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A Probabilistic Model for Road S election in Mobile Maps

A Probabilistic Model for Road S election in Mobile Maps. Thomas C. van Dijk Jan- Henrik Haunert W2GIS, 5 April 2013. Overview: a little bit of …. modelling , interpretation, … algorithms, … evaluation, … outlook, …. Road selection. focus -and- context map route map.

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A Probabilistic Model for Road S election in Mobile Maps

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  1. A Probabilistic Modelfor Road Selectionin Mobile Maps Thomas C. van DijkJan-HenrikHaunert W2GIS, 5 April 2013

  2. Overview: a little bit of … modelling, interpretation, … algorithms, … evaluation, … outlook, …

  3. Road selection focus-and-context maproute map • Depends on user task

  4. Road selection destination map Kopf et al.focus maps Yamamoto et al.

  5. Motivating example • Pedestrian with a smartphone • Current location available • Wayfinding to a destination • Interest in surroundings • Freedom of movement • Contrast: in-car sat nav

  6. From scoring to selection • Maximise score • using given number of road segments • Minimise number of road segments • selecting at least a given amount of score • Additional constraints?

  7. Betweenness centrality Consider all shortest paths between all pairs of nodes; count how often each edge is used Folklore interpretation: • People are likely to travel shortest paths • Heavily traveled edges are important

  8. Betweenness centrality Consider all shortest paths between all pairs of nodes; count how often each edge is used Probabilistic interpretation: • Start at a uniformly random node,uniformly random destination,take a uniformly random shortest path • Probability that you visit a certain edge

  9. PageRank primer • Origin in web page ranking • (Page, Brinn, Motwani, Winograd… Google) • Previous application in predicting human movement (Jiang) • Usually stated as eigenvector calculation • Also: interpretation in terms of random walks

  10. Traveler Random Surfer model on road segment • Hypothetical user is at a web page • Transitions along a uniformly random link • All transitions take equal time • Sometimes jumps to a uniformly random page turn road segment

  11. Non-uniform transition probability • Walking along a road segment… • Coming up to a crossing… • Where to go…? • Shortest path? Straight on? Anywhere? • What’s the network? • Web pages, hyperlinks?

  12. Bidirected line graph

  13. Bidirected line graph

  14. Bidirected line graph

  15. Bidirected line graph

  16. Bidirected line graph

  17. Non-uniform transition probability • At a node of the line graph… • Follow an arc… • Where to go…? • Straight on? • Constant given road network • Shortest path? • Constant given road network and destination

  18. Non-uniform damping • PageRank jumps to uniformly random node • Instead, jump to the current user location! Probabilistic interpretation: • Consider random walk starting at user location • Exponential distribution of walk length • Score of node = probability of going to node

  19. Data set • OpenStreetMap • City of Würzburg and surroundings • Lower Franconia, Germany • Approx 105 nodes • Examples here: crop to the city itself • 3786 nodes, 4987 road segments

  20. Demo

  21. Shortest-path bonus

  22. Non-turning bonus

  23. Runtime of simple implementation • Calculate PageRank • Loop over nodes • Distribute score to neighbours • Repeat • Select nodes with high score • Runtime: ~100 ms

  24. ‘Direct’ selection • Select all nodes with high rank • Select no nodes with tiny rank • O( log n ) time • Correctness holds almost surely • Almost immediately from Borgset al’salgorithm for Significant PageRanks.

  25. Really big maps? • Just calculate, then select • Approximation algorithm: • Runtime O( log(n) log(ε-1) ε-1ρ-2)

  26. Gaps • Selection is not necessarily connected • Simplified example:

  27. Gaps

  28. Strokes

  29. Overview: a little bit of … modelling, interpretation, … algorithms, … evaluation, … outlook, …

  30. Q?

  31. A!

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