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Journal of Computer Science and Technology

, Volume 31, Issue 4, pp 637–648 | Cite as

Dynamic Shortest Path Monitoring in Spatial Networks

  • Shuo Shang
  • Lisi Chen
  • Zhe-Wei Wei
  • Dan-Huai Guo
  • Ji-Rong Wen
Regular Paper

Abstract

With the increasing availability of real-time traffic information, dynamic spatial networks are pervasive nowadays and path planning in dynamic spatial networks becomes an important issue. In this light, we propose and investigate a novel problem of dynamically monitoring shortest paths in spatial networks (DSPM query). When a traveler aims to a destination, his/her shortest path to the destination may change due to two reasons: 1) the travel costs of some edges have been updated and 2) the traveler deviates from the pre-planned path. Our target is to accelerate the shortest path computing in dynamic spatial networks, and we believe that this study may be useful in many mobile applications, such as route planning and recommendation, car navigation and tracking, and location-based services in general. This problem is challenging due to two reasons: 1) how to maintain and reuse the existing computation results to accelerate the following computations, and 2) how to prune the search space effectively. To overcome these challenges, filter-and-refinement paradigm is adopted. We maintain an expansion tree and define a pair of upper and lower bounds to prune the search space. A series of optimization techniques are developed to accelerate the shortest path computing. The performance of the developed methods is studied in extensive experiments based on real spatial data.

