we model the road network and moving objects in a graph of cellular automata (GCA), which makes full use of the constraints of the network and the stochastic behavior of the traffic. A simulation-based method based on graphs of cellular automata is proposed to predict future trajectories. Our technique strongly differs from the linear prediction method, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changes. The experiments, carried on two different datasets, show that the simulation-based prediction method provides higher accuracy than the linear prediction method.
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https://hal.inria.fr/inria-00120276
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Submitted on : Wednesday, December 13, 2006 - 7:12:15 PM
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Jidong Cheny, Xiaofeng Meng, Yanyan Guo, Stéphane Grumbach, Hui Suny. Modeling and Predicting Future Trajectories of Moving Objects in a Constrained Network. Proceedings of the 7th International Conference on Mobile Data Management (MDM'06), IEEE Computer Society, May 2006, Nara / Japan, Japan. pp.156, ⟨10.1109/MDM.2006.107⟩. ⟨inria-00120276⟩

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