Skip to main content
Log in

A Dilution-matching-encoding compaction of trajectories over road networks

  • Published:
GeoInformatica Aims and scope Submit manuscript

Abstract

Many devices nowadays record traveled routes as sequences of GPS locations. With the growing popularity of smartphones, millions of such routes are generated each day, and many routes have to be stored locally on the device or transmitted to a remote database. It is, thus, essential to encode the sequences, in order to decrease the volume of the stored or transmitted data. In this paper we study the problem of encoding routes over a vectorial road network (map), where GPS locations can be associated with vertices or with road segments. We consider a three-step process of dilution, map-matching and coding, which helps reducing the amount of transmitted data between the cellular device and remote servers. We present two methods to code routes. The first method represents the given route as a sequence of greedy paths. We provide two algorithms to generate a greedy-path code for a sequence of n vertices on the map. The first algorithm has O(n) time complexity, and the second one has O(n 2) time complexity, but it is optimal, meaning that it generates the shortest possible greedy-path code. Decoding a greedy-path code can be done in O(n) time. The second method encodes a route as a sequence of shortest paths. We provide algorithms to generate unidirectional and bidirectional optimal shortest-path codes. Encoding and decoding a shortest-path code can be done in O(k n 2 logn) time, where k is the length of the produced code, assuming the graph valency is bounded. Our experimental evaluation shows that shortest-path codes are more compact than greedy-path codes, justifying the larger time complexity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31

Similar content being viewed by others

References

  1. Bellman R (1958) On a routing problem. Q Appl Math 16(1):87–90

    Google Scholar 

  2. Borradaile G, Sankowski P, Wulff-Nilsen C (2010) Min st-cut oracle for planar graphs with near-linear preprocessing time. In: Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, FOCS ’10. IEEE Computer Society, Washington,DC, pp 601–610

  3. Bose P, Morin P (2004) Online routing in triangulations. SIAM J Comput 33:937–951

    Article  Google Scholar 

  4. Cao X, Cong G, Jensen CS (2010) Mining significant semantic locations from GPS data. Proc VLDB Endowment 3(1–2):1009–1020

    Article  Google Scholar 

  5. Chen Y, Jiang K, Zheng Y, Li C, Yu N (2009) Trajectory simplification method for location-based social networking services. In: Proc. of the ACM international workshop on location-based social networks. Seattle, Washington, pp 33–40

  6. Civilis A, Jensen CS, Pakalnis S (2005) Techniques for efficient road-network-based tracking of moving objects. IEEE Trans Knowl Data Eng 17(5):698–712

    Article  Google Scholar 

  7. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1(1):269–271

    Article  Google Scholar 

  8. Douglas DH, Peucker TK (1973) Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: Inter. J Geogr Inf Geovisualization 10 (2):112–122

    Article  Google Scholar 

  9. Doytsher Y, Galon B, Kanza Y (2010) Querying geo-social data by bridging spatial networks and social networks. In: Proc. of the 2nd ACM SIGSPATIAL international workshop on location-based social networks, San Jose, pp 39–46

  10. Doytsher Y, Galon B, Kanza Y (2011) Storing routes in socio-spatial networks and supporting social-based route recommendation. In: Proc. of the 3rd ACM SIGSPATIAL international workshop on location-based social networks, pp 49–56

  11. Feldman D, Sugaya A, Rus D (2012) An effective coreset compression algorithm for large scale sensor networks. In: Proceedings of the 11th international conference on information processing in sensor networks. ACM, Beijing, pp 257–268

    Google Scholar 

  12. Fredman ML, Tarjan RE (1984) Fibonacci heaps and their uses in improved network optimization algorithms. In: Proceedings of the 25th annual symposium on foundations of computer science. IEEE, pp 338–346

  13. Giannotti F, Nanni M, Pedreschi D, Pinelli F, Renso C, Rinzivillo S, Trasarti R (2011) Unveiling the complexity of human mobility by querying and mining massive trajectory data. VLDB J 20(5):695–719

    Article  Google Scholar 

  14. Gotsman R, Kanza Y (2013) Compact representation of GPS trajectories over vectorial road networks. In: Proc. of the 13th international conference on advances in spatial and temporal databases, SSTD’13. Springer-Verlag, Munich, pp 241–258

    Google Scholar 

  15. Greenfeld JS (2002) Matching GPS observations to locations on a digital map. In: Proceedings of the 81st annual meeting of the transportation research board

  16. Hartvigsen D, Mardon R (1994) The all-pairs min cut problem and the minimum cycle basis problem on planar graphs. SIAM J Discret Math 7(3):403–418

    Article  Google Scholar 

  17. Hershberger J, Snoeyink J (1994) An o(n logn) implementation of the Douglas-Peucker algorithm for line simplification. In: Proceedings of the tenth annual symposium on computational geometry. ACM, Stony Brook, New York, pp 383–384

  18. Hummel B (2006) Map matching for vehicle guidance. In: Dynamic and mobile GIS: Investigating space and time. CRC Press, pp 437–438

  19. Imai H, Iri M (1986) Computational-geometric methods for polygonal approximations of a curve. Comp Vis, Graph, Image Proc 36(1):31–41

    Article  Google Scholar 

  20. Levin R, Kravi E, Kanza Y (2012) Concurrent and robust topological map matching. In: Proceedings of the 20th international conference on advances in geographic information systems, SIGSPATIAL ’12. ACM, Redondo Beach, pp 617–620

  21. Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma WY (2008) Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS ’08. ACM, Irvine, pp 34:1–34:10

