Calculating Route Probability from Uncertain Origins to a Destination

  • Carolin von Groote-BidlingmaierEmail author
  • David Jonietz
  • Sabine Timpf
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Uncertainty in location information can affect the results of network-based route calculations to a high degree. In this study, a routing scenario is analysed where the destination is known, but the location of the point of origin can only approximately be described as “somewhere inside a polygon”. Using the concrete example of car-driving football fans arriving at a game, an approach is proposed to compute and describe the probability of them taking specific routes from their home county to the stadium. A set of candidate points of origin is created and shortest paths to the destination calculated. The observed frequency of an edge being included in a route allows inferring a routing probability for each edge. Several methods to derive a set of candidate points of origin are presented and discussed, ranging from purely geometrical to geographically weighted approaches. Our results show that the differences between the methods in determining the points of origin produce only slightly different probabilities, i.e., neither advantages nor drawbacks are to be expected from using a purely geometrical approach.


Routing Probability Network analysis 



We gratefully acknowledge support of Carolin von Groote-Bidlingmaier through the program “Chancengleichheit von Frauen in Forschung und Lehre” of the University of Augsburg.


  1. Basiri A, Winstanley A, Sester M, Amirian P, Kuntzsch C (2012) Uncertainty handling in navigation services using rough and fuzzy set theory. In: Kroeger P, Renz M (eds) QUeST ’12 Proceedings of the Third ACM SIGSPATIAL international workshop on querying and mining uncertain spatio-temporal data. Redondo Beach, CA, USA, 07 Nov 2012Google Scholar
  2. Chang KT, Khatib Z, Ou y (2002) Effects of zoning structure and network detail on traffic demand modelling. Environ Plan 29:37–52CrossRefGoogle Scholar
  3. Gonzales JP, Stentz A (2007) Planning with uncertainty in position using high-resolution maps. In: Proceedings IEEE international conference on robotics and automation, Rome, Italy, 2007Google Scholar
  4. Goodchild MF (2009) Methods: uncertainty. In: Kitchin R, Thrift M (eds) International encyclopedia of human geography. Springer, New YorkGoogle Scholar
  5. Hait A, Simeon T, Taix M (1999) Robust motion planning for rough terrain navigation. In: Proceedings. IEEE/RSJ International. Conference. Robotics and Systems, Kyongu, KoreaGoogle Scholar
  6. Hajek A (2012) Interpretations of Probability. In: Zalta EN (ed) The stanford encyclopedia of philosophy. Accessed 30 May 2014
  7. Hazewinkel M (2001) Law of large numbers. In: Hazewinkel M (ed) Encyclopaedia of mathematics. Springer, BerlinGoogle Scholar
  8. Jaynes ET (2003) Probability theory—the logic of science. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  9. Liao F, Rasouli S, Timmermans H (2014) Incorporating activity-travel time uncertainty and stochastic space-time prisms in multistate supernetworks for activity-travel scheduling. Int J Geogr Inf Sci 28(5):928–945CrossRefGoogle Scholar
  10. MacNally MG (2007) The four step model. In: Hensher DA, Button KJ (eds) Handbook of transport modeling. Elsevier, OxfordGoogle Scholar
  11. Miller HJ, Shaw S-L (2001) Geographic information systems for transportation: principles and applications. Oxford University Press, OxfordGoogle Scholar
  12. Prato CG (2009) Route choice modelling: past, present and future research directions. J Choice Modeling 2(1):65–100CrossRefGoogle Scholar
  13. Qian ZS, Zhang HM (2012) On centroid connectors in static traffic assignment: their effects on flow patterns and how to optimize their selections. Transp Res Part B 46:1489–1503CrossRefGoogle Scholar
  14. Qiu D, Papotti P, Blanco L (2013) Future locations prediction with uncertain data. In: Blockeel H, Kersting K, Nijssen S, Zelezny F (eds) Machine learning and knowledge discovery in databases, LNCS 8188. Springer, Berlin, pp 417–432CrossRefGoogle Scholar
  15. Wang S, Wenzhong S, Yuan H, Chen G (2005) Attribute uncertainty in GIS data. Fuzzy Syst Knowl Discov, LNCS 3614:614–623CrossRefGoogle Scholar
  16. Worboys M (1998) Imprecision in finite resolution spatial data. Geoinformatica 2(3):257–279CrossRefGoogle Scholar
  17. Zhang J, Goodchild MF (2002) Uncertainty in geographical information. Taylor and Francis, New YorkCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Carolin von Groote-Bidlingmaier
    • 1
    Email author
  • David Jonietz
    • 1
  • Sabine Timpf
    • 1
  1. 1.Geoinformatics Group, Department of GeographyUniversity of AugsburgAugsburgGermany

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