LAMI 1. User Oriented Trajectory Similarity Search 2. Calibration-free Localization using Relative Distance Estimations 3. From GPS Traces to a Routable.

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-Istodor10.2.2014

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

How to limit the Dead Space?

Longest Common Subsequence

Heaviest Common Subsequence

O(N 2)

User Oriented Similarity

Heaviest Common Subsequence

User defined Regions

Improve speed of HCSSHeaviest Grouped Subsequence

HCSS ≤ HGSS

1. HGSS = 4922. HGSS = 4523. HGSS = 4124. HGSS = 301……………….

HCSS ?

Purpose

How to calculate the position of mobile devices that do not posses a GPS sensor

Centroid and Fingerprinting

How it works?

Cell1=50%Cell2=80%Cell3=60% GPS signature

How it works?

Cell1=50%Cell2=80%Cell3=60%

Cell1=80%Cell3=20%Cell5=30%Cell6=20%

Common Cells = 2Uncommon Cells = 3Spearman = ?

Feature :

(Experimentally deduced)

Regression Formula(Experimentally deduced)

Fitted from GPS phones

Features ={Common, Uncommon, Spearman}

Estimating locations

Objective: Road Network from GPS Tracks

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

≠

?

Solution

Banding issue

expectingGaussian

Distribution

Matching the Gaussians

expectingGaussian

Distribution

Finding centroids gives

number of bands