, Volume 19, Issue 4, pp 723–746 | Cite as

Planning unobstructed paths in traffic-aware spatial networks

  • Shuo Shang
  • Jiajun Liu
  • Kai Zheng
  • Hua Lu
  • Torben Bach Pedersen
  • Ji-Rong Wen


Route planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of planning unobstructed paths in traffic-aware spatial networks (TAUP queries) to avoid potential traffic congestions. We propose two probabilistic TAUP queries: (1) a time-threshold query like “what is the path from the check-in desk to the flight SK 1217 with the minimum congestion probability to take at most 45 minutes?”, and (2) a probability-threshold query like “what is the fastest path from the check-in desk to the flight SK 1217 whose congestion probability is less than 20 %?”. These queries are mainly motivated by indoor space applications, but are also applicable in outdoor spaces. We believe that these queries are useful in some popular applications, such as planning unobstructed paths for VIP bags in airports and planning convenient routes for travelers. The TAUP queries are challenged by two difficulties: (1) how to model the traffic awareness in spatial networks practically, and (2) how to compute the TAUP queries efficiently under different query settings. To overcome these challenges, we construct a traffic-aware spatial network Gta(V, E) by analyzing uncertain trajectories of moving objects. Based on Gta(V, E), two efficient algorithms are developed to compute the TAUP queries. The performances of TAUP queries are verified by extensive experiments on real and synthetic spatial data.


Traffic-aware spatial networks Probabilistic path planning Efficiency Spatio-temporal databases 


  1. 1.
    Alt H, Efrat A, Rote G, Wenk C (2003) Matching planar maps. In: SODA, pp 589–598Google Scholar
  2. 2.
    Brakatsoulas S, Pfoser D, Salas R, Wenk C (2005) On map-matching vehicle tracking data. In: VLDB, pp 853–864Google Scholar
  3. 3.
    Cheng R, Kalashnikov DV, Prabhakar S (2004) Querying imprecise data in moving object environments. IEEE Trans Knowl Data Eng 16(9):1112–1127CrossRefGoogle Scholar
  4. 4.
    Dijkstra EW (1959) A note on two problems in connection with graphs. Numer Math 1:269–271CrossRefGoogle Scholar
  5. 5.
    Ding B, Yu JX, Qin L (2008) Finding time-dependent shortest paths over large graphs. In: EDBT, pp 205–216Google Scholar
  6. 6.
    Greenfeld J (2002) Matching gps observations to locations on a digital map. In: 81th annual meeting of the transportation research boardGoogle Scholar
  7. 7.
    Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Cybern 4(2):100–107CrossRefGoogle Scholar
  8. 8.
    Hua M, Pei J (2010) Probabilistic path queries in road networks: traffic uncertainty aware path selection. In: EDBT, pp 347–358Google Scholar
  9. 9.
    Jensen CS, Lu H, Yang B (2009) Graph model based indoor tracking. In: Mobile data management, pp 122–131Google Scholar
  10. 10.
    Jensen CS, Lu H, Yang B (2009) Indexing the trajectories of moving objects in symbolic indoor space. In: SSTD, pp 208–227Google Scholar
  11. 11.
    Liu K, Deng K, Ding Z, Li M, Zhou X (2009) Moir/mt: monitoring large-scale road network traffic in real-time. In: VLDB, pp 1538–1541Google Scholar
  12. 12.
    Muckell J, Hwang J-H, Lawson C, Ravi S (2010) Algorithms for compressing gps trajectory data: an empirical evaluation. In: ACM GISGoogle Scholar
  13. 13.
    Pfoser D, Jensen CS (1999) Capturing the uncertainty of moving-object representations. In: SSD, pp 111–132Google Scholar
  14. 14.
    Shang S, Ding R, Zheng K, Jensen CS, Kalnis P, Zhou X (2014) Personalized trajectory matching in spatial networks. VLDB J 23(3):449–468CrossRefGoogle Scholar
  15. 15.
    Shang S, Lu H, Pedersen TB, Xie X (2013) Finding traffic-aware fastest paths in spatial networks. In: SSTD, pp 128–145Google Scholar
  16. 16.
    Shang S, Lu H, Pedersen TB, Xie X (2013) Modeling of traffic-aware travel time in spatial networks. In: MDM, p 4Google Scholar
  17. 17.
    Shang S, Yuan B, Deng K, Xie K, Zheng K, Zhou X (2012) Pnn query processing on compressed trajectories. GeoInformatica 16(3):467–496CrossRefGoogle Scholar
  18. 18.
    Trajcevski G, Tamassia R, Ding H, Scheuermann P, Cruz IF (2009) Continuous probabilistic nearest-neighbor queries for uncertain trajectories. In: EDBT, pp 874–885Google Scholar
  19. 19.
    Trajcevski G, Wolfson O, Hinrichs K, Chamberlain S (2004) Managing uncertainty in moving objects databases. ACM Trans Database Syst 29(3):463–507CrossRefGoogle Scholar
  20. 20.
    Wenk C, Salas R, Pfoser D (2006) Addressing the need for map-matching speed: localizing globalb curve-matching algorithms. In: SSDBMGoogle Scholar
  21. 21.
    Wolfson O, Chamberlain S, Dao S, Jiang L, Mendez G (1998) Cost and imprecision in modeling the position of moving objects. In: ICDE, pp 588–596Google Scholar
  22. 22.
    Wolfson O, Sistla AP, Chamberlain S, Yesha Y (1999) Updating and querying databases that track mobile units. Distributed and Parallel Databases 7(3):257–387CrossRefGoogle Scholar
  23. 23.
    Yuan J, Zheng Y, Xie X, Sun G (2011) Driving with knowledge from the physical world. In: KDD, pp 316–324Google Scholar
  24. 24.
    Yuan J, Zheng Y, Xie X, Sun G (2013) T-drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans Knowl Data Eng 25(1):220–232CrossRefGoogle Scholar
  25. 25.
    Yuan J, Zheng Y, Zhang C, Xie W, Xie X, Sun G, Huang Y (2010) T-drive: driving directions based on taxi trajectories. In: GIS, pp 99–108Google Scholar
  26. 26.
    Zarchan P (1996) Global positioning system theory and applications. In: American institute of aeronautics and astronautics, p 1Google Scholar
  27. 27.
    Zhang M, Chen S, Jensen CS, Ooi BC, Zhang Z (2009) Effectively indexing uncertain moving objects for predictive queries. PVLDB 2(1):1198–1209Google Scholar
  28. 28.
    Zheng K, Trajcevski G, Zhou X, Scheuermann P (2011) Probabilistic range queries for uncertain trajectories on road networks. In: EDBT, pp 283–294Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Shuo Shang
    • 1
    • 2
  • Jiajun Liu
    • 3
  • Kai Zheng
    • 4
  • Hua Lu
    • 5
  • Torben Bach Pedersen
    • 5
  • Ji-Rong Wen
    • 6
  1. 1.State Key Laboratory of Petroleum Resources and ProspectingChina University of PetroleumBeijingChina
  2. 2.Department of Computer ScienceChina University of PetroleumBeijingChina
  3. 3.CSIROCanberraAustralia
  4. 4.School of ITEEThe University of QueenslandBrisbaneAustralia
  5. 5.Department of Computer ScienceAalborg UniversityAalborgDenmark
  6. 6.MOE Key Laboratory of Data Engineering and Knowledge EngineeringRenmin UniversityBeijingChina

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