Abstract
Traffic condition is one vital piece of information that any commuter would wish to obtain to plan an efficient route. However, most existing works monitor and report only current traffic, which makes it too late for commuters to change their routes when they realize they are already stuck in the traffic. Therefore, in this paper, we propose a traffic prediction approach by defining and solving a novel continuous predictive line query. The continuous predictive line query aims to accurately estimate traffic conditions in the near future based on current movement of vehicles on the roads, and continuously update the predicted traffic conditions as vehicles move. The predicted traffic condition will not only help redirect commuters in advance but also help relieve the overall traffic congestion problem. We have proposed three algorithms to answer the query and carried out both theoretical and empirical study. Our experimental results demonstrate the effectiveness and efficiency of our approach.
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Notes
- 1.
Both the terms Moving Object and Vehicle will be used interchangeably.
- 2.
Note that the minimum speed is always greater than 0 since we exclude outlier objects with speed equal to 0 (e.g., an object stopped at a gas station).
- 3.
Since the accuracy is not affected by the buffer, it is omitted in the discussion.
References
Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6, 153–180 (2004)
Cai, Y., Hua, K.A., Cao, G.: Processing range-monitoring queries on heterogeneous mobile objects. In: IEEE International Conference on Mobile Data Management (2004)
Chen, S., Ooi, B.C., Tan, K.-L., Nascimento, M.A.: ST2B-tree: a self-tunable spatio-temporal B+-tree index for moving objects. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, pp. 29–42. ACM, New York (2008)
Predic, B., Papadopoulos, A.N., Stojanovic, D., Djordjevic-Kajan, S., Nanopoulos, A.: Continuous range query processing for network constrained mobile objects. In: 8th International Conference on Enterprise Information Systems (2006)
Feng, J., Lu, J., Zhu, Y., Mukai, N., Watanabe, T.: Indexing of moving objects on road network using composite structure. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007/WIRN 2007, Part II. LNCS (LNAI), vol. 4693, pp. 1097–1104. Springer, Heidelberg (2007)
Gedik, B., Liu, L.: MobiEyes: a distributed location monitoring service using moving location queries. IEEE Trans. Mob. Comput. 5, 1384–1402 (2006)
Heendaliya, L., Lin, D., Hurson, A.R.: Predictive line queries for traffic forecasting. In: Database and Expert Systems Applications (2012)
Heendaliya, L., Lin, D., Hurson, A.: Continuous predictive line queries under road-network constraints. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds.) DEXA 2013, Part II. LNCS, vol. 8056, pp. 228–242. Springer, Heidelberg (2013)
Hu, H., Xu, J., Lee, D.L.: A generic framework for monitoring continuous spatial queries over moving objects. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2005)
Jensen, C.S., Lin, D., Ooi, B.C.: Query and update efficient B\(^+\)-tree based indexing of moving objects. In: Proceedings of the 30\(^{th}\) International Conference on Very Large Data Bases, VLDB 2004, vol. 30, pp. 768–779. VLDB Endowment (2004)
Jensen, C.S., Lin, D., Beng, C.O., Zhang, R.: Effective density queries on continuously moving objects. In: Proceedings of the 22nd International Conference on Data Engineering (2006)
Kyoung-Sook, K., Si-Wan, K., Tae-Wan, K., Ki-Joune, L.: Fast indexing and updating method for moving objects on road networks. In: Proceedings of the 4th International Conference on Web Information Systems Engineering Workshops (2003)
Liu, F., Hua, K.A.: Moving query monitoring in spatial network environments. Mob. Netw. Appl. (2012)
Hadjieleftheriou, M., Kollios, G., Gunopulos, D., Tsotras, V.J.: On-line discovery of dense areas in spatio-temporal databases. In: Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds.) SSTD 2003. LNCS, vol. 2750, pp. 306–324. Springer, Heidelberg (2003)
mobiThinking (2013)
Mouratidis, K., Papadias, D., Bakiras, S., Tao, Y.: A threshold-based algorithm for continuous monitoring of k nearest neighbors. IEEE Trans. Knowl. Data Eng. 17, 1451–1464 (2005)
Mouratidis, K., Papadias, D., Hadjieleftheriou, M.: Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2005)
Mouratidis, K., Yiu, L., Papadias, D., Mamoulis, N.: Continuous nearest neighbor monitoring in road networks. In: Proceedings of the 32nd International Conference on Very Large Data Bases (2006)
Nehme, R.V., Rundensteiner, E.A.: SCUBA: scalable cluster-based algorithm for evaluating continuous spatio-temporal queries on moving objects. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 1001–1019. Springer, Heidelberg (2006)
Ni, J., Ravishankar, C.V.: Pointwise-dense region queries in spatio-temporal databases. In: IEEE 23rd International Conference on Data Engineering (2007)
Nutanong, S., Tanin, E., Shao, J., Zhang, R., Kotagiri, R.: Continuous detour queries in spatial networks. IEEE Trans. Knowl. Data Eng. 24, 1201–1215 (2012)
Prabhakar, S., Xia, Y., Kalashnikov, D.V., Aref, W.G., Hambrusch, S.E.: Query indexing and velocity constrained indexing: scalable techniques for continuous queries on moving objects. IEEE Trans. Comput. 51, 1124–1140 (2002)
Tao, Y., Papadias, D., Sun, J.: The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In: Proceedings of the 29th International Conference on Very Large Data Bases, VLDB 2003, vol. 29, pp. 790–801. VLDB Endowment (2003)
Šaltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. SIGMOD Rec. 29(2), 331–342 (2000)
Wang, H., Zimmermann, R.: Processing of continuous location-based range queries on moving objects in road networks. IEEE Trans. Knowl. Data Eng. 23, 1065–1078 (2011)
Wen, J., Meng, X., Hao, X., Xu, J.: An Efficient approach for continuous density queries. Front. Comput. Sci. 6, 581–595 (2012)
Xia, T., Zhang, D.: Continuous reverse nearest neighbor monitoring. In: Proceedings of the 22nd International Conference on Data Engineering (2006)
Xiong, X., Mokbel, M.F., Aref, W.G.: SEA-CNN: scalable processing of continuous K-Nearest neighbor queries in spatio-temporal databases. In: Proceedings of the 21st International Conference on Data Engineering (2005)
Yiu, M.L., Tao, Y., Mamoulis, N.: The Bdual-tree: indexing moving objects by space filling curves in the dual space. Very Large Data Bases J. 17, 379–400 (2008)
Acknowledgement
This work is partly funded by the U.S. National Science Foundation under Grant No. CNS-1250327.
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Heendaliya, L., Lin, D., Hurson, A. (2015). Continuous Predictive Line Queries for On-the-Go Traffic Estimation. In: Hameurlain, A., Küng, J., Wagner, R., Decker, H., Lhotska, L., Link, S. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XVIII. Lecture Notes in Computer Science(), vol 8980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46485-4_4
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