  22. Meratnia N, de By RA (2004) Spatiotemportal compression techniques for moving point objects. In: Proceedings of the 9th international conference on extending database technology (EDBT). Heraklion Crete, Greece, pp 765–782

  23. Muckell J, Hwang JH, Lawson CT, Ravi SS (2010) Algorithms for compressing GPS trajectory data: An empirical evaluation. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, GIS ’10. ACM, San Jose, pp 402–405

  24. Muckell J, Hwang JH, Patil V, Lawson CT, Ping F, Ravi SS (2011) SQUISH: An online approach for GPS trajectory compression. In: Proceedings of the 2nd international conference on computing for geospatial research & applications, COM.Geo ’11. ACM, Washington, DC, pp 13:1– 13:8

  25. Muckell J Jr, PWO, Hwang JH, Lawson CT, Ravi SS (2013) Compression of trajectory data: a comprehensive evaluation and new approach. Geoinformatica

  26. Mulmuley K, Vazirani UV, Vazirani VV (1987) Matching is as easy as matrix inversion. Combinatorica 7(1):105–113

    Article  Google Scholar 

  27. Newson P, Krumm J (2009) Hidden Markov map matching through noise and sparseness. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pp 336–343

  28. Ore O (1962) Theory of Graphs. AMS Colloquium Publications 38.American Mathematical Soc

  29. Potamias M, Patroumpas K, Sellis T (2006) Amnesic online synopses for moving objects. In: Proceedings of the 15th ACM international conference on information and knowledge management, CIKM ’06. ACM, Arlington, pp 784–785

    Google Scholar 

  30. Potamias M, Patroumpas K, Sellis T (2006) Sampling trajectory streams with spatiotemporal criteria. In: Proceedings of the 18th international conference on scientific and statistical database management, SSDBM ’06. IEEE Computer Society, Washington, DC, pp 275–284

  31. Potamias M, Patroumpas K, Sellis T (2007) Online amnesic summarization of streaming locations. In: Proc. of the 10th international conference on advances in spatial and temporal databases, SSTD’07. Springer-Verlag, Boston, pp 148–166

  32. Quddus MA, Ochieng W, Zhao L, Noland RB (2003) A general map matching algorithm for transport telematics applications. GPS Resolut 7(3)

  33. Quddus MA, Ochieng WY, Noland RB (2007) Current map-matching algorithms for transport applications: State-of-the art and future research directions. In: Transportation research part c: Emerging technologies

  34. Trajcevski G, Cao H, Scheuermanny P, Wolfsonz O, Vaccaro D (2006) On-line data reduction and the quality of history in moving objects databases. In: Proceedings of the 5th ACM international workshop on data engineering for wireless and mobile access, MobiDE ’06. ACM, Chicago, pp 19–26

  35. Viterbi AJ (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. Trans Inf Theory 13 (2):260–269

    Article  Google Scholar 

  36. White CE, Bernstein D, Kornhauser AL (2000) Some map matching algorithms for personal navigation assistants. In: Transportation research part c: emerging technologies, vol 8, pp 91–108

  37. Xu Z, Zhang R, Kotagiri R, Parampalli U (2012) An adaptive algorithm for online time series segmentation with error bound guarantee. In: Proceedings of the 15th international conference on extending database technology, EDBT ’12. ACM, Berlin, pp 192–203

    Google Scholar 

  38. Xue AY, Zhang R, Zheng Y, Xie X, Huang J, Xu Z (2013) Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In: Proc. of the 2013 IEEE international conference on data engineering (ICDE 2013). IEEE Computer Society, Washington, DC, pp 254–265

    Google Scholar 

  39. Yin H, Wolfson O (2004) A weight-based map matching method in moving objects databases. In: Proceedings of the 16th international conference on scientific and statistical database management, SSDBM ’04. IEEE Computer Society, Washington, DC, pp 437–438

  40. Zheng K, Trajcevski G, Zhou X, Scheuermann P (2011) Probabilistic range queries for uncertain trajectories on road networks. In: Proceedings of the 14th international conference on extending database technology, EDBT/ICDT ’11. ACM, Uppsala, Sweden, pp 283–294

  41. Zheng K, Zheng Y, Xie X, Zhou X (2012) Reducing uncertainty of low-sampling-rate trajectories. In: Proceedings of the 2012 IEEE 28th international conference on data engineering. IEEE Computer Society, Washington, DC, pp 1144–1155

  42. Zheng Y, Li Q, Chen Y, Xie X, Ma WY (2008) Understanding mobility based on GPS data. In: Proceedings of the 10th international conference on ubiquitous computing, UbiComp ’08. ACM, Seoul, pp 312–321

  43. Zheng Y, Zhang L, Ma Z, Xie X, Ma WY (2011) Recommending friends and locations based on individual location history. ACM Trans Web 5(1):1–5. 44

    Article  Google Scholar 

  44. Zheng Y, Zhang L, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international conference on world wide web, WWW ’09. ACM, Madrid, pp 791–800

  45. Zheng Y, Zhou X (eds.) (2011) Computing with Spatial Trajectories, Springer

Download references

Acknowledgements

This research was supported in part by the Israel Science Foundation (Grant 1467/13) and by the Isreali Ministry of Science and Technology (Grant 3-9617).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yaron Kanza.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gotsman, R., Kanza, Y. A Dilution-matching-encoding compaction of trajectories over road networks. Geoinformatica 19, 331–364 (2015). https://doi.org/10.1007/s10707-014-0216-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10707-014-0216-4

Keywords

Navigation