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Towards fusing uncertain location data from heterogeneous sources

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Abstract

Properly incorporating location-uncertainties – which is, fully considering their impact when processing queries of interest – is a paramount in any application dealing with spatio-temporal data. Typically, the location-uncertainty is a consequence of the fact that objects cannot be tracked continuously and the inherent imprecision of localization devices. Although there is a large body of works tackling various aspects of efficient management of uncertainty in spatio-temporal data – the settings consider homogeneous localization devices, e.g., either a Global Positioning System (GPS), or different sensors (roadside, indoor, etc.).In this work, we take a first step towards combining the uncertain location data – i.e., fusing the uncertainty of moving objects location – obtained from both GPS devices and roadside sensors. We develop a formal model for capturing the whereabouts in time in this setting and propose the Fused Bead (FB) model, extending the bead model based solely on GPS locations. We also present algorithms for answering traditional spatio-temporal range queries, as well as a special variant pertaining to objects locations with respect to lanes on road segments – augmenting the conventional graph based road network with the width attribute. In addition, pruning techniques are proposed in order to expedite the query processing. We evaluated the benefits of the proposed approach on both real (Beijing taxi) and synthetic (generated from a customized trajectory generator) data. Our experiments demonstrate that the proposed method of fusing the uncertainties may eliminate up to 26 % of the false positives in the Beijing taxi data, and up to 40 % of the false positives in the larger synthetic dataset, when compared to using the traditional bead uncertainty models.

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Notes

  1. The original notation in [27] was B(t i , x i , y i , t i+1, x i+1, y i+1, v m a x ) and we slightly modified it for consistency with the rest of the notation in this article.

  2. For clarity, we present the details of calculating \(t_{i}^{l1}\) and other significant times in the Appendix.

  3. Throughout this work, we assume independence between location-values in successive location samples (cf. [4, 11]).

References

  1. Brugere I, Gunturi VMV, Shekhar S (2014) Modeling and analysis of spatiotemporal social networks. In: Encyclopedia of Social Network Analysis and Mining, pp 950–960

  2. Cao Q, Yan T, Stankovic J, Abdelzaher T (2005) Analysis of target detection performance for wireless sensor networks. In: DCOSS, pp 276–292

  3. Chen W, Hou J, Sha L (2003) Dynamic clustering for acoustic target tracking in wireless sensor networks. In: IEEE International Conference on Network Protocols (ICNP’03)

  4. Cheng R, Emrich T, Kriegel HP, Mamoulis N, Renz M, Trajcevski G, Züfle A (2014) Managing uncertainty in spatial and spatio-temporal data. In: ICDE, pp 1302–1305

  5. Dao TS, Leung KY, Clark CM, Huissoon JP (2007) Markov-based lane positioning using intervehicle communication. Trans. Intell. Transport. Sys. 8 (4):641–650. doi:10.1109/TITS.2007.908574

    Article  Google Scholar 

  6. Department of Transportation (2014) U.S.: Travel monitoring and traffic volume. http://www.fhwa.dot.gov/policyinformation/travelmonitoring.cfm. Office of Highway Policy Information

  7. Ding Z, Güting RH (2004) Managing moving objects on dynamic transportation networks. In: SSDBM

  8. Ding Z, Güting RH (2004) Uncertainty management for network constrained moving objects. In: DEXA, 411–421

  9. Du J, Barth M (2008) Next-generation automated vehicle location systems: Positioning at the lane level. IEEE Trans. Intell. Transp. Syst. 9(1):48–57. doi:10.1109/TITS.2007.908141

    Article  Google Scholar 

  10. EasySen LLC (2008) Wieye - sensor board for wireless surveillance applications. 401 North Coquillard Dr., South Bend, IN 46617

  11. Emrich T, Kriegel HP, Mamoulis N, Renz M, Züfle A (2012) Querying uncertain spatio-temporal data. In: ICDE, 354–365

  12. George B, Shekhar S (2008) SP-TAG: a routing algorithm in non-stationary transportation networks. In: 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, MobiQuitous 2008, Dublin, Ireland

  13. Gilat A (2010) Numerical methods for engineers and scientists. Wiley

  14. Gowrisankar N, Nittel S (2002) Reducing uncertainty in location prediction of moving objects in road networks. In: GIScience

  15. Güting RH, Schneider M (2005) Moving objects databases. Morgan Kaufmann

  16. Hägerstrand T (1970) What about people in regional science Papers of the Regional Science Association 24:7–21

    Article  Google Scholar 

  17. Hellebrandt M, Mathar R (1998). Location tracking of mobiles in cellular radio networks 45(5):1558–1562

    Google Scholar 

  18. Honeywell International Inc (2005) Vehicle detection using amr sensors. Tech. rep., Defense and Space Electronics Systems, 12001 Highway 55, Plymouth, MN 55441

