Abstract
Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future trajectories of the objects. In this paper, we propose an original data access method, the ANR-tree, which supports predictive queries. We focus on real life environments, where the objects move within constrained networks, such as vehicles on roads. We introduce a simulation-based prediction model based on graphs of cellular automata, which makes full use of the network constraints and the stochastic traffic behavior. Our technique differs strongly from the linear prediction model, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changing frequently. The data structure extends the R-tree with adaptive units which group neighbor objects moving in the similar moving patterns. The predicted movement of the adaptive unit is not given by a single trajectory, but instead by two trajectory bounds based on different assumptions on the traffic conditions and obtained from the simulation. Our experiments, carried on two different datasets, show that the ANR-tree is essentially one order of magnitude more efficient than the TPR-tree, and is much more scalable.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Saltenis S, Jensen C S. Indexing of moving objects for location-based service. In Proc. 18th Int. Conf. Data Engineering, San Jose, CA, 2002, pp.463–472.
Agarwal P K, Arge L, Erickson J. Indexing moving points (extended abstract). In Proc. the 19th ACM SIGMOD-SIGACT-SIGART Symp. Principles of Database Systems, Dallas, Texas, 2000, pp.175–186.
Jensen C S, Lin D, Ooi B C. Query and update efficient B+-tree based indexing of moving objects. In Proc. 30th Int. Conf. Very Large Data Bases, Toronto, Canada, 2004, pp.768–779.
Patel M, Chen Y, Chakka V. STRIPES: An efficient index for predicted trajectories. In Proc. the ACM SIGMOD Int. Conf. Management of Data, Paris, France, 2004, pp.637–646.
Kollios G, Gunopulos D, Tsotras J V. On indexing mobile objects. In Proc. the 8th ACM SIGMOD-SIGACT-SIGART Symp. Principles of Database Systems, Philadelphia, USA, 1999, pp.261–272.
Saltenis S, Jensen C S, Leutenegger S T, Lopez M A. Indexing the positions of continuously moving objects. In Proc. the ACM SIGMOD Int. Conf. Management of Data, Dallas, Texas, USA, 2000, pp.331–342.
Tao Y, Papadias D, Sun J. The TPR*-tree: An optimized spatiotemporal access method for predictive queries. In Proc. 29th Int. Conf. Very Large Data Bases, Berlin, Germany, 2003, pp.790–801.
Almeida V T D, Güting R H. Indexing the trajectories of moving objects in networks. GeoInformatica, 2005, 9(1): 33–60.
Frentzos E. Indexing objects moving on fixed networks. In Proc. the 8th Int. Symp. Spatial and Temporal Databases, Santorini Island, Greece, 2003, pp.289–305.
Pfoser D, Jensen C S. Indexing of network constrained moving objects. In Proc. 11th ACM Int. Symp. Advances in Geographic Information Systems, New Orleans, Louisiana, USA, 2003, pp.25–32.
Nascimento M A, Silva J R O. Towards historical R-trees. In ACM Symposium on Applied Computing, Atlanta, Georgia, 1998, pp.235–240.
Pfoser D, Jensen C S, Theodoridis Y. Novel approaches in query processing for moving object trajectories. In Proc. 26th Int. Conf. Very Large Data Bases, Cairo, Egypt, 2000, pp.395–406.
Tao Y, Papadias D. MV3R-tree: A spatio-temporal access method for timestamp and interval queries. In Proc. 27th Int. Conf. Very Large Data Bases, Roma, Italy, 2001, pp.431–440.
Guttman A. R-trees: A dynamic index structure for spatial searching. In Proc. the ACM SIGMOD Int. Conf. Management of Data, Boston, USA, 1984, pp.47–57.
Yiu M L, Tao Y, Mamoulis N. The B dual-Tree: Indexing moving objects by space-filling curves in the dual space. To appear in Very Large Data Base Journal, 2006.
Nagel K, Schreckenberg M. A cellular automaton model for freeway traffic. Journal Physique, 1992, 2: 2221–2229.
Theodoridis Y, Stefanakis E, Sellis T K. Efficient cost models for spatial queries using R-trees. TKDE, 2000, 12(1): 19–32.
Brinkhoff T. A framework for generating network-based moving objects. GeoInformatica, 2002, 6(2): 153–180.
Author information
Authors and Affiliations
Corresponding author
Additional information
Partly supported by the National Natural Science Foundation of China (Grant No. 60573091), the Key Project of Ministry of Education of China (Grant No. 03044), Program for New Century Excellent Talents in University (NCET), Program for Creative Ph.D. Thesis in University.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Chen, JD., Meng, XF. Indexing Future Trajectories of Moving Objects in a Constrained Network. J Comput Sci Technol 22, 245–251 (2007). https://doi.org/10.1007/s11390-007-9031-9
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11390-007-9031-9