Keywords

shortest path dynamic spatial network spatial database location-based service 

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References

  1. [1]
    Parkinson B, Spiker Jr J, Axelrad P, Enge P. Global positioning system: Theory and applications. In Progress in Astronautics and Aeronautics 163, Zarchan P(ed.), American Institute of Aeronautics and Astronautics, Inc., 1996.Google Scholar
  2. [2]
    Dijkstra E W. A note on two problems in connection with graphs. Numerische Mathematik, 1959, 1(1): 269–271.MathSciNetCrossRefzbMATHGoogle Scholar
  3. [3]
    Hart P E, Nilsson N J, Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 1968, 4(2): 100–107.CrossRefGoogle Scholar
  4. [4]
    Ding B, Yu J X, Qin L. Finding time-dependent shortest paths over large graphs. In Proc. the 11th EDBT, March 2008, pp.205-216.Google Scholar
  5. [5]
    Hua M, Pei J. Probabilistic path queries in road networks: Traffic uncertainty aware path selection. In Proc. the 13th EDBT, March 2010, pp.347-358.Google Scholar
  6. [6]
    Yang B, Guo C, Jensen C S et al. Stochastic skyline route planning under time-varying uncertainty. In Proc. the 30th IEEE International Conference on Data Engineering, March 31-April 4, 2014, pp.136-147.Google Scholar
  7. [7]
    Shang S, Chen L, Wei Z et al. Collective travel planning in spatial networks. IEEE Trans. Knowl. Data Eng., 2016, 28(5): 1132–1146.CrossRefGoogle Scholar
  8. [8]
    Papadias D, Shen Q, Tao Y et al. Group nearest neighbor queries. In Proc. the 20th ICDE, March 30-April 2, 2004, pp.301-312.Google Scholar
  9. [9]
    Papadias D, Tao Y, Mouratidis K et al. Aggregate nearest neighbor queries in spatial databases. ACM Transactions on Database Systems, 2005, 30(2): 529–576.CrossRefGoogle Scholar
  10. [10]
    Shang S, Lu H, Pedersen T B et al. Finding traffic-aware fastest paths in spatial networks. In Proc. the 13th SSTD, Aug. 2013, pp.128-145.Google Scholar
  11. [11]
    Shang S, Lu H, Pedersen T B et al. Modeling of trafficaware travel time in spatial networks. In Proc. the 14th IEEE MDM, June 2013, pp.247-250.Google Scholar
  12. [12]
    Shang S, Liu J, Zheng K et al. Planning unobstructed paths in traffic-aware spatial networks. GeoInformatica, 2015, 19(4): 723–746.CrossRefGoogle Scholar
  13. [13]
    Chen Z, Shen H T, Zhou X. Monitoring path nearest neighbor in road networks. In Proc. ACM SIGMOD, June 2009, pp.591-602.Google Scholar
  14. [14]
    Shang S, Yuan B, Deng K et al. PNN query processing on compressed trajectories. GeoInformatica, 2012, 16(3): 467–496.CrossRefGoogle Scholar
  15. [15]
    Shang S, Zheng K, Jensen C S et al. Discovery of path nearby clusters in spatial networks. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(6): 1505–1518.CrossRefGoogle Scholar
  16. [16]
    Zheng K, Shang S, Yuan N J et al. Towards efficient search for activity trajectories. In Proc. the 29th ICDE, April 2013, pp.230-241.Google Scholar
  17. [17]
    Shang S, Ding R, Yuan B et al. User oriented trajectory search for trip recommendation. In Proc. the 15th EDBT, March 2012, pp.156-167.Google Scholar
  18. [18]
    Shang S, Ding R, Zheng K et al. Personalized trajectory matching in spatial networks. The VLDB Journal, 2014, 23(3): 449–468.CrossRefGoogle Scholar
  19. [19]
    Wang F, Zhu Z. Global path planning of wheeled robots using multi-objective memetic algorithms. In Proc. the 14th IDEAL, Oct. 2013, pp.437-444.Google Scholar
  20. [20]
    Guo X, Zhang D, Wu K et al. MODLoc: Localizing multiple objects in dynamic indoor environment. IEEE Transactions on Parallel and Distribution Systems, 2014, 25(11): 2969–2980.MathSciNetCrossRefGoogle Scholar
  21. [21]
    Shang S, Xie K, Zheng K et al. VID join: Mapping trajectories to points of interest to support location-based services. Journal of Computer Science and Technology, 2015, 30(4): 725–744.CrossRefGoogle Scholar
  22. [22]
    Li B, Tan S,Wang M et al. Investigation on cost assignment in spatial image steganography. IEEE Transactions on Information Forensics and Security, 2014, 9(8): 1264–1277.CrossRefGoogle Scholar
  23. [23]
    Li B, Wang M, Li X et al. A strategy of clustering modification directions in spatial image steganography. IEEETransactions on Information Forensics and Security, 2015, 10(9): 1905–1917CrossRefGoogle Scholar
  24. [24]
    Yang X S, Pei J, Sun W. Elastic image registration using hierarchical spatially based mean shift. Comp. in Bio. and Med., 2013, 43(9): 1086–1097.CrossRefGoogle Scholar
  25. [25]
    Zhou F, Jiao J, Lei B Y. A linear threshold-hurdle model for product adoption prediction incorporating social network effects. Inf. Sci., 2015, 307: 95–109.CrossRefGoogle Scholar
  26. [26]
    Wang J, Huang J Z, Guo J et al. Recommending high-utility search engine queries via a query-recommending model. Neurocomputing, 2015, 167(C): 195–208.CrossRefGoogle Scholar
  27. [27]
    Dai M, Sung C. Achieving high diversity and multiplexing gains in the asynchronous parallel relay network. Trans. Emerging Telecommunications Technologies, 2013, 24(2): 232–243.CrossRefGoogle Scholar
  28. [28]
    Zhang D, Lu K, Mao R. A precise RFID indoor localization system with sensor network assistance. China Communications, 2015, 12(4): 13–22.CrossRefGoogle Scholar
  29. [29]
    Huang X, Cheng H, Li R H et al. Top-k structural diversity search in large networks. VLDB J., 2015, 24(3): 319–343.CrossRefGoogle Scholar
  30. [30]
    Wu R, Li C, Lu D. Power minimization with derivative constraints for high dynamic GPS interference suppression. SCIENCE CHINA Information Sciences, 2012, 55(4): 857–866.MathSciNetCrossRefGoogle Scholar
  31. [31]
    Zhao Q, Liew S, Zhang S, Yu Y. Distance-based location management utilizing initial position for mobile communication networks. IEEE Trans. Mob. Comput., 2016, 15(1): 107–120.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Shuo Shang
    • 1
    • 2
  • Lisi Chen
    • 3
  • Zhe-Wei Wei
    • 4
  • Dan-Huai Guo
    • 5
  • Ji-Rong Wen
    • 4
  1. 1.State Key Laboratory of Software Development EnvironmentBeijingChina
  2. 2.Department of Computer ScienceChina University of PetroleumBeijingChina
  3. 3.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore
  4. 4.Beijing Key Laboratory of Big-Data Management and Analysis MethodsRenmin University of ChinaBeijingChina
  5. 5.Computer Network Information CenterChinese Academy of SciencesBeijingChina

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