  19. Hornsby K, Egenhofer MJ (2002) Modeling moving objects over multiple granularities. Ann Math Artif Intell 36(1–2):177–194

    Article  Google Scholar 

  20. Jeong J, Hwang T, He T, Du DHC (2007) Mcta: Target tracking algorithm based on minimal contour in wireless sensor networks. In: INFOCOM

  21. Kelly R (2007) M42 active traffic management scheme. Road Traffic Technology. S.M. Limited Editor. Birmingham, United Kingdom

  22. Khaleghi B, Khamis A M, Karray F, Razavi S N (2013) Multisensor data fusion: A review of the state-of-the-art. Information Fusion 14(1):28–44. doi:10.1016/j.inffus.2011.08.001

    Article  Google Scholar 

  23. Kim S, Shekhar S (2005) Contraflow network reconfiguration for evacuation planning: a summary of results. In: 13th ACM International Workshop on Geographic Information Systems, ACM-GIS 2005, November 4–5, 2005, Bremen, Germany, Proceedings, pp 250–259

  24. Kuijpers B, Miller HJ, Neutens T, Othman W (2010) Anchor uncertainty and space-time prisms on road networks. Int J Geogr Inf Sci 24(8):1223–1248

    Article  Google Scholar 

  25. Kuijpers B, Miller HJ, Othman W (2011) Kinetic space-time prisms. GIS ‘11, pp 162–170. ACM, Chicago, IL, USA. doi:10.1145/2093973.2093996

  26. Kuijpers B, Othman W (2009) Modelling uncertainty on road networks via space-time prisms. Int’l Journal on GIS 23(9)

  27. Kuijpers B, Othman W (2009) Trajectory databases: data models, uncertainty and complete query languages. Comput Syst Sci. doi:10.1016/j.jcss.2009.10.002

  28. Laser Technology Inc (2013) Trusense t-series. Online; accessed May 22, 2014

  29. Leonard T (2012) Delivering deeper insights with big data and real-time analytics

  30. Li G, Li Y, Shu L, Fan P (2011) Cknn query processing over moving objects with uncertain speeds in road networks. In: APWeb, pp 65–76

  31. Liu W, Zheng Y, Chawla S, Yuan J, Xing X (2011) Discovering spatio-temporal causal interactions in traffic data streams. In: KDD, pp 1010–1018

  32. Mckinsey Global Institute (2011) Big data: The next frontier for innovation, competition, and productivity

  33. Parent C, Spaccapietra S, Renso C, Andrienko G, Andrienko N, Bogorny V, Damiani ML, Gkoulalas-Divanis A, Macedo J, Pelekis N, Theodoridis Y, Yan Z (2013) Semantic trajectories modeling and analysis. ACM Comput Surv 45(4):42:1–42:32

    Article  Google Scholar 

  34. Pattem S, Poduri S, Krishnamachari B (2003) Energy-quality tradeoffs for target tracking in wireless sensor networks. In: IPSN

  35. Pfoser D, Jensen CS (1999) Capturing the uncertainty of moving objects representation. In: SSD

  36. Pfoser D, Tryfona N (2001) Capturing fuzziness and uncertainty of spatiotemporal objects. In: ADBIS, pp 112–126

  37. Pfoser D, Tryfona N, Jensen CS (2005) Indeterminacy and spatiotemporal data: Basic definitions and case study. GeoInformatica 9:3

    Google Scholar 

  38. Quddus MA, Ochieng WY, Noland RB (2007) Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part C: Emerging Technologies 15(5):312–328. doi:10.1016/j.trc.2007.05.002. http://www.sciencedirect.com/science/article/pii/S0968090X07000265

    Article  Google Scholar 

  39. Schiller J (ed) (2004) A.V.: Location-Based Services. Morgan Kaufmann

  40. Schramm AJ, Rakotonirainy A (2009) The effect of road lane width on cyclist safety in urban areas. In: Proceedings of the 2009 Australasian Road Safety Research, Policing and Education Conference: Smarter, Safer Directions. Roads and Traffic Authority of New South Wales, Australia

  41. Sekimoto Y, Matsubayashi Y, Yamada H, Imai R, Usui T, Kanasugi H (2012) Lightweight lane positioning of vehicles using a smartphone gps by monitoring the distance from the center line. In: ITSC 2012, pp 1561–1565. doi:10.1109/ITSC.2012.6338737

  42. Shekhar S, Chawla S (2003) Spatial Databases: A Tour. Prentice Hall

  43. Sistla A, Wolfson P, Chamberlain S, Dao S (1999) Querying the uncertain positions of moving objects. In: Etzion O, Jajodia S, Sripada S (eds) Temporal Databases, Research and Practice

