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LAMI 1. User Oriented Trajectory Similarity Search 2. Calibration-free Localization using Relative Distance Estimations 3. From GPS Traces to a Routable Road Map. Radu Mariescu-Istodor 10.2.2014. Similarity Search. Naive solution is to compare Every pair of points ( 10 8 )
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LAMI1. User Oriented Trajectory Similarity Search 2. Calibration-free Localization using Relative Distance Estimations 3. From GPS Traces to a Routable Road Map RaduMariescu-Istodor 10.2.2014
Similarity Search Naive solution is to compare Every pair of points (108) From reference route and Every route in DB (105) Average route length ~1000 points. 108 haversines ~ 30 minutes
Limiting the space R-tree indexed points => easy to conduct range checks Naive solution is to compare Every pair of points (108) From reference route and Every route in DB (105) the routes that have at least one point in one of the squares
Reducing range check calculations Grouping several squares into a bounding rectangle
How to group? Dead Space Dead Space Limiting the Dead Space as much as possible
Longest Common Subsequence O(N2) Heaviest Common Subsequence
User Oriented Similarity User defined Regions Heaviest Common Subsequence
Improve speed of HCSS Heaviest Grouped Subsequence HCSS ≤ HGSS 1. HGSS = 492 2. HGSS = 452 3. HGSS = 412 4. HGSS = 301 ………………. HCSS ?
Purpose How to calculate the position of mobile devices that do not posses a GPS sensor
How it works? Cell1=50% Cell2=80% Cell3=60% GPS signature
How it works? Common Cells = 2 Uncommon Cells = 3 Spearman = ? Feature : Cell1=50% Cell2=80% Cell3=60% Cell1=80% Cell3=20% Cell5=30% Cell6=20% (Experimentally deduced)
Regression Formula (Experimentally deduced) Fitted from GPS phones Features ={Common, Uncommon, Spearman}
Merging nearby trajectories Gravity
Merging nearby trajectories Gravity Spring Resultant force applied in small iterations until change insignificant
Traces of opposite directions Method so far Desired output:
Repelling force ≠ ? Sign tells if same direction or not
Banding issue expecting Gaussian Distribution
Matching the Gaussians expecting Gaussian Distribution Finding centroids gives number of bands