  44. Southwest Research Institute Advanced traffic management systems. http://www.swri.org/4ORG/d10/its/atms/default.htm

  45. Tao Y, Xiao X, Cheng R (2007) Range search on multidimensional uncertain data. ACM Trans Database Syst 32(3):15

    Article  Google Scholar 

  46. Toledo-Moreo R, Betaille D, Peyret F (2010) Lane-level integrity provision for navigation and map matching with gnss, dead reckoning, and enhanced maps. IEEE Trans Intell Transp Syst 11(1):100–112. doi:10.1109/TITS.2009.2031625

    Article  Google Scholar 

  47. Trajcevski G, Choudhary A, Wolfson O, Li Y, Li G (2010) Uncertain range queries for necklaces. In: MDM

  48. Trajcevski G, Tamassia R, Cruz I, Scheuermann P, Hartglass D, Zamierowski C (2011) Ranking continuous nearest neighbors for uncertain trajectories. VLDB J 20(5):767–791

    Article  Google Scholar 

  49. Trajcevski G, Wolfson O, Hinrichs K, Chamberlain S (2004) Managing uncertainty in moving objects databases. ACM Trans Database Syst 29(3)

  50. Turner-Fairbank Highway Research Center Traffic Detector Handbook. U.S. Department of transportation

  51. United States Department of Defense (2008) Navstar gps: Global positioning system standard

  52. Wang H, Yao K, Estrin D (2005) Information-theoretic approaches for sensor selection and placement for target localization and tracking in sensor networks. JCN 7 (4):438–449

    Google Scholar 

  53. Winter S, Yin ZC (2010) Directed movements in probabilistic time geography. Int J Geogr Inf Sci 24(9)

  54. Yuan J, Zheng Y, Xie X, Sun G (2011) Driving with knowledge from the physical world. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’11, pp 316–324. ACM, New York, NY, USA. doi:10.1145/2020408.2020462

  55. 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):220–232. doi:10.1109/TKDE.2011.200

    Article  Google Scholar 

  56. Zhang B, Trajcevski G (2014) The tale of (fusing) two uncertainties. In: Proc. International Conference ACM SIGSPATIAL GIS

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Correspondence to Bing Zhang.

Additional information

Research Supported by the NSF grant III 1213038.

Research Supported by the NSF grants CNS 0910952 and III 1213038, and ONR grant N00014-14-10215.

Appendices

Appendix A: Significant times in instantaneous possible location query

In Section 4 we analyze the boundary of the possible locations at a given time instant under the FB model. The detailed significant times calculation will be presented here. Let d m i n and d m a x denote the shortest and longest distance from L 1 to any point \(P(t_{s1},\varepsilon ) \in \overline {P_{1}P_{2}}\).

$$\begin{array}{@{}rcl@{}} t_{i}^{l1}&=& \frac{t_{1} + t_{s}}{2} - \frac{d_{max}}{2v_{max}}\\ t_{i}^{lA}&=& \frac{t_{1} + t_{s}}{2} - \frac{d_{min}}{2v_{max}}\\ t_{i}^{d1}&=& \frac{t_{1} + t_{s}}{2} + \frac{d_{min}}{2v_{max}}\\ t_{i}^{dA}&=& \frac{t_{1} + t_{s}}{2} + \frac{d_{max}}{2v_{max}} \end{array} $$

Appendix B: Enter/exit time calculation for range query

The general case for time t∈[t i , t i+1] being a critical point occurs when the intersection of the uncertain region at t with a query rectangle is a single point. In the time interval [t i , t s ], the single-point-intersection between disk centered at the first GPS point and query region stands for the entering moment. Similarly, in the time interval [t s , t i+1], the single-point-intersection represents exiting moment. Since the query region is represented as polygon in the (X, Y) plane, each edge of the polygon is defined as a segment of 2D line y = a x+b.

The entry boundary of FB is:

$$(x - x_{i})^{2} + (y - y_{i})^{2} = (t - t_{i})^{2}v^{2}_{max}$$

Substituting for y for the equation of the line, we have:

$$(x - x_{i})^{2} + (ax+b - y_{i})^{2} = (t - t_{i})^{2}v^{2}_{max}$$

This yields an equation in x and t:

$$A*x^{2}+x*(B+C*t)+D*t^{2}+E=0$$

Where A, B, C, D, E are constant. Solving for x, as a function of t, we have:

$$x_{1,2}=\frac{-(B+C*t) \pm \sqrt{(B+C*t)^{2}-4*A*(D*t^{2}+E)}}{2*A}$$

To be noted that, we need to check the solution for x against the boundaries of the respect edge of the query region. To find the time for critical point, we set the discriminant to be zero:

$$\sqrt{(B+C*t)^{2}-4*A*(D*t^{2}+E)}=0$$

The real root t i n is the time instant when the uncertain trajectory start to enter the query prism.

In the time interval [t s , t i+1], we can use the similar method to find the exiting time t o u t .

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Zhang, B., Trajcevski, G. & Liu, L. Towards fusing uncertain location data from heterogeneous sources. Geoinformatica 20, 179–212 (2016). https://doi.org/10.1007/s10707-015-0238